AI-enhanced thyroid detection using YOLO to empower healthcare professionals

Abstract

In this research, we present an approach to improve thyroid diagnosis and provide experts in medical fields by leveraging artificial intelligence (AI) technology. Our research aims to speed up the diagnostic process, particularly in emergency situations where specialized medical personnel are not always available. Our technology includes a user-friendly interface that enables healthcare professionals to efficiently submit ultrasound scans of their patients' thyroids. The images are then analyzed using integrated AI algorithms based on the YOLOv5 object detection model. In real-time, these algorithms efficiently and rapidly identify and localize thyroid areas. With an overall precision rate of 0.879 and a recall rate of 0.862, the results show a high degree of precision and recall. The average precision (mAP50) is 0.892, confirming the accuracy of localization. The system offers invaluable support to healthcare professionals with comprehensive reports and annotations, facilitating informed decision-making and patient care. The combination of AI technologies presented in this study yields promising results, highlighting their potential to transform thyroid detection and professional assistance in the medical field.

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