Search results for: chest radiography

Authors: Andrew White, Abigail Lowe, Kory Watkins, Hamed Akhlaghi, Nicole Winter

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The timing and appropriateness of diagnostic imaging are critical to the evaluation and management of traumatic injuries. Within the subclass of trauma patients, the prevalence of c-spine injury is less than 4%. However, the incidence of delayed diagnosis within this cohort has been documented as up to 20%, with inadequate radiological examination most cited issue. In order to assess those in which c-spine injury cannot be fully excluded based on clinical examination alone and, therefore, should undergo diagnostic imaging, a set of criteria is used to provide clinical guidance. The NEXUS (National Emergency X-Radiography Utilisation Study) criteria is a validated clinical decision-making tool used to facilitate selective c-spine radiography. The criteria allow clinicians to determine whether cervical spine imaging can be safely avoided in appropriate patients. The NEXUS criteria are widely used within the Emergency Department setting given their ease of use and relatively straightforward application and are used in the Victorian State Trauma System’s guidelines. This audit utilized retrospective data collection to examine the concordance of c-spine imaging in trauma patients to that of the NEXUS criteria and assess compliance with state guidance on diagnostic imaging in trauma. Of the 183 patients that presented with trauma to the head, neck, or face (244 excluded due to incorrect triage), 98 did not undergo imaging of the c-spine. Out of those 98, 44% fulfilled at least one of the NEXUS criteria, meaning the c-spine could not be clinically cleared as per the current guidelines. The criterion most met was intoxication, comprising 42% (18 of 43), with midline spinal tenderness (or absence of documentation of this) the second most common with 23% (10 of 43). Intoxication being the most met criteria is significant but not unexpected given the cohort of patients seen at St Vincent’s and within many emergency departments in general. Given these patients will always meet NEXUS criteria, an element of clinical judgment is likely needed, or concurrent use of the Canadian C-Spine Rules to exclude the need for imaging. Midline tenderness as a met criterion was often in the context of poor or absent documentation relating to this, emphasizing the importance of clear and accurate assessments. The distracting injury was identified in 7 out of the 43 patients; however, only one of these patients exhibited a thoracic injury (T11 compression fracture), with the remainder comprising injuries to the extremities – some studies suggest that C-spine imaging may not be required in the evaluable blunt trauma patient despite distracting injuries in any body regions that do not involve the upper chest. This emphasises the need for standardised definitions for distracting injury, at least at a departmental/regional level. The data highlights the currently poor application of the NEXUS guidelines, with likely common themes throughout emergency departments, highlighting the need for further education regarding implementation and potential refinement/clarification of criteria. Of note, there appeared to be no significant differences between levels of experience with respect to inappropriately clearing the c-spine clinically with respect to the guidelines.

Keywords: imaging, guidelines, emergency medicine, audit

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Authors: Balakrishna Valluru, Ruth Suckling

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A 16 year old male was found collapsed on the road following stab injuries to the chest and abdomen and was transported to the emergency department by ambulance. On arrival in the emergency department the patient was breathless and appeared pale. He was maintaining his airway with spontaneous breathing and had a heart rate of 122 beats per minute with a blood pressure of 83/63 mmHg. He was resuscitated initially with three units of packed red cells. Clinical examination identified three incisional wounds each measuring 2 cm. These were in the left para-sternal region, right infra-scapular region and left upper quadrant of the abdomen. The chest wound over the left parasternal area at the level of 4tth intercostal space was bleeding intermittently on leaning forwards and was relieving his breathlessness intermittently. CT imaging was performed to characterize his injuries and determine his management. CT scan of chest and abdomen showed moderate size haemopericardium with left sided haemopneumothorax. The patient underwent urgent surgical repair of the left ventricle and left anterior descending artery. He recovered without complications and was discharged from the hospital. This case highlights the fact that the potential to develop a life threatening cardiac tamponade was mitigated by the left parasternal stab wound. This injury fortuitously provided a pericardial window through which the bleeding from the injured left ventricle and left anterior descending artery could drain into the left hemithorax providing an opportunity for timely surgical intervention to repair the cardiac injuries.

Keywords: stab, incisional, haemo-pericardium, haemo-pneumothorax

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Authors: Mahdi Bazarganigilani

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Aortic enlargement, also known as an aortic aneurysm, can occur when the walls of the aorta become weak. This disease can become deadly if overlooked and undiagnosed. In this paper, a computer-aided diagnosis (CAD) system was introduced to accurately diagnose aortic enlargement from chest x-ray images. An enhanced convolutional neural network (CNN) was employed and then trained by transfer learning by using three different main areas from the original images. The areas included the left lung, heart, and right lung. The accuracy of the system was then evaluated on 1001 samples by using 4-fold cross-validation. A promising accuracy of 90% was achieved in terms of the F-measure indicator. The results showed using different areas from the original image in the training phase of CNN could increase the accuracy of predictions. This encouraged the author to evaluate this method on a larger dataset and even on different CAD systems for further enhancement of this methodology.

Keywords: computer-aided diagnosis systems, aortic enlargement, chest X-ray, image processing, convolutional neural networks

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Authors: Pranav Ajmera, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar, Purnachandra Lamghare

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The cardiothoracic ratio (CTR) is the ratio of the diameter of the heart to the diameter of the thorax. An abnormal CTR, that is, a value greater than 0.55, is often an indicator of an underlying pathological condition. The accurate prediction of an abnormal CTR from chest X-rays (CXRs) aids in the early diagnosis of clinical conditions. We propose a deep learning-based model for automatic CTR calculation that can assist the radiologist with the diagnosis of cardiomegaly and optimize the radiology flow. The study population included 1012 posteroanterior (PA) CXRs from a single institution. The Attention U-Net deep learning (DL) architecture was used for the automatic calculation of CTR. A CTR of 0.55 was used as a cut-off to categorize the condition as cardiomegaly present or absent. An observer performance test was conducted to assess the radiologist's performance in diagnosing cardiomegaly with and without artificial intelligence (AI) assistance. The Attention U-Net model was highly specific in calculating the CTR. The model exhibited a sensitivity of 0.80 [95% CI: 0.75, 0.85], precision of 0.99 [95% CI: 0.98, 1], and a F1 score of 0.88 [95% CI: 0.85, 0.91]. During the analysis, we observed that 51 out of 1012 samples were misclassified by the model when compared to annotations made by the expert radiologist. We further observed that the sensitivity of the reviewing radiologist in identifying cardiomegaly increased from 40.50% to 88.4% when aided by the AI-generated CTR. Our segmentation-based AI model demonstrated high specificity and sensitivity for CTR calculation. The performance of the radiologist on the observer performance test improved significantly with AI assistance. A DL-based segmentation model for rapid quantification of CTR can therefore have significant potential to be used in clinical workflows.

Keywords: cardiomegaly, deep learning, chest radiograph, artificial intelligence, cardiothoracic ratio

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Authors: Kulsum Maula, Md Kamrul Alam, Md Ibrahim Khalil, Md Nazmul Hasan, Mohammad Omar Faruq

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Background: Percutaneous Tube Thoracostomy (PTT) is an invasive procedure that can save a life now and then in different traumatic and non-traumatic conditions. But still, it is an enigma; how our trainee surgeons are at home in this procedure. Objectives: To evaluate the outcome of the percutaneous tube thoracostomy performed by trainees in both trauma and non-trauma patients. Study design: Prospective, Observational Study. The duration of the study was September 2018 to February 2019. Methods: All patients who need PTT in traumatic and non-traumatic conditions were selected by purposive sampling. Thereafter, they were scrutinized according to eligibility criteria and 96 patients were finalized. A pre-tested, observation-based, peer-reviewed data collection sheet was prepared before the study. Data regarding clinical and surgical outcome profiles were recorded. Data were compiled, edited, and analyzed. Results: Among 96 patients, the highest 32.29% belonged to age group 31-40 years and the lowest 9.37% belonged to the age group ≤20. The mean age of the respondents was 29.19±9.81. We found out of 96 patients, 70(72.91%) were indicated PTT for traumatic conditions and the rest 26(27.08%) were indicated PTT for non-traumatic chest conditions, where 36(37.5%) had simple penumothorax, 21(21.87%) haemothorax, 14(14.58%) massive pleural effusion, 13(13.54%) tension pneumothorax, 10(10.41%) haemopneumothorax, and 2(2.08%) had pyothorax respectively. In 53.12% of patients had right-sided intercostal chest tube (ICT) insertion, whereas 46.87% had left-sided ICT insertion. In our study, 89.55 % of the tube was placed at the normal anatomical position. Besides, 10.41% of tube thoracostomy were performed deviated from anatomical site. Among 96 patients 62.5% patients had length of incision 2-3cm, 35.41% had >3cm and 2.08% had <2cm respectively. Out of 96 patients, 75(78.13%) showed uneventful outcomes, whereas 21(21.87%) had complications, including 11.15%(11) each had wound infection, 4.46%(4) subcutaneous emphysema, 4.28%(3) drain auto expulsion, 2.85%(2) hemorrhage, 1.45%(1) had a non-functioning drain and empyema with ascending infection respectively (p=<0.05). Conclusion: PTT is a life-saving procedure that is most frequently implemented in chest trauma patients in our country. In the majority of cases, the outcome of PTT was uneventful (78.13). Besides this, more than one-third of patients had a length of incision more than 3 cm that needed extra stitches and 10.41% of cases of PTT were placed other than the normal anatomical site. Trainees of Dhaka Medical College Hospitals are doing well in their performance of PTT insertion, but still, some anatomical orientations are necessary to avoid operative and post-operative complications.

Keywords: PTT, trainee, trauma, non-chest trauma patients

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Authors: A. Nurgiartiningsih, G. Ciptadi, S. B. Siswijono

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This research was done to identify the performance and breeding potency of Local Buffalo in Kangean Island, Sumenep, East Java, Indonesia. Materials used were buffalo and farmer in Kangean Island. Method used was survey with purposive sampling method. Qualitative trait and existing breeding system including the type of production system were directly observed. Quantitative trait consisted of chest girth, body weight and wither height were measured and recorded. Data were analyzed using analysis of variance applying software GENSTAT 14. Results showed the purposes of buffalo breeding in Kangean Island were for production of calves, saving, religion tradition, and buffalo racing. The combination between grazing and cut and carry system were applied in Kangean Island. Forage, grass and agricultural waste product were available abundantly especially, during the wet season. Buffalo in Kangean Island was categorized as swamp buffalo with 48 chromosomes. Observation on qualitative trait indicated that there were three skin color types: gray (81.25%), red (10.42%) and white/albino (8.33%). Analysis on quantitative trait showed that there was no significant difference between male and female buffalo. The performance of male buffalo was 132.56 cm, 119.33 cm and 174.11 cm, for the mean of body length, whither height and chest girth, respectively. The performance of female buffalo were 129.8 cm, 114.0 cm and 166.2 cm, for mean of body length, wither height and chest girth (CG), respectively. The performance of local buffalo in Kangean Island was categorized well. Kangean Island could be promoted as center of buffalo breeding and conservation. For optimal improvement of population number and its genetics value, government policy in buffalo breeding program should be implemented.

Keywords: chromosome, qualitative trait, quantitative trait, swamp buffalo

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Authors: Saghar Karimi, Mohammad Saeed Ahrari Khafi, Amin Abolhasani Foroughi

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In recent decades, keeping exotic species as pet animals has become widespread. Turtles are exotic species from chelonians, which are interested by many people. Caspian pond and European pond turtles from Emydidea family are commonly kept as pets in Iran. Presence of the shell in turtles makes achievement to a comprehensive clinical examination impossible. Respiratory system is one of the most important structures to be examined completely. Presence of the air in the respiratory system makes radiography the first modality to think of; however, image quality would be affected by the shell. Computed tomography (CT) as a radiography-based and non-invasive technique provides cross-sectional scans with little superimposition. The aim of this study was to depict normal computed tomographic anatomy of the respiratory system in Caspian Pond Turtle. Five adult Caspian pond turtle were scanned using a 16-detector CT machine. Our results showed that computed tomography is able to well illustrated different parts of respiratory system in turtle and can be used for detecting abnormalities and disorders.

Keywords: anatomy, computed tomography, respiratory system, turtle

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Authors: Nasrin Azarbani

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Aim: Systemic sclerosis (SSc) is a chronic connective tissue disease with the clinical hallmark of skin thickening and tethering. Correlation of musculoskeletal features with other parameters should be considered in SSc patients. Methods: We reviewed the records of all patients who had more than one visit and standard anteroposterior radiography of hand. We used univariate analysis, and factors with p<0.05 were included in logistic regression to find out dependent factors. Results: Overall, 180 SSc patients were enrolled in our study, 161 (89.4%) of whom were women. Median age (IQR) was 47.0 years (16), and 52% had diffuse subtype of the disease. In multivariate analysis, tendon friction rubs (TFRs) was associated with the presence of calcinosis, muscle tenderness, and flexion contracture (FC) on physical examination (p<0.05). Arthritis showed no differences in the two subtypes of the disease (p=0.98), and in multivariate analysis, there were no correlations between radiographic arthritis and serological and clinical features. The radiographic results indicated that disease duration correlated with joint erosion, acro-osteolysis, resorption of distal ulna, calcinosis and radiologic FC (p< 0.05). Acro-osteolysis was more frequent in the dcSSc subtype, TFRs, and anti-TOPO I antibody. Radiologic FC showed an association with skin score, calcinosis and haematocrit <30% (p<0.05). Joint flexion on radiography was associated with disease duration, modified Rodnan skin score, calcinosis, and low haematocrit (P<0.01). Conclusion: Disease duration was a main dependent factor for developing joint erosion, acro-osteolysis, bone resorption, calcinosis, and flexion contracture on hand radiography. Acro-osteolysis presented in the severe form of the disease. Acro-osteolysis was the only dependent variable associated with bone demineralization.

Keywords: sclerosis, disease subsets, joint erosion, musculoskeletal

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Authors: Shirazu Issahaku, Isaac Kwesi Acquah, Simon Mensah Amoh, George Nunoo

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Introduction: Diagnostic Reference Levels are important indicators for monitoring and optimizing protocol and procedure in medical imaging between facilities and equipment. This helps to evaluate whether, in routine clinical conditions, the median value obtained for a representative group of patients within an agreed range from a specified procedure is unusually high or low for that procedure. This study aimed to propose Diagnostic Reference Levels for Computed Tomography examination of the most common routine examination of the head, chest and abdominal pelvis regions at the University of Ghana Medical Centre. Methods: The Diagnostic Reference Levels were determined based on the investigation of the most common routine examinations, including head Computed Tomography examination with and without contrast, abdominopelvic Computed Tomography examination with and without contrast, and chest Computed Tomography examination without contrast. The study was based on two dose indicators: the volumetric Computed Tomography Dose Index and Dose-Length Product. Results: The estimated median distribution for head Computed Tomography with contrast for volumetric-Computed Tomography dose index and Dose-Length Product were 38.33 mGy and 829.35 mGy.cm, while without contrast, were 38.90 mGy and 860.90 mGy.cm respectively. For an abdominopelvic Computed Tomography examination with contrast, the estimated volumetric-Computed Tomography dose index and Dose-Length Product values were 40.19 mGy and 2096.60 mGy.cm. In the absence of contrast, the calculated values were 14.65 mGy and 800.40 mGy.cm, respectively. Additionally, for chest Computed Tomography examination, the estimated values were 12.75 mGy and 423.95 mGy.cm for volumetric-Computed Tomography dose index and Dose-Length Product, respectively. These median values represent the proposed diagnostic reference values of the head, chest, and abdominal pelvis regions. Conclusions: The proposed Diagnostic Reference Level is comparable to the recommended International Atomic Energy Agency and International Commission Radiation Protection Publication 135 and other regional published data by the European Commission and Regional National Diagnostic Reference Level in Africa. These reference levels will serve as benchmarks to guide clinicians in optimizing radiation dose levels while ensuring accurate diagnostic image quality at the facility.

Keywords: diagnostic reference levels, computed tomography dose index, computed tomography, radiation exposure, dose-length product, radiation protection

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Authors: Amhimmid Mohammed Saffour, Mustafa Mohamed Abdullah

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The use of computers for handling image data in the healthcare is growing. However, the amount of data produced by modern image generating techniques is vast. This data might be a problem from a storage point of view or when the data is sent over a network. This paper using wavelet transform technique for medical images compression. MATLAB program, are designed to evaluate medical images storage and transmission time problem at Sebha Medical Center Libya. In this paper, three different Computed Tomography images which are abdomen, brain and chest have been selected and compressed using wavelet transform. Objective evaluation has been performed to measure the quality of the compressed images. For this evaluation, the results show that the Peak Signal to Noise Ratio (PSNR) which indicates the quality of the compressed image is ranging from (25.89db to 34.35db for abdomen images, 23.26db to 33.3db for brain images and 25.5db to 36.11db for chest images. These values shows that the compression ratio is nearly to 30:1 is acceptable.

Keywords: medical image, Matlab, image compression, wavelet's, objective evaluation

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Authors: Uma Maheshwari V., Rajanikanth Aluvalu, Kumar Gautam

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The COVID-19 disease is a highly contagious viral infection with major worldwide health implications. The global economy suffers as a result of COVID. The spread of this pandemic disease can be slowed if positive patients are found early. COVID-19 disease prediction is beneficial for identifying patients' health problems that are at risk for COVID. Deep learning and machine learning algorithms for COVID prediction using X-rays have the potential to be extremely useful in solving the scarcity of doctors and clinicians in remote places. In this paper, a convolutional neural network (CNN) with deep layers is presented for recognizing COVID-19 patients using real-world datasets. We gathered around 6000 X-ray scan images from various sources and split them into two categories: normal and COVID-impacted. Our model examines chest X-ray images to recognize such patients. Because X-rays are commonly available and affordable, our findings show that X-ray analysis is effective in COVID diagnosis. The predictions performed well, with an average accuracy of 99% on training photographs and 88% on X-ray test images.

Keywords: deep CNN, COVID–19 analysis, feature extraction, feature map, accuracy

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Authors: Hongmei Wang, Ziyun Xiang, Ying liu, Li Yu, Dongsheng Yue

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Intro: The COVID-19 (Corona Virus Disease 2019) has greatly changed the global economic, political and financial ecology. The mutation of the coronavirus in the UK in December 2020 has brought new panic to the world. Deep learning was performed on Chest Computed tomography (CT) of COVID-19 and Influenza and describes their characteristics. The predominant features of COVID-19 pneumonia was ground-glass opacification, followed by consolidation. Lesion density: most lesions appear as ground-glass shadows, and some lesions coexist with solid lesions. Lesion distribution: the focus is mainly on the dorsal side of the periphery of the lung, with the lower lobe of the lungs as the focus, and it is often close to the pleura. Other features it has are grid-like shadows in ground glass lesions, thickening signs of diseased vessels, air bronchi signs and halo signs. The severe disease involves whole bilateral lungs, showing white lung signs, air bronchograms can be seen, and there can be a small amount of pleural effusion in the bilateral chest cavity. At the same time, this year's flu season could be near its peak after surging throughout the United States for months. Chest CT for Influenza infection is characterized by focal ground glass shadows in the lungs, with or without patchy consolidation, and bronchiole air bronchograms are visible in the concentration. There are patchy ground-glass shadows, consolidation, air bronchus signs, mosaic lung perfusion, etc. The lesions are mostly fused, which is prominent near the hilar and two lungs. Grid-like shadows and small patchy ground-glass shadows are visible. Deep neural networks have great potential in image analysis and diagnosis that traditional machine learning algorithms do not. Method: Aiming at the two major infectious diseases COVID-19 and influenza, which are currently circulating in the world, the chest CT of patients with two infectious diseases is classified and diagnosed using deep learning algorithms. The residual network is proposed to solve the problem of network degradation when there are too many hidden layers in a deep neural network (DNN). The proposed deep residual system (ResNet) is a milestone in the history of the Convolutional neural network (CNN) images, which solves the problem of difficult training of deep CNN models. Many visual tasks can get excellent results through fine-tuning ResNet. The pre-trained convolutional neural network ResNet is introduced as a feature extractor, eliminating the need to design complex models and time-consuming training. Fastai is based on Pytorch, packaging best practices for in-depth learning strategies, and finding the best way to handle diagnoses issues. Based on the one-cycle approach of the Fastai algorithm, the classification diagnosis of lung CT for two infectious diseases is realized, and a higher recognition rate is obtained. Results: A deep learning model was developed to efficiently identify the differences between COVID-19 and influenza using chest CT.

Keywords: COVID-19, Fastai, influenza, transfer network

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Authors: Tesfahiwot Abay Weldearegay

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Ethiopian Federal ministry of health, in collaboration with different partners, provides financial support from sustainable development grants and global fund budget sources to Regional health science colleges through the regional health bureau to improve the quality of training and avail professionals based on the regional health bureau demand from the year of 2012 to 2019EC. It was mainly focused on health extension workers (HEW) Level III&IV, Health Information technicians (HIT), Emergency Medical technicians (EMT), laboratory technicians, Pharmacy technicians, Anesthesia Level V, Radiography, midwifery, Environmental health and biomedical equipment technician. Laboratory technician, Radiography and Pharmacy technician, was retooling program. The study aims at assessing the Utilization and outcome of budgets transferred through regional health bureau to regional health science colleges. The study used both quantitative and qualitative approaches to develop sufficient data to explain the utilization of the budget, and outcomes obtained from the transferred budget and to identify the gaps. The data for the study were obtained through structured questionnaires and interviews was conducted to increase the reliability of the data. Nationally, students enrolled in different disciplines at RHSC through budget support for RHB to improve the quality of training were 87 840 students and the total Budget transferred, according to MOU was 895,752,038 Ethiopian birr. Among the students enrolled nationally in different disciplines at RHSC through budget support only 72% of students have graduated from different disciplines. In Hareri and Addis Ababa, all enrolled students were graduated (100%). At the same time, Oromia 69%, Amara 77%, SNNP 58% students graduated, respectively. The demand of the regional health bureau and the enrollment capacity of health science colleges increased from year to year. The financial support added great value to the HSCs to cop with problems related to student fees, skill lab materials and renovation.

Keywords: emergency medical technician, radiography, Biomedical, health extension

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Devam Sheth

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Nuclear imaging with stress myocardial perfusion (stress test) is the preferred first line investigation for noninvasive evaluation of ischaemic heart condition. The sensitivity of this test is close to 90 % making it a very reliable test. However, rarely it gives a false negative result which can be explained by the phenomenon termed as “balanced ischaemia”. We present the case of a 78 year Caucasian female without any significant past cardiac history, who presents with chest pain and shortness of breath since one day. The initial ECG and cardiac enzymes were non-impressive. Few hours later, she had some substernal chest pain along with some ST segment depression in the lateral leads. Stress test comes back negative for any significant perfusion defects. However, given her typical symptoms, she underwent a cardiac catheterization which revealed significant triple vessel disease mandating her to get a bypass surgery. This unusual phenomenon of false nuclear stress test in the setting of positive ECG changes can be explained only by balanced ischemia wherein due to global myocardial ischemia, the stress test fails to reveal relative perfusion defects in the affected segments.

Keywords: balanced, false positive, ischemia, myocardial perfusion imaging

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Authors: Krenare Shabani, Kreshnike Dedushi Hoti, Serbeze Kabashi, Jeton Shatri, Arben Rroji, Mrikë Bunjaku, Leotrim Berisha, Jona Kosova, Edmond Puca, Bleriana Shabani

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Introduction: Workers in Kosovo's mining industry are subjected to hazardous working conditions and airborne particles, such as silica dust, which can cause silicosis and other severe respiratory illnesses. The purpose of this research is to assess the health impacts of such exposures, as well as the importance of imaging and functional testing in detecting pathological changes early on. Methodology: The study is prospective and cross-sectional and was carried out during the year 2024. 626 people (446 miners and 180 non-miners) were enrolled in the study. Subjects underwent spirometry and chest radiography. Data were analysed with SPSS24. Results: The average age of the participants is 48 years. Demographics and Smoking: Smoking was common among young miners. Radiological Changes: Radiographic abnormalities in the lungs were seen in 23.1% of miners and 10.6% of non-miners, including small irregular opacities and emphysematous changes. Lung Function: The FEV1/FVC ratio decreased with increased exposure time, indicating a decline in pulmonary function.Impact of Exposure Duration: Longer exposure duration was associated with a higher number of miners experiencing coughs and requiring medical consultations such as CT scans and biopsies. Conclusions: Medical imaging and functional testing are critical for early diagnosis of lung abnormalities in miners.Findings demonstrate a strong correlation between extended exposure to mine dust and the development of respiratory disorders, emphasising the importance of preventative measures and routine health monitoring.

Keywords: silicosis, miners, imaging, spirometry

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Fiona Bei Na Tan, Glen Wen Kiat Ho, Ee Leen Liow, Li Yin Tan, Sean Wei Loong Ho

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Background: Shoulder arthroscopy is an increasingly common procedure. Pneumothorax post-shoulder arthroscopy is a rare complication. Objectives: Our aim is to highlight a case report of pneumothorax post shoulder arthroscopy and to conduct a literature review to evaluate the possible risk factors associated with developing a pneumothorax during or after shoulder arthroscopy. Case Report: We report the case of a 75-year-old male non-smoker who underwent left shoulder arthroscopy without regional anaesthesia and in the left lateral position. The general anaesthesia and surgery were uncomplicated. The patient was desaturated postoperatively and was found to have a pneumothorax on examination and chest X-ray. A chest tube drain was inserted promptly into the right chest. He had an uncomplicated postoperative course. Methods: PubMed Medline and Cochrane database search was carried out using the terms shoulder arthroplasty, pneumothorax, pneumomediastinum, and subcutaneous emphysema. We selected full-text articles written in English. Results: Thirty-two articles were identified and thoroughly reviewed. Based on our inclusion and exclusion criteria, 14 articles, which included 20 cases of pneumothorax during or after shoulder arthroscopy, were included. Eighty percent (16/20) of pneumothoraxes occurred postoperatively. In the articles that specify the side of pneumothorax, 91% (10/11) occur on the ipsilateral side of the arthroscopy. Eighty-eight percent (7/8) of pneumothoraxes occurred when subacromial decompression was performed. Fifty-six percent (9/16) occurred in patients placed in the lateral decubitus position. Only 30% (6/20) occurred in current or ex-smokers, and only 25% (5/20) had a pre-existing lung condition. Overall, of the articles that posit a mechanism, 75% (9/12) deem the pathogenesis to be multifactorial. Conclusion: The exact mechanism of pneumothorax is currently unknown. Awareness of this complication and timely recognition are important to prevent life-threatening sequelae. Surgeons should have a low threshold to obtain diagnostic plain radiographs in the event of clinical suspicion.

Keywords: rotator cuff repair, decompression, pressure, complication

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Authors: Sardorbek Numonov, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Dongeun Choi, Byung-Woo Hong

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The detection of curvilinear structures often plays an important role in the analysis of images. In particular, it is considered as a crucial step for the diagnosis of chronic respiratory diseases to localize the fissures in chest CT imagery where the lung is divided into five lobes by the fissures that are characterized by linear features in appearance. However, the characteristic linear features for the fissures are often shown to be subtle due to the high intensity variability, pathological deformation or image noise involved in the imaging procedure, which leads to the uncertainty in the quantification of anatomical or functional properties of the lung. Thus, it is desired to enhance the linear features present in the chest CT images so that the distinctiveness in the delineation of the lobe is improved. We propose a recursive diffusion process that prefers coherent features based on the analysis of structure tensor in an anisotropic manner. The local image features associated with certain scales and directions can be characterized by the eigenanalysis of the structure tensor that is often regularized via isotropic diffusion filters. However, the isotropic diffusion filters involved in the computation of the structure tensor generally blur geometrically significant structure of the features leading to the degradation of the characteristic power in the feature space. Thus, it is required to take into consideration of local structure of the feature in scale and direction when computing the structure tensor. We apply an anisotropic diffusion in consideration of scale and direction of the features in the computation of the structure tensor that subsequently provides the geometrical structure of the features by its eigenanalysis that determines the shape of the anisotropic diffusion kernel. The recursive application of the anisotropic diffusion with the kernel the shape of which is derived from the structure tensor leading to the anisotropic scale-space where the geometrical features are preserved via the eigenanalysis of the structure tensor computed from the diffused image. The recursive interaction between the anisotropic diffusion based on the geometry-driven kernels and the computation of the structure tensor that determines the shape of the diffusion kernels yields a scale-space where geometrical properties of the image structure are effectively characterized. We apply our recursive anisotropic diffusion algorithm to the detection of curvilinear structure in the chest CT imagery where the fissures present curvilinear features and define the boundary of lobes. It is shown that our algorithm yields precise detection of the fissures while overcoming the subtlety in defining the characteristic linear features. The quantitative evaluation demonstrates the robustness and effectiveness of the proposed algorithm for the detection of fissures in the chest CT in terms of the false positive and the true positive measures. The receiver operating characteristic curves indicate the potential of our algorithm as a segmentation tool in the clinical environment. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: anisotropic diffusion, chest CT imagery, chronic respiratory disease, curvilinear structure, fissure detection, structure tensor

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Authors: Sahar Heidary, Ramin Ghasemi Shayan

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By the development of new coronavirus (2019-nCoV) pneumonia, chest high-resolution computed tomography (HRCT) has been one of the main investigative implements. To realize timely and truthful diagnostics, defining the radiological features of the infection is of excessive value. The purpose of this impression was to consider the imaging demonstrations of early-stage coronavirus disease 2019 (COVID-19) and to run an imaging base for a primary finding of supposed cases and stratified interference. The right prophetic rate of HRCT was 85%, sensitivity was 73% for all patients. Total accuracy was 68%. There was no important change in these values for symptomatic and asymptomatic persons. These consequences were besides free of the period of X-ray from the beginning of signs or interaction. Therefore, we suggest that HRCT is a brilliant attachment for early identification of COVID-19 pneumonia in both symptomatic and asymptomatic individuals in adding to the role of predictive gauge for COVID-19 pneumonia. Patients experienced non-contrast HRCT chest checkups and images were restored in a thin 1.25 mm lung window. Images were estimated for the existence of lung scratches & a CT severity notch was allocated separately for each patient based on the number of lung lobes convoluted.

Keywords: COVID-19, radiology, respiratory diseases, HRCT

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Authors: Keerthilatha Pai, Sakshi Kamra

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Objectives: Determining the position of the mandibular canal prior to implant placement and surgeries of the posterior mandible are important to avoid the nerve injury. The visibility of the mandibular canal varies according to the imaging modality. Although panoramic radiography is the most common, slowly cone beam computed tomography is replacing it. This study was conducted with an aim to determine and compare the visibility of superior and inferior borders of the mandibular canal in digital panoramic radiograph, reformatted panoramic radiograph and cross-sectional images of cone beam computed tomography. Study design: digital panoramic, reformatted panoramic radiograph and cross sectional CBCT images of 25 human mandibles were evaluated for the visibility of the superior and inferior borders of the mandibular canal according to a 5 point scoring criteria. Also, the canal was evaluated as completely visible, partially visible and not visible. The mean scores and visibility percentage of all the imaging modalities were determined and compared. The interobserver and intraobserver agreement in the visualization of the superior and inferior borders of the mandibular canal were determined. Results: The superior and inferior borders of the mandibular canal were completely visible in 47% of the samples in digital panoramic, 63% in reformatted panoramic and 75.6% in CBCT cross-sectional images. The mandibular canal was invisible in 24% of samples in digital panoramic, 19% in reformatted panoramic and 2% in cross-sectional CBCT images. Maximum visibility was seen in Zone 5 and least visibility in Zone 1. On comparison of all the imaging modalities, CBCT cross-sectional images showed better visibility of superior border in Zones 2,3,4,6 and inferior border in Zones 2,3,4,6. The difference was statistically significant. Conclusion: CBCT cross-sectional images were much superior in the visualization of the mandibular canal in comparison to reformatted and digital panoramic radiographs. The inferior border was better visualized in comparison to the superior border in digital panoramic imaging. The mandibular canal was maximumly visible in posterior one-third region of the mandible and the visibility decreased towards the mental foramen.

Keywords: cone beam computed tomography, mandibular canal, reformatted panoramic radiograph, visualization

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Authors: Angeli Marie P. Lagman, Nephtali M. Gorgonio

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Catamenial pneumothorax is defined as a recurrent accumulation of air in the pleural cavity, which occurs in the period of 72 hours before or after menses. In a menstruating woman presenting with the difficulty of breathing and chest pain with concomitant radiographic evidence of pneumothorax, a diagnosis of catamenial pneumothorax should be entertained. Two cases of catamenial pneumothorax were reported in our local literature. This report added two more cases. The first case is 45 years old G1P1, while the second case is 46 years old G2P2. These two patients had a history of pelvic endometriosis in the past. All other signs and symptoms were similar to the previously reported cases. All patients presented with difficulty of breathing associated with chest pain. Imaging studies showed right-sided pneumothorax in all patients. Intraoperatively, subpleural bleb, diaphragmatic fenestrations, and endometriotic implants were found. Three patients underwent video-assisted thoracosurgery (VATS), while one patient underwent open thoracotomy with pleurodesis. Histopathology revealed endometriosis in only two patients. All patients received postoperative hormonal therapy, and there were no recurrences noted in all patients. Endometriosis-related catamenial pneumothorax is a rare condition that needs early recognition of the symptoms. Several theories may be involved to explain the pathogenesis of catamenial pneumothorax. Two cases show a strong significant association between a history of pelvic endometriosis and the development of catamenial pneumothorax, while one case can be explained by the hormonal theory. The difficulty of breathing and chest pain in relation to menses may prompt early diagnosis. One case has shown that pneumothorax may occur even after menstruation. A biopsy of the endometrial implants may not always show endometrial glands and stroma, nor will immunostaining, which will not always show estrogen and progesterone receptors. Video-assisted thoracoscopic surgery is the gold standard in the diagnosis and treatment of catamenial pneumothorax. Postoperative hormonal suppression will further reduce the disease recurrence and facilitate the effectiveness of the surgical treatment.

Keywords: catamenial pneumothorax, endometriosis, menstruation, video assisted thoracosurgery

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Ghada Esheba, Bayan Hafiz, Ashwaq Al-Qarni, Abdulelah AlMalki, Esraa Kaheel

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Hydatid disease is caused by genus Echinococcus, it is transmitted to human through sheep and cattle. People who lived in an endemic area should be suspected to have the disease. Pulmonary hydatid disease can be presented by respiratory manifestations as in our case. We report a case of child, 13 years old, who was presented by shortness of breath and non-productive cough 2 months ago. The patient had an attack of hemoptysis 3 months ago but there is no history of fever, other constitutional symptoms or any medical illness. The patient has had a close contact with a horse. On examination, the patient was oriented and vitally stable. Both side of chest were moving equally with decrease air entry on the left side of the chest. Cervical lymph node enlargement was also detected. The case was provisionally diagnosed as tuberculosis. The x-ray was normal, while CT scan showed two cysts in the left side. The patient was treated surgically with resection of both cysts without lobectomy. Broncho-alveolar lavage was done and together with plural effusion and both cysts were sent for histopathology. The patient received the following medication: albendazole 200MG/BID/Orally for 30 days and Cefuroxime 250MG/Q12H/Orally for 10 days.

Keywords: Echinococcus granulosus, hydatid disease, pediatrics, pulmonary hydatid cyst

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Adhimoolam Saravana Kumar, K. N. Govindarajan, B. Devanand, R. Rajakumar

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The quality of Computed Tomography (CT) procedures has improved in recent years due to technological developments and increased diagnostic ability of CT scanners. Due to the fact that CT doses are the peak among diagnostic radiology practices, it is of great significance to be aware of patient’s CT radiation dose whenever a CT examination is preferred. CT radiation dose delivered to patients in the form of volume CT dose index (CTDIvol) values, is displayed on scanner monitors at the end of each examination and it is an important fact to assure that this information is accurate. The objective of this study was to estimate the CTDIvol values for great number of patients during the most frequent CT examinations, to study the comparison between CT dose monitor values and measured ones, as well as to highlight the fluctuation of CTDIvol values for the same CT examination at different centres and scanner models. The output CT dose indices measurements were carried out on single and multislice scanners for available kV, 5 mm slice thickness, 100 mA and FOV combination used. The 100 CT scanners were involved in this study. Data with regard to 15,000 examinations in patients, who underwent routine head, chest and abdomen CT were collected using a questionnaire sent to a large number of hospitals. Out of the 15,000 examinations, 5000 were head CT examinations, 5000 were chest CT examinations and 5000 were abdominal CT examinations. Comprehensive quality assurance (QA) was performed for all the machines involved in this work. Followed by QA, CT phantom dose measurements were carried out in South India using actual scanning parameters used clinically by the hospitals. From this study, we have measured the mean divergence between the measured and displayed CTDIvol values were 5.2, 8.4, and -5.7 for selected head, chest and abdomen procedures for protocols as mentioned above, respectively. Thus, this investigation revealed an observable change in CT practices, with a much wider range of studies being performed currently in South India. This reflects the improved capacity of CT scanners to scan longer scan lengths and at finer resolutions as permitted by helical and multislice technology. Also, some of the CT scanners have used smaller slice thickness for routine CT procedures to achieve better resolution and image quality. It leads to an increase in the patient radiation dose as well as the measured CTDIv, so it is suggested that such CT scanners should select appropriate slice thickness and scanning parameters in order to reduce the patient dose. If these routine scan parameters for head, chest and abdomen procedures are optimized than the dose indices would be optimal and lead to the lowering of the CT doses. In South Indian region all the CT machines were routinely tested for QA once in a year as per AERB requirements.

Keywords: CT dose index, weighted CTDI, volumetric CTDI, radiation dose

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Authors: H. N. Alyaseen, S. E. Alalawi, T. Cordoba, É. Delisle, C. Cordoba, A. Odobescu

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Sternal dehiscence is defined as the persistent separation of sternal bones that are often complicated with mediastinitis. Etiologies that lead to sternal dehiscence vary, with cardiovascular and thoracic surgeries being the most common. Early diagnosis in susceptible patients is crucial to the management of such cases, as they are associated with high mortality rates. A recent meta-analysis of more than four hundred thousand patients concluded that deep sternal wound infections were the leading cause of mortality and morbidity in patients undergoing cardiac procedures. Long-term complications associated with sternal dehiscence include increased hospitalizations, cardiac infarctions, and renal and respiratory failures. Numerous osteosynthesis methods have been described in the literature. Surgical materials offer enough rigidity to support the sternum and can be flexible enough to allow physiological breathing movements of the chest; however, these materials fall short when managing patients with extensive bone loss, osteopenia, or general poor bone quality, for such cases, flaps offer a better closure system. Early utilization of flaps yields better survival rates compared to delayed closure or to patients treated with sternal rewiring and closed drainage. The utilization of pectoralis major flaps, rectus abdominus, and latissimus muscle flaps have all been described in the literature as great alternatives. Flap selection depends on a variety of factors, mainly the size of the sternal defect, infection, and the availability of local tissues. Free fibular flaps are commonly harvested flaps utilized in reconstruction around the body. In cases regarding sternal reconstruction with free fibular flaps, the literature exclusively discussed the flap applied vertically to the chest wall. We present a different technique applying the free fibular triple barrel flap oriented in a transverse manner, in parallel to the ribs. In our experience, this method could have enhanced results and improved prognosis as it contributes to the normal circumferential shape of the chest wall.

Keywords: sternal dehiscence, management, free fibular flaps, novel surgical techniques

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Luca Collini, Michele Antolotti, Diego Schiavi

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At present, complex geometries within production time shrinkage, rapidly increasing demand, and high-quality standard requirement make the non-destructive (ND) control of additively manufactured components indispensable means. On the other hand, a technology gap and the lack of standards regulating the methods and the acceptance criteria indicate the NDT of these components a stimulating field to be still fully explored. Up to date, penetrant testing, acoustic wave, tomography, radiography, and semi-automated ultrasound methods have been tested on metal powder based products so far. External defects, distortion, surface porosity, roughness, texture, internal porosity, and inclusions are the typical defects in the focus of testing. Detection of density and layers compactness are also been tried on stainless steels by the ultrasonic scattering method. In this work, the authors want to present and discuss the radiographic and the ultrasound ND testing on additively manufactured Ti₆Al₄V and inconel parts obtained by the selective laser melting (SLM) technology. In order to test the possibilities given by the radiographic method, both X-Rays and γ-Rays are tried on a set of specifically designed specimens realized by the SLM. The specimens contain a family of defectology, which represent the most commonly found, as cracks and lack of fusion. The tests are also applied to real parts of various complexity and thickness. A set of practical indications and of acceptance criteria is finally drawn.

Keywords: non-destructive testing, selective laser melting, radiography, UT method

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Authors: Jaweriya Aftab, Madho Mal, Hamida Memon

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The first case of coronavirus disease 2019 (COVID-19) in Pakistan was recorded on February 26th, 2020. While the country went through various phases of lockdowns, the importance of proper sensitization campaigns was highlighted by healthcare workers to combat misinformation. Past studies via Google trends analysis have shown a rise in public interest in multiple COVID-19-related symptoms as well as cardiovascular symptoms. As there is a paucity of data related to the trends in Pakistan, we conducted a retrospective analysis to bridge further information. Methods: As per the recommendations from past studies, a Google trend analysis was conducted for various symptoms, including ‘Fever’, ‘Chest Pain’, ‘Shortness of Breath’, and ‘Cough’ between 1st January 2019 to 31st December 2021. The trends in various search results were analyzed and modeled. Results: Our analysis found various rises in public interest in the various symptoms (fever, chest pain, shortness of breath, and cough) that correspond closely to the wave of the virus's spread in the country. Conclusion: Our study confirms similar trends in Pakistan as previously reported in studies from India, USA, and UK, whereby the public interest in various COVID-19 symptoms rose with the number of cases. This further highlights the need for a strong approach to combat misinformation during such a critical period.

Keywords: covid, trend, Pakistan, public

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