Search results for: diagnostic

Authors: Sohail Imran, Tariq Mahmood

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In complex healthcare diagnostic investigation process, medical practitioners have to focus on ways to standardize their processes to perform high quality care and optimize the time and costs. Process mining techniques can be applied to extract process related knowledge from data without considering causal and dynamic dependencies in business domain and processes. The application of process mining is effective in diagnostic investigation. It is very helpful where a treatment gives no dispositive evidence favoring it. In this paper, we applied process mining to discover important process flow of diagnostic investigation for hepatitis patients. This approach has some benefits which can enhance the quality and efficiency of diagnostic investigation processes.

Keywords: process mining, healthcare, diagnostic investigation process, process flow

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Authors: Vijayan Narayananayar

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Identifying learning gaps is crucial in ensuring learners have the necessary knowledge and skills to succeed. The Learning and Teaching (L&T) approach requires tutors to identify gaps in knowledge and improvise learning activities to close them. One approach to identifying learning gaps is through diagnostic assessment, which uses well-structured questions and answer options. The paper focuses on the use of diagnostic assessment as a learning and teaching approach in a foundational module at a polytechnic. The study used diagnostic assessment over two semesters, including the COVID and post-COVID semesters, to identify gaps in learning. The design of the diagnostic activity, pedagogical intervention, and survey responses completed by learners were analyzed. Results showed that diagnostic assessment can be an effective tool for identifying learning gaps and designing interventions to address them. Additionally, the use of diagnostic assessment provides an opportunity for tutors to engage with learners on a one-to-one basis, tailoring teaching to individual needs. The paper also discusses the design of diagnostic questions and answer options, including characteristics that need to be considered in achieving the target of identifying learning gaps. The implications of using diagnostic assessment as a learning and teaching approach include bridging the gap between theory and practice, and ensuring learners are equipped with skills necessary for their future careers. This paper can be useful in helping educators and practitioners to incorporate diagnostic assessment into their L&T approach.

Keywords: assessment, learning & teaching, diagnostic assessment, analytics

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Authors: S. Sina, B. Zeinali, M. Karimipourfard, F. Lotfalizadeh, M. Sadeghi, E. Zamani, M. Zehtabian, R. Faghihi

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Proper dosimetery is very essential in diagnostic radiology. The goal of this study is to verify the application of LiF:Mg, Cu, P (TLD100H) in obtaining the entrance skin dose (ESD) of patients undergoing diagnostic radiology. The results of dosimetry performed by TLD-100H were compared with those obtained by TLD100, which is a common dosimeter in diagnostic radiology. The results show a close agreement between the dose measured by the two dosimeters. According to the results of this study, the TLD-100H dosimeters have higher sensitivities (i.e. signal(nc)/dose) than TLD-100. Therefore, it is suggested that the TLD-100H are effective dosimeters for dosimetry in low dose fields.

Keywords: entrance skin dose, TLD, diagnostic radiology, dosimeter

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Authors: Kyaw Zin Htut

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In this article, the synthesis of the efficiency of intelligent diagnostic system in the aircraft measuring devices is described. The technology developments of the diagnostic system are considered based on the model errors of the gyro instruments, which are used to measure the parameters of the aircraft. The synthesis of the diagnostic intelligent system is considered on the example of the problem of assessment and forecasting errors of the gyroscope devices on the onboard aircraft. The result of the system is to detect of faults of the aircraft measuring devices as well as the analysis of the measuring equipment to improve the efficiency of its work.

Keywords: diagnostic, dynamic system, errors of gyro instruments, model errors, assessment, prognosis

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Authors: Zhidong Zhang, Yingchen Yang

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In this study, a diagnostic assessment model for Mastery Engineering Learning was established based on a group of undergraduate students who studied in an engineering course. A diagnostic assessment model can examine both students' learning process and report achievement results. One very unique characteristic is that the diagnostic assessment model can recognize the errors and anything blocking students in their learning processes. The feedback is provided to help students to know how to solve the learning problems with alternative strategies and help the instructor to find alternative pedagogical strategies in the instructional designs. Dynamics is a core course in which is a common course being shared by several engineering programs. This course is a very challenging for engineering students to solve the problems. Thus knowledge acquisition and problem-solving skills are crucial for student success. Therefore, developing an effective and valid assessment model for student learning are of great importance. Diagnostic assessment is such a model which can provide effective feedback for both students and instructor in the mastery of engineering learning.

Keywords: diagnostic assessment, mastery learning, engineering, bayesian network model, learning processes

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Authors: Yuxuan Tang

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Cognitive diagnostic assessment has received much attention in second language education, and assessment for it can provide pedagogically useful feedback for language learners. However, there is a lack of research on how students interpret and use cognitive diagnostic feedback. Thus the present study aims to adopt a mixed-method approach mainly to explore the relationship between the goal-orientation and students' response to cognitive diagnostic feedback. Almost 200 Chinese undergraduates from two universities in Xi'an, China, will be invited to do a cognitive diagnostic reading test, and each student will receive specialized cognitive diagnostic feedback, comprising of students' reading attributes mastery level generated by applying a well-selected cognitive diagnostic model, students' perceived reading ability assessed by a self-assessing questionnaire and students’ level position in the whole class. And a goal-orientation questionnaire and a self-generated questionnaire on the perception of feedback will be given to students the moment they receive feedback. In addition, interviews of students will be conducted on their future plans to see whether they have awareness of carrying out studying plans. The study aims to find a new perspective towards how students use and interpret cognitive diagnostic feedback in terms of their different goal-orientation (self-based, task-based, and other-based goals) by applying the newest goal orientation model, which is an important construct of motivation in psychology, seldom researched under language learning area. And the study is expected to provide evidence on how diagnostic feedback promotes students' learning under the educational belief of assessment for learning. Practically speaking, according to the personalized diagnostic feedback, students can take remedial self-learning more purposefully, and teachers can target students' weaknesses to adjust teaching methods and carry out tailored teaching.

Keywords: assessment for learning, cognitive diagnostic assessment, goal-orientation, personalized feedback

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Authors: Wenbo Du, Xiaomei Ma

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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: M. Barański

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This paper presents a several diagnostic methods designed to electrical machines especially for permanent magnets (PM) machines. Those machines are commonly used in small wind and water systems and vehicles drives. Those methods are preferred by the author in periodic diagnostic of electrical machines. The special attention should be paid to diagnostic method of turn-to-turn insulation and vibrations. Both of those methods were created in Institute of Electrical Drives and Machines Komel. The vibration diagnostic method is the main thesis of author’s doctoral dissertation. This is method of determination the technical condition of PM electrical machine basing on its own signals is the subject of patent application No P.405669. Specific structural properties of machines excited by permanent magnets are used in this method - electromotive force (EMF) generated due to vibrations. There was analysed number of publications which describe vibration diagnostic methods and tests of electrical machines with permanent magnets and there was no method found to determine the technical condition of such machine basing on their own signals.

Keywords: electrical vehicle, generator, main insulation, permanent magnet, thermography, turn-to-traction drive, turn insulation, vibrations

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Authors: Mohamed Hamada Abdelkader Fayed

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Background: To evaluate the diagnostic accuracy of (ADD-RS) with D-dimer as a screening test to exclude AAS. Methods: We conducted research for the studies examining the diagnostic accuracy of (ADD- RS)+ D-dimer to exclude the diagnosis of AAS, We searched MEDLINE, Embase, and Cochrane of Trials up to 31 December 2020. Results: We identified 3 studies using (ADD-RS) with D-dimer as a diagnostic tool for AAS, involving 3261 patients were AAS was diagnosed in 559(17.14%) patients. Overall results showed that the pooled sensitivities were 97.6 (95% CI 0.95.6, 99.6) at (ADD-RS)≤1(low risk group) with D-dimer and 97.4(95% CI 0.95.4,, 99.4) at (ADD-RS)>1(High risk group) with D-dimer., the failure rate was 0.48% at low risk group and 4.3% at high risk group respectively. Conclusions: (ADD-RS) with D-dimer was a useful screening test with high sensitivity to exclude Acute Aortic Syndrome.

Keywords: aortic dissection detection risk score, D-dimer, acute aortic syndrome, diagnostic accuracy

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Authors: Eun Young Park, Jongwon Park

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A physical touch of a product can provide ample diagnostic information about the product attributes and quality. However, consumers’ product judgments and purchases can be erroneously influenced by non-diagnostic haptic information. For example, consumers’ evaluations of the coffee they drink could be affected by the heaviness of a cup that is used for just serving the coffee. This important issue has received little attention in prior research. The present research contributes to the literature by identifying when and how non-diagnostic haptic information can have an influence and why such influence occurs. Specifically, five studies experimentally varied the content of non-diagnostic haptic information, such as the weight of a cup (heavy vs. light) and the texture of a cup holder (smooth vs. rough), and then assessed the impact of the manipulation on product judgments and decisions. Results show that non-diagnostic haptic information has a biasing impact on consumer judgments. For example, the heavy (vs. light) cup increases consumers’ perception of the richness of coffee in it, and the rough (vs. smooth) texture of a cup holder increases the perception of the healthfulness of fruit juice in it, which in turn increases consumers’ purchase intentions of the product. When consumers are cognitively distracted during the touch experience, the impact of the content of haptic information is no longer evident, but the valence (positive vs. negative) of the haptic experience influences product judgments. However, consumers are able to avoid the impact of non-diagnostic haptic information, if and only if they are both knowledgeable about the product category and undistracted from processing the touch experience. In sum, the nature of the influence by non-diagnostic haptic information (i.e., assimilation effect vs. contrast effect vs. null effect) is determined by the content and valence of haptic information, the relative impact of which depends on whether consumers can identify the content and source of the haptic information. Theoretically, to our best knowledge, this research is the first to document the empirical evidence of the interplay between cognitive and affective processes that determines the impact of non-diagnostic haptic information. Managerial implications are discussed.

Keywords: consumer behavior, haptic information, product judgments, touch effect

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Authors: Justina I. R. Udotong, Essien U. Essien

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Diagnostic enzymes like aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) were determined as indices of heavy metal pollution in Tilapia guinensis. Three different sets of fishes treated with lead (Pb), iron (Fe) and copper (Cu) were used for the study while a fourth group with no heavy metal served as a control. Fishes in each of the groups were exposed to 2.65 mg/l of Pb, 0.85 mg/l of Fe and 0.35 mg/l of Cu in aerated aquaria for 96 hours. Tissue fractionation of the liver tissues was carried out and the three diagnostic enzymes (AST, ALT, and ALP) were estimated. Serum levels of the same diagnostic enzymes were also measured. The mean values of the serum enzyme activity for ALP in each experimental group were 19.5±1.62, 29.67±2.17 and 1.15±0.27 IU/L for Pb, Fe and Cu groups compared with 9.99±1.34 IU/L enzyme activity in the control. This result showed that Pb and Fe caused increased release of the enzyme into the blood circulation indicating increased tissue damage while Cu caused a reduction in the serum level as compared with the level in the control group. The mean values of enzyme activity obtained in the liver were 102.14±6.12, 140.17±2.06 and 168.23±3.52 IU/L for Pb, Fe and Cu groups, respectively compared to 91.20±9.42 IU/L enzyme activity for the control group. The serum and liver AST and ALT activities obtained in Pb, Fe, Cu and control groups are reported. It was generally noted that the presence of the heavy metal caused liver tissues damage and consequent increased level of the diagnostic enzymes in the serum.

Keywords: diagnostic enzymes, enzyme activity, heavy metals, tissues investigations

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Authors: Ananda Perera

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Computer-Assisted Medical Evaluation of Symptoms (CAMEOS) is a medical expert system designed to help General Practices (GPs) make an accurate diagnosis. CAMEOS comprises a knowledge base, user input, inference engine, reasoning module, and output statement. The knowledge base was developed by the author. User input is an Html file. The physician user collects data in the consultation. Data is sent to the inference engine at servers. CAMEOS uses set theory to simulate diagnostic reasoning. The program output is a list of differential diagnoses, the most probable diagnosis, and the diagnostic reasoning.

Keywords: CDSS, computerized decision support systems, expert systems, general practice, diagnosis, diagnostic systems, primary care diagnostic system, artificial intelligence in medicine

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Authors: Sabrina Azzi, Michal Iglewski, Véronique Nabelsi

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Diagnosis error problem is frequent and one of the most important safety problems today. One of the main objectives of our work is to propose an ontological representation that takes into account the diagnostic criteria in order to improve the diagnostic. We choose pneumonia disease since it is one of the frequent diseases affected by diagnosis errors and have harmful effects on patients. To achieve our aim, we use a semi-automated method to integrate diverse knowledge sources that include publically available pneumonia disease guidelines from international repositories, biomedical ontologies and electronic health records. We follow the principles of the Open Biomedical Ontologies (OBO) Foundry. The resulting ontology covers symptoms and signs, all the types of pneumonia, antecedents, pathogens, and diagnostic testing. The first evaluation results show that most of the terms are covered by the ontology. This work is still in progress and represents a first and major step toward a development of a diagnosis decision support system for pneumonia.

Keywords: Clinical decision support system, Diagnostic errors, Ontology, Pneumonia

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Authors: Okhee Woo, Hye Seon Shin

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Purpose: To investigate the diagnostic power of digital breast tomosynthesis (DBT) in evaluation of breast microcalcifications and usefulness as a pre-procedural study for stereotactic biopsy in comparison with full-field digital mammogram (FFDM) and FFDM plus magnification image (FFDM+MAG). Methods and Materials: An IRB approved retrospective observer performance study on DBT, FFDM, and FFDM+MAG was done. Image quality was rated in 5-point scoring system for lesion clarity (1, very indistinct; 2, indistinct; 3, fair; 4, clear; 5, very clear) and compared by Wilcoxon test. Diagnostic power was compared by diagnostic values and AUC with 95% confidence interval. Additionally, procedural report of biopsy was analysed for patient positioning and adequacy of instruments. Results: DBT showed higher lesion clarity (median 5, interquartile range 4-5) than FFDM (3, 2-4, p-value < 0.0001), and no statistically significant difference to FFDM+MAG (4, 4-5, p-value=0.3345). Diagnostic sensitivity and specificity of DBT were 86.4% and 92.5%; FFDM 70.4% and 66.7%; FFDM+MAG 93.8% and 89.6%. The AUCs of DBT (0.88) and FFDM+MAG (0.89) were larger than FFDM (0.59, p-values < 0.0001) but there was no statistically significant difference between DBT and FFDM+MAG (p-value=0.878). In 2 cases with DBT, petit needle could be appropriately prepared; and other 3 without DBT, patient repositioning was needed. Conclusion: DBT showed better image quality and diagnostic values than FFDM and equivalent to FFDM+MAG in the evaluation of breast microcalcifications. Evaluation with DBT as a pre-procedural study for breast stereotactic biopsy can lead to more accurate localization and successful biopsy and also waive the need for additional magnification images.

Keywords: DBT, breast cancer, stereotactic biopsy, mammography

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Authors: Hyun-Woo Cho

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Continuous monitoring of industrial plants is one of necessary tasks when it comes to ensuring high-quality final products. In terms of monitoring and diagnosis, it is quite critical and important to detect some incipient abnormal events of manufacturing processes in order to improve safety and reliability of operations involved and to reduce related losses. In this work a new multivariate statistical online diagnostic method is presented using a case study. For building some reference models an empirical discriminant model is constructed based on various past operation runs. When a fault is detected on-line, an on-line diagnostic module is initiated. Finally, the status of the current operating conditions is compared with the reference model to make a diagnostic decision. The performance of the presented framework is evaluated using a dataset from complex industrial processes. It has been shown that the proposed diagnostic method outperforms other techniques especially in terms of incipient detection of any faults occurred.

Keywords: data mining, empirical model, on-line diagnostics, process fault, process monitoring

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Suprit Pradhan, Tshering Yangzom

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Even with availability of the modern day online insulation diagnostic technologies like partial discharge monitoring, the measurements like Dissipation Factor (tanδ), DC High Voltage Insulation Currents, Polarization Index (PI) and Insulation Resistance Measurements are still widely used as a diagnostic tools to assess the condition of stator insulation in hydro power plants. To evaluate the condition of stator winding insulation in one of the generators that have been operated since 1999, diagnostic tests were performed on the stator bars of 15 MVA generators of Basochhu Hydropower Plant. This paper presents diagnostic study done on the data gathered from the measurements which were performed in 2015 and 2016 as part of regular maintenance as since its commissioning no proper aging data were maintained. Measurement results of Dissipation Factor, DC High Potential tests and Polarization Index are discussed with regard to their effectiveness in assessing the ageing condition of the stator insulation. After a brief review of the theoretical background, the strengths of each diagnostic method in detecting symptoms of insulation deterioration are identified. The interesting results observed from Basochhu Hydropower Plant is taken into consideration to conclude that Polarization Index and DC High Voltage Insulation current measurements are best suited for the detection of humidity and contamination problems and Dissipation Factor measurement is a robust indicator of long-term ageing caused by oxidative degradation.

Keywords: dissipation Factor (tanδ), polarization Index (PI), DC High Voltage Insulation Current, insulation resistance (IR), Tan Delta Tip-Up, dielectric absorption ratio

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Authors: K. Hinde, R. Bookun, P. Tran

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Percutaneous CT guided biopsies provide a fast, minimally invasive, cost effective and safe method for obtaining tissue for histopathology and culture. Standards for diagnostic yield vary depending on whether the tissue is being obtained for histopathology or culture. We present a retrospective audit from Western Health in Melbourne Australia over a 12-month period which aimed to determine the diagnostic yield, technical success and complication rate for CT guided bone biopsies and identify factors affecting these results. The digital imaging storage program (Synapse Picture Archiving and Communication System – Fujifilm Australia) was analysed with key word searches from October 2015 to October 2016. Nineteen CT guided bone biopsies were performed during this time. The most common referring unit was oncology, work up imaging included CT, MRI, bone scan and PET scan. The complication rate was 0%, overall diagnostic yield was 74% with a technical success of 95%. When performing biopsies for histologic analysis diagnostic yield was 85% and when performing biopsies for bacterial culture diagnostic yield was 60%. There was no significant relationship identified between size of lesion, distance of lesion to skin, lesion appearance on CT, the number of samples taken or gauge of needle to diagnostic yield or technical success. CT guided bone biopsy at Western Health meets the standard reported at other major clinical centres for technical success and safety. It is a useful investigation in identification of primary malignancy in distal bone metastases.

Keywords: bone biopsy, computed tomography, core biopsy, histopathology

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Authors: Jae Ni Jang, SoYoon Park, Sukhee Park, Yumin Song, Jae Won Kim, Keum Nae Kang, Young Uk Kim

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Objectives: Patellofemoral pain syndrome (PFPS) is a common clinical condition characterized by anterior knee pain. Here, we investigated the quadriceps tendon cross-sectional area (QTCSA) as a novel predictor for the diagnosis of PFPS. By examining the association between the QTCSA and PFPS, we aimed to provide a more valuable diagnostic parameter and more equivocal assessment of the diagnostic potential of PFPS by comparing the QTCSA with the quadriceps tendon thickness (QTT), a traditional measure of quadriceps tendon hypertrophy. Patients and Methods: This retrospective study included 30 patients with PFPS and 30 healthy participants who underwent knee magnetic resonance imaging. T1-weighted turbo spin echo transverse magnetic resonance images were obtained. The QTCSA was measured on the axial-angled phases of the images by drawing outlines, and the QTT was measured at the most hypertrophied quadriceps tendon. Results: The average QTT and QTCSA for patients with PFPS (6.33±0.80 mm and 155.77±36.60 mm², respectively) were significantly greater than those for healthy participants (5.77±0.36 mm and 111.90±24.10 mm2, respectively; both P<0.001). We used a receiver operating characteristic curve to confirm the sensitivities and specificities for both the QTT and QTCSA as predictors of PFPS. The optimal diagnostic cutoff value for QTT was 5.98 mm, with a sensitivity of 66.7%, a specificity of 70.0%, and an area under the curve of 0.75 (0.62–0.88). The optimal diagnostic cutoff value for QTCSA was 121.04 mm², with a sensitivity of 73.3%, a specificity of 70.0%, and an area under the curve of 0.83 (0.74–0.93). Conclusion: The QTCSA was found to be a more reliable diagnostic indicator for PFPS than QTT.

Keywords: patellofemoral pain syndrome, quadriceps muscle, hypertrophy, magnetic resonance imaging

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Authors: Muhammad Iqbal, Edi Istiyono

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Misconceptions are one of the problems in physics learning where students' understanding is not in line with scientific theory. The aim of this research is to find diagnostic tools to identify misconceptions and how to remediate physics misconceptions. In this research, the articles that will be reviewed come from the Scopus database related to physics misconceptions from 2013-2023. The articles obtained from the Scopus database were then selected according to the Prisma model, so 29 articles were obtained that focused on discussing physics misconceptions, especially regarding diagnostic tools and remediation methods. Currently, the most widely used diagnostic tool is the four-tier test, which is able to measure students' misconceptions in depth by knowing whether students are guessing or not and from then on, there is also a trend toward five-tier diagnostic tests with additional sources of information obtained. So that the origin of students' misconceptions is known. There are several ways to remediate student misconceptions, namely 11 ways and one of the methods used is digital practicum so that abstract things can be visualized into real ones. This research is limited to knowing what tools are used to diagnose and remediate misconceptions, so it is not yet known how big the effect of remediation methods is on misconceptions. The researcher recommends that in the future further research can be carried out to find out the most appropriate remediation method for remediating student misconceptions.

Keywords: misconception, remediation, systematic review, tools

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Authors: González A. Julio, Ramírez L. Leonardo, Puerta A. Gabriel

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Telemedicine services use a large amount of data, most of which are diagnostic images in Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7) formats. Metadata is generated from each related image to support their identification. This study presents the use of decision trees for the optimization of information search processes for diagnostic images, hosted on the cloud server. To analyze the performance in the server, the following quality of service (QoS) metrics are evaluated: delay, bandwidth, jitter, latency and throughput in five test scenarios for a total of 26 experiments during the loading and downloading of DICOM images, hosted by the telemedicine group server of the Universidad Militar Nueva Granada, Bogotá, Colombia. By applying decision trees as a data mining technique and comparing it with the sequential search, it was possible to evaluate the search times of diagnostic images in the server. The results show that by using the metadata in decision trees, the search times are substantially improved, the computational resources are optimized and the request management of the telemedicine image service is improved. Based on the experiments carried out, search efficiency increased by 45% in relation to the sequential search, given that, when downloading a diagnostic image, false positives are avoided in management and acquisition processes of said information. It is concluded that, for the diagnostic images services in telemedicine, the technique of decision trees guarantees the accessibility and robustness in the acquisition and manipulation of medical images, in improvement of the diagnoses and medical procedures in patients.

Keywords: cloud storage, decision trees, diagnostic image, search, telemedicine

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Authors: Khanh To Duc, Monica Chiogna, Gianfranco Adimari

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With three diagnostic categories, assessment of the performance of diagnostic tests is achieved by the analysis of the receiver operating characteristic (ROC) surface, which generalizes the ROC curve for binary diagnostic outcomes. The volume under the ROC surface (VUS) is a summary index usually employed for measuring the overall diagnostic accuracy. When the true disease status can be exactly assessed by means of a gold standard (GS) test, unbiased nonparametric estimators of the ROC surface and VUS are easily obtained. In practice, unfortunately, disease status verification via the GS test could be unavailable for all study subjects, due to the expensiveness or invasiveness of the GS test. Thus, often only a subset of patients undergoes disease verification. Statistical evaluations of diagnostic accuracy based only on data from subjects with verified disease status are typically biased. This bias is known as verification bias. Here, we consider the problem of correcting for verification bias when continuous diagnostic tests for three-class disease status are considered. We assume that selection for disease verification does not depend on disease status, given test results and other observed covariates, i.e., we assume that the true disease status, when missing, is missing at random. Under this assumption, we discuss several solutions for ROC surface analysis based on imputation and re-weighting methods. In particular, verification bias-corrected estimators of the ROC surface and of VUS are proposed, namely, full imputation, mean score imputation, inverse probability weighting and semiparametric efficient estimators. Consistency and asymptotic normality of the proposed estimators are established, and their finite sample behavior is investigated by means of Monte Carlo simulation studies. Two illustrations using real datasets are also given.

Keywords: imputation, missing at random, inverse probability weighting, ROC surface analysis

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Authors: Ilija Kelepurovski, Stefan Lazorovski, Pece Petkovski, Marian Anakievski, Svetlana Petkovska

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Introduction: Benign prostatic hyperplasia (BPH) is a common condition that affects men over the age of 50 and is characterized by an enlarged prostate gland that can cause lower urinary tract symptoms (LUTS). Uroflowmetry and cystoscopy are two commonly used diagnostic tests to evaluate LUTS and diagnose BPH. While both tests can be useful, there is a risk of overusing cystoscopy and underusing uroflowmetry in the evaluation of LUTS. The aim of this study was to compare the use of uroflowmetry and cystoscopy in a prospective cohort of 100 patients with suspected BPH or other urinary tract conditions and to assess the diagnostic yield of each test. Materials and Methods: This was a prospective study of 100 male patients over the age of 50 with suspected BPH or other urinary tract conditions who underwent uroflowmetry and cystoscopy for the evaluation of LUTS at a single tertiary care center. Inclusion criteria included male patients over the age of 50 with suspected BPH or other urinary tract conditions, while exclusion criteria included previous urethral or bladder surgery, active urinary tract infection, and significant comorbidities. The primary outcome of the study was the frequency of cystoscopy in the evaluation of LUTS, and the secondary outcome was the diagnostic yield of each test. Results: Of the 100 patients included in the study, 86 (86%) were diagnosed with BPH and 14 (14%) had other urinary tract conditions. The mean age of the study population was 67 years. Uroflowmetry was performed on all 100 patients, while cystoscopy was performed on 70 (70%) of the patients. The diagnostic yield of uroflowmetry was high, with a clear diagnosis made in 92 (92%) of the patients. The diagnostic yield of cystoscopy was also high, with a clear diagnosis made in 63 (90%) of the patients who underwent the procedure. There was no statistically significant difference in the diagnostic yield of uroflowmetry and cystoscopy (p = 0.20). Discussion: Our study found that uroflowmetry is an effective and well-tolerated diagnostic tool for evaluating LUTS and diagnosing BPH, with a high diagnostic yield and low risk of complications. Cystoscopy is also a useful diagnostic tool, but it is more invasive and carries a small risk of complications such as bleeding or urinary tract infection. Both tests had a high diagnostic yield, suggesting that either test can provide useful information in the evaluation of LUTS. However, the fact that 70% of the study population underwent cystoscopy raises concerns about the potential overuse of this test in the evaluation of LUTS. This is especially relevant given the focus on patient-centered care and the need to minimize unnecessary or invasive procedures. Our findings underscore the importance of considering the clinical context and using evidence-based guidelines. Conclusion: In this prospective study of 100 patients with suspected BPH or other urinary tract conditions, we found that uroflowmetry and cystoscopy were both valuable diagnostic tools for the evaluation of LUTS. However, the potential overuse of cystoscopy in this population warrants further investigation and highlights the need for careful consideration of the optimal use of diagnostic tests in the evaluation of LUTS and the diagnosis of BPH. Further research is needed to better understand the relative roles of uroflowmetry and cystoscopy in the diagnostic workup of patients with LUTS, and to develop evidence-based guidelines for their appropriate use.

Keywords: uroflowmetry, cystoscopy, LUTS, BPH

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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: Lubabatu Sada Sodangu

Abstract:

Malaria is a major cause of death in tropical and subtropical nations. Malaria cases are continually rising as a result of a number of factors, despite the fact that the condition is now treatable using effective methods. In this situation, quick and effective diagnostic methods are essential for the management and control of malaria. Malaria diagnosis using conventional methods is still troublesome, hence new technologies have been created and implemented to get around the drawbacks. The review describes the currently known malaria diagnostic techniques, their strengths and shortcomings.

Keywords: malaria, technique, diagnosis, Africa

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Authors: Lubabatu Sada Sodangi

Abstract:

Malaria is a major cause of death in tropical and subtropical nations. Malaria cases are continually rising as a result of a number of factors, despite the fact that the condition is now treatable using effective methods. In this situation, quick and effective diagnostic methods are essential for the management and control of malaria. Malaria diagnosis using conventional methods is still troublesome; hence, new technologies have been created and implemented to get around the drawbacks. The review describes the currently known malaria diagnostic techniques, their strengths, and shortcomings.

Keywords: malaria, technique, diagnosis, Africa

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Authors: D. Diaz, C. Guevara, P. Rey

Abstract:

This paper contains information about designing, shielding and protocols building in order to avoid ionizing radiation in X-Rays imaging areas as generated by X-Ray, mammography equipment, computed tomography equipment and digital subtraction angiography equipment, according to global standards. Furthermore, tools and elements about infrastructure to improve protection over patients, physicians and staff involved in a diagnostic imaging area are presented. In addition, technical parameters about each machine and the architecture designs and maps are described.

Keywords: imaging area, X-ray, shielding, dose

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

Abstract:

This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: G. E. Okungbowa, H. O. Adams, S. E. Eze

Abstract:

This study is on the estimation of adult patient doses for Chest X-ray diagnostic examinations of new admitted undergraduate students attending a tertiary institution health centre as part of their routine clearance and check up on admitted into the institution. A total of 531 newly admitted undergraduate students were recruited for this survey in the first quarter of 2016 (January to March, 2016). CALDOSE_X 5.0 software was used to compute the Entrance Surface Dose (ESD) and Effective Dose (ED); while the Statistical Package for Social Sciences (SPSS) version 21.0 was used to carry out the statistical analyses. The basic patients' data and exposure parameters required for the software are age, sex, examination type, projection posture, tube potential and current-time product. The mean Entrance Surface Dose and Effective Doses of the undergraduate students were calculated using the software, and the values were compared with existing literature and internationally established diagnostic reference levels. The mean ESD calculated is 0.29 mGy, and the mean effective dose is 0.04 mSv. The values of ESD and ED obtained are below the internationally established diagnostic reference levels, which could be attributed to good radiographic techniques employed during the chest X-ray procedure for these students.

Keywords: x-ray, dose, examination, chest

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Authors: Adedeji W. Oyediran, Yekini N. Asafe

Abstract:

Mechatronics is synergistic integration of mechanical engineering, with electronics and intelligent computer control in the design and manufacturing of industrial products and processes. Automobile (auto car, motor car or car is a wheeled motor vehicle used for transporting passengers, which also carries its own engine or motor) is a mechatronic system which served as major means of transportation around the world. Virtually all community has a need for automobile. This makes automobile issues as related to diagnostic and repair interesting to all communities. Consequent to the diversification of skill in diagnosing automobile faults and approaches in solving some problems and innovation in automobile industry. It is appropriate to say that repair and diagnostic of automobile will be better enhanced if community has opportunity of sharing knowledge and idea globally. This paper discussed the desirable elements in automobile as mechatronics system and present conceptual framework of virtual community model for automobile users.

Keywords: automobile, automobile users, knowledge sharing, mechatronics system, virtual community

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