Human understandable thyroid ultrasound imaging AI report system-A bridge between AI and clinicians

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.

Automated summary

Artificial intelligence (AI) enables accurate diagnosis of thyroid cancer but lacks explanation. This study developed TiNet, a human understandable AI report system for thyroid cancer prediction. TiNet uses deep learning to extract features of thyroid nodules and provides quantitative explanations. Compared to clinical reports, TiNet reports were significantly easier to understand and can enhance collaboration between AI and clinicians.