Machine learning-based segmentation of images to diagnose the orthopedic diseases and to guide the orthopedic surgeries

The last few years have seen a significant adoption of automated segmentation techniques by medical professionals for precise clinical decision-making. Automated segmentation techniques can provide any medical expert accurate information which can help in effective decision-making for surgeries and for recommending treatment. The proposed research study aims to evaluate the effectiveness of medical image segmentation algorithms along with biomedical sensors for the diagnosis and treatment of orthopedic diseases. The study collects data from MRI and scanned images, which is then used to develop image segmentation methods using machine learning techniques. The study divides the data into two groups, a control group that uses X-ray images with traditional methods and an experimental group that uses MRI or CT scanning methods along with ML techniques for diagnosis of orthopedic diseases. The proposed method is evaluated using standard performance metrics such as VAS score, Ramsay score, disease classification treatment effect, and observation indexes. The proposed research uses ML techniques such as Fast KNN and Mask R-CNN for the segmentation of medical images to diagnose and treat orthopedic diseases. The potential benefits of using automated segmentation techniques in orthopedics, including more accurate diagnoses, improved surgical precision and reduced risk of complications are revealed. The results of the study suggest that the proposed innovative non-invasive medical treatment methods can enhance the accuracy of orthopedic treatments and increase the safety of orthopedic disease treatment by providing high precision in segmented images. Automated segmentation techniques can provide medical experts with accurate information for effective decision-making in surgeries and treatment recommendations.