Objective: To explore the value of artificial intelligence (AI) based on key anatomical structure in thyroid ultrasound standard plane (TUSP) recognition. Methods: Adult thyroid ultrasound imageswere selected as the research objects

including 8978 TUSP images in standard set and 1916 images in experimental set. The images in standard set were further divided into training set and verifying set for training and verifying the ability of AI in recognizing and classifying TUSP. The images in experimental set including TUSP images and non-standard plane (N-SP) images. Taking the classification results of ultrasound experts as the standard

the differences among AI

junior doctors and intermediate doctors in the recognition and classification on experimental set were compared and analyzed.Results: On experimental set

The classification accuracy of AI for eight TUSP sections ranged were from 94.7% to 99.9%

and the classification accuracy of AI for N-SP was 93.8%.AI was superior to junior doctors in the recognition efficiency of TUSP and N-SP (P<0.05).The ability of AI to recognize longitudinal plane of the left lobe of thyroid (LPLT) and N-SP sections was not significantly different from that of intermediate doctors (P=0.468

P=0.816). And AI performed slightly better than intermediate doctors on other sections of TUSP (P<0.05).The classification efficiency of AI was significantly better than the that of artificial classification (P<0.05).Conclusion :AI based on key anatomical structure detection has high accuracy and efficiency for TUSP and N-SP recognition and classification

which can be used as an auxiliary method for thyroid ultrasound image quality control and specialized training.