Human understandable thyroid ultrasound imaging AI report system — A bridge between AI and clinicians
Siqiong Yao, Pengcheng Shen, Tongwei Dai, Fang Dai, Yun Wang, Weituo Zhang, Hui Lü
Abstract
Artificial intelligence (AI) enables accurate diagnosis of thyroid cancer; however, the lack of explanation limits its application. In this study, we collected 10,021 ultrasound images from 8,079 patients across four independent institutions to develop and validate a human understandable AI report system named TiNet for thyroid cancer prediction. TiNet can extract thyroid nodule features such as texture, margin, echogenicity, shape, and location using a deep learning method conforming to the clinical diagnosis standard. Moreover, it offers excellent prediction performance (AUC 0.88) and provides quantitative explanations for the predictions. We conducted a reverse cognitive test in which clinicians matched the correct ultrasound images according to TiNet and clinical reports. The results indicated that TiNet reports (87.1% accuracy) were significantly easier to understand than clinical reports (81.6% accuracy; p < 0.001). TiNet can serve as a bridge between AI-based diagnosis and clinicians, enhancing human-AI cooperative medical decision-making.