Bone Fracture Detection System

Abstract

The prompt and precise identification of bone fractures is crucial for patient care and treatment results in the field of medical diagnostics. Conventional techniques frequently depend on qualified radiologists to interpret radiographic images, which is a subjective and variable procedure. Artificial intelligence (AI)-powered automated bone fracture detection systems (BFDS) have surfaced as viable solutions to these problems. These technologies help identify and classify fractures with high precision by using sophisticated image processing algorithms and machine learning models to quickly and correctly evaluate radiography pictures. An overview of the design, development, and assessment of a BFDS intended to improve diagnostic precision, shorten interpretation times, and assist medical professionals in making defensible judgments is provided in this study. Pre-processing methods for images, feature extraction strategies, and classification algorithms designed to identify different kinds of bone fractures are important elements. Additionally, iterative learning from annotated datasets enables continual improvement through the incorporation of deep learning frameworks, guaranteeing strong performance across a range of patient demographics and fracture patterns. Our BFDS shows great promise in enhancing clinical workflows, enhancing patient care results, and developing the field of musculoskeletal radiology via thorough validation and comparison with traditional diagnostic techniques. Healthcare professionals may improve fracture detection's speed, dependability, and scalability by utilizing AI-driven diagnostics, which will eventually improve patient happiness and healthcare delivery