The Role of Artificial Intelligence in Optimizing Ultrasound Diagnosis of Adenomyosis and Pelvic Endometriosis

The application of artificial intelligence (AI) in medical diagnostics opens new perspectives for enhancing the accuracy, speed, and efficiency of examinations in patients with gynecological pathology. Ultrasound diagnosis of adenomyosis and pelvic endometriosis remains a challenging task due to the variability of clinical manifestations, indistinct boundaries of affected tissues, and individual anatomical features. The aim of this study is a comprehensive analysis of the role of AI algorithms in optimizing the ultrasound examination process, improving the accuracy of diagnostic conclusions, and reducing the likelihood of diagnostic errors. Based on current scientific research and international clinical guidelines, methods of automated ultrasound image processing, the use of machine learning algorithms for identifying pathological changes in the endometrium and myometrium, and the integration of AI solutions into the workflow of diagnostic physicians are discussed. The prospects of applying deep learning models for automatic delineation of lesion areas, predicting disease severity, and monitoring treatment effectiveness are also considered. Special attention is given to the clinical advantages of AI implementation: reducing examination time, minimizing subjective operator influence, increasing result reproducibility, and enabling early detection of pathologies, which significantly impacts individualized therapy planning. The economic efficiency of AI integration is also highlighted, as automation of the examination process allows for optimized personnel time and reduced costs for repeat studies. It is concluded that an effective combination of physician expertise and intelligent algorithms enables the creation of a highly accurate and reliable system for ultrasound diagnosis of adenomyosis and endometriosis, meeting modern requirements of personalized medicine. Implementing AI in routine examinations of gynecological patients represents an important step toward improving the quality of medical care and optimizing clinical resources.

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