Search results for: x-rays

Authors: Ronak Mishra, Sachin Kale

Abstract:

Background: The clinical outcome of a new fixation device (femoral neck system, FNS) for femoral neck fractures is not described properly. The main purpose of this study was to evaluate the functional outcome of the patients of femoral neck fractures treated with FNS. Methods: A retrospective study was done among patients aged 60 years or less. On the basis of inclusion and exclusion criteria a final sample size of 30 was considered. Blood loss, type of fracture internal fixation, and length of clinical follow-up were all acquired from patient records. The volume of blood loss was calculated. The mean and standard deviation of continuous variables were reported (with range). Harris Hip score (HHS) And Post op xrays at intervals(6 weeks, 6 months ,12 months ) we used to clinically asses the patient. Results: Out of all 60% were females and 40% were males. The mean age of the patients was. 44.12(+-) years The comparison of functional outcomes of the patients treated with FNS using Harris Hip Score. It showed a highly significant comparison between the patients at post operatively , 6 weeks and 3 months and 12 months . There were no postoperative complications seen among the patients. Conclusion: FNS offers superior biomechanical qualities and greatly improved overall build stability. It allows for a significant reduction in operation time, potentially lowering risks and consequences associated with surgery.

Keywords: FNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Jonathan Gong

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

Procedia PDF Downloads 93

Authors: Adel Edwar Waheeb Louka

Abstract:

Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Robert E. Kenney, Richard B. Aguilar, Efrain Antunez, Gregory Schor-Haskin, Rafael Rey, Catie Falcon, Luis Arce

Abstract:

This study sought to explore the effect of Sarapin injections on chronic knee pain (CKP). Many adults suffer from CKP which is most often attributed to osteoarthritis. Current treatment regimens for CKP involve the use NSAIDS medications, injections with steroids/analgesic, platelet rich plasma injections, or orthopedic surgical interventions. Sarapin is a commercially available homeopathic aqueous extract from the pitcher plant. Studies on the use of Sarapin as a treatment for cervical, thoracic, and lumbosacral facet joint nerve blocks have been performed with mixed results. There is little available evidence on the use of Sarapin in CKP. This study examines the effect of a series of 3 weekly injections of aqueous Sarapin in 95 elderly patients with CKP in a primary care setting. Cano Health, a primary care group, identified 95 successive patients with CKP from its multimodal physiotherapy program for chronic pain. Patients underwent evaluation by a clinician, underwent diagnostic Xrays of the knees, and the treatment plan with three weekly Sarapin injections was discussed. A pain and functional limitation survey (a modified Lower Extremity Functional Scale (mLEFS)) was administered prior to initiating treatment (Entry Survey (ES)). Each patient received an intraarticular injection of 2 cc of aqueous Sarapin with 1cc 1% lidocaine during weeks 1, 2 and 3. The mLEFS was administered again at week 4, one week after the third Sarapin injection (Exit Survey (ExS)). Demographics: Mean Age 62 +/- 9.8; 73% female; 89% Hispanic/Latino; mean time between ES and ExS was 27.5 +/-8.2 days. Survey: The mLEFS was based on a published Lower Extremity Functional Scale and each patient rated their pain or functional limitation from 0 (no difficulty) to 5 (severe difficulty) for 10 questions. Answers were summed and compared. Maximum score for severe difficulty would be 50 points. Results: Mean pain/functional scores: ES was 30.3 +/-12.1 and ExS was 19.5 +/- 12.5. This represents a relative improvement of 35.7% (P<0.00001). A total of 81% (77/95) of the patients showed improvement in symptoms at week four as assessed by the mLEFS. There were 11 patients who reported an increase in their survey scores while 7 patients reported no change. When evaluating the cohort that reported improvement, the ES was 30.9 +/-11.4 and ExS was 16.3 +/-9.8 yielding a 47.2% relative improvement (P<0.00001). Injections were well tolerated, and no adverse events were reported. Conclusions: In this cohort of 95 elderly patients with CKP, treatment with 3 weekly injections of Sarapin significantly improved pain and function as assessed by a mLEFS survey. The majority (81%) of patients responded positively to therapy, 12% had worsening symptoms and 7% reported no change. The use of intraarticular injections of Sarapin for CKP was shown to be an effective modality of treatment. Sarapin’s low cost, tolerability, and ease of use make it an attractive alternative to NSAIDS, steroids, PRP or surgical intervention for this common debilitating condition.

Keywords: Sarapin, intraarticular, chronic knee pain, osteoarthritis

Procedia PDF Downloads 55