The Application of ML in Medical Image Analysis Research

The development of Machine Learning (ML) and Deep Learning (DL) has brought revolutionary changes to medical image analysis and provided a new solution for diagnosing and treating all categories of disease. This paper is going to review the applications of ML and DL techniques in the field of medical image analysis such as X-rays, CT scans, MRI, and DEXA, in particular, their role in the diagnosis of orthopedic diseases. With the progress of the AIs processing power and the universality of open-source platforms, MLs algorithms have developed rapidly, it has significantly boosted the classification, segmentation and lesion detection of the medical image. However, there also exist challenges like data quality, model interpretability, and class imbalances. The paper will summarize and analyze the present situation of ML and DL in medical images and emphasize their advantages, limitations, and future development potential. Studying recent literature and experiment reports clarifies that solving these limitations is crucial for the clinical adoption and usefulness of ML and DL. Finally, this paper calls for more research to improve the accuracy, efficiency, and safety of using ML and DL in medical images, enabling these technologies to truly enhance patient outcomes.