شخصی‌سازی فرایند آموزش رزیدنت‌های رادیولوژی با استفاده از یادگیری عمیق و استخراج تعاملی مدل خطاهای تشخیصی آنان

چکیده مقاله :

تصاویر رادیولوژی از ابزارهای رایج و پرکاربرد تشخیصی هستند که به طور گسترده توسط پزشکان برای تشخیص و نظارت بر روند درمان بیماری‌های مختلف استفاده می‌شوند. تفسیر صحیح این تصاویر نیازمند آموزش رزیدنت‌ها و کارشناسان متخصص با دقت تشخیصی بالا است. در این پژوهش، یک سامانه هوشمند، مبتنی بر یادگیری ماشین، طراحی، پیاده‌سازی و ارزیابی می‌شود؛ که با استفاده از استخراج تعاملی مدل خطاهای تشخیصی مثبتِ کاذب و منفیِ کاذب رزیدنت‌های تحت آموزش، دوره آموزش آنها را بصورت شخصی‌سازی‌شده انجام دهد. راهکار پیشنهادی در قالب یک سامانه برخطِ آموزش شخصی‌سازی‌شده طراحی می‌شود که طی فرایندی تکرارشونده، رزیدنت‌ها را در آزمون‌های تطبیقی طراحی‌شده توسط الگوریتم انتخاب سؤالات، مبتنی بر عملکرد هر رزیدنت، ارزیابی کرده و مدل خطاهای تشخیصی آنها را استخراج می‌کند. در این سامانه، با بهره‌گیری از یک مدل مبتنی بر یادگیری عمیق، ویژگی درجه سختی هر یک از تصاویر رادیوگرافیِ آموزشی استخراج می‌شود. سپس با رعایت پراکندگی مناسب انواع کلاس‌های بیماری و تنوع درجه سختی تصاویر، سؤالات آزمون‌های رزیدنت‌ها طراحی می‌شود. در آزمون‌های بعدی، با تحلیل خطاهای هر فرد در آزمون‌های قبلی و الگویابی آن‌ها، وزن ارزیابی هر تصویر مطابق با خطاهای تشخیصی پیشین هر فرد در تصاویر مشابه قبلی، مشخص می‌شود. در طی این فرایند تکرارشونده، دوره آموزش هر رزیدنت با در نظر گرفتن نقاط ضعف و قوت او در آزمون‌های قبلی طی می‌شود. نتایج آزمایش‌های انجام شده، نشان‌دهنده بهبود عملکرد کاربران سامانه، هم از نظر دقت تشخیص آنها در هر کلاس با هر درجه سختی تصویر، و هم از نظر کاهش نرخ خطاهای مثبت کاذب و منفی کاذب آنها است. 

چکیده انگلیسی :

Radiological imaging is an essential diagnostic tool widely utilized by physicians for the diagnosis and monitoring of various medical conditions. Accurate interpretation of these images demands training of specialized residents and medical experts to ensure high diagnostic precision. This study presents an intelligent system that leverages machine learning to provide a personalized radiology residency training process through the interactive extraction of false-positive and false-negative diagnostic error patterns. The proposed approach is designed as an online personalized training platform that employs an iterative process to evaluate residents through adaptive tests generated by a question-selection algorithm based on each resident's performance, with the aim of extracting diagnostic error models of them independently. The proposed method employs a deep learning-based model to evaluate the difficulty level of educational radiographic images. Test questions for residents are then systematically designed to ensure an optimal distribution of disease classes and a diverse range of image difficulty levels. In subsequent tests, by analyzing each resident's mistakes in previous tests and finding their error patterns, the evaluation weight of each test image is determined according to each resident's mistakes in previous similar images. During this iterative process, each resident's training period is conducted taking into account their strengths and weaknesses in previous tests. Experimental results indicate significant improvements in diagnostic accuracy of residents across different disease classes and various difficulty levels, accompanied by remarkable reductions in false-positive and false-negative error rates. 

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