Machine Learning for Enhanced Diagnosis of Endometriosis: Challenges and Opportunities
- سال انتشار: 1403
- محل انتشار: چهارمین همایش بین المللی و سیزدهمین همایش ملی بیوانفورماتیک
- کد COI اختصاصی: IBIS13_145
- زبان مقاله: انگلیسی
- تعداد مشاهده: 52
نویسندگان
Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Iran
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
چکیده
Endometriosis is a chronic and complex condition that significantly affects the quality of life for over ۱۹۰ million women globally. Delayed diagnosis can lead to serious complications such as infertility and chronic pain. This study explores the challenges and opportunities of employing machine learning and deep learning models to enhance the diagnosis of endometriosis and improve healthcare outcomes. Key challenges include the variability and complexity of clinical symptoms, a lack of high-quality data, the necessity for specialized knowledge in algorithm implementation, and the absence of standardized evaluation metrics for comparing models (Ellis et al., ۲۰۲۲). Artificial intelligence can potentially reveal hidden patterns in clinical and imaging data. At the same time, machine learning algorithms can facilitate the development of non-invasive screening tools and generate more accurate predictions of treatment outcomes. These advancements are likely to improve diagnostic accuracy and reduce healthcare costs. The study examines various input data, including clinical information, imaging data (MRI and laparoscopic images), and laboratory results (biochemical markers such as CA۱۲۵ and VEGF۱) (Goldstein & Cohen, ۲۰۲۳). It evaluates various models, including deep learning models like ResNet۵۰ and classical models, including decision trees, random forests, logistic regression, and AdaBoost (Zhang et al., ۲۰۲۳). The findings demonstrate that the AdaBoost model performs best in diagnosing endometriosis, achieving an accuracy of ۹۴% and a sensitivity of ۹۳% (Balica et al., ۲۰۲۳). In comparison, the ResNet۵۰ model achieves an accuracy of ۹۱% and a sensitivity of ۸۲% (Visalaxi & Muthu, ۲۰۲۱). To further enhance research in this field, it is recommended that datasets be expanded to incorporate more diverse patient populations and that models be compared across various conditions and similar contexts. Furthermore, clear guidelines for applying artificial intelligence in diagnosing and treating endometriosis are essential. Despite existing challenges, machine learning and deep learning use in analyzing and predicting endometriosis presents significant potential, necessitating ongoing research to refine model performance and increase confidence in their clinical applications.کلیدواژه ها
endometriosis, machine learning, deep learning, diagnosis, artificial intelligence, healthcareاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.