Real-Time Joint Bleeding Detection and Clinical Decision Support System for Hemophilia Patient

Abstract

Hemophilia is a rare genetic bleeding disorder where blood does not clot properly due to lack of clotting factors, Factor VIII in Hemophilia A and Factor IX in Hemophilia B, which can result in spontaneous and recurrent internal bleeding, particularly into joint spaces such as knees, ankles, and elbows. If not treated quickly, joint bleeding can lead to pain and swelling, and eventually joint damage. In Bangladesh, most patients experience prohibitive delay in clinical evaluation, including travel to referral centers. This paper presents a Novel Real-Time Joint Bleeding Detection and Clinical Decision Support Tool for the Remote Assessment of Hemophilia-A patient's joint. Through the Regional Youth Committee, a dataset of more than 2000 images of joint bleeding has been collected across the country with the approval of Hemophilia Society of Bangladesh. After Augmentation in Roboflow the image increase to 5000 Certified hemophilia specialists categorized the images into five classes: severe, moderate, mild, fixed joint, and no bleeding. Different deep learning models including CNN with Xception and hybrid model ViTForImageClassification with DenseNet121 were trained. The highest validation accuracy attained was 81.82% with unedited images, underscoring the significance of background context in the medical image classification process. The bestperforming model was saved as an .h5 file and this was used to develop a web application with Flask. Using this system, patients are able to receive on-the-go assessments and treatment recommendations from doctors in real-time, enabling timely action and reducing.