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[Application of an artificial intelligence-assisted diagnostic system for lymph nodes in head and neck imaging
Gongxin Yang1, Xiaoqing Dai, Jingbo Wang
1Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. Shanghai 200011, China.
Purpose:
To explore the feasibility and effectiveness of a CT-based artificial intelligence (AI) assisted diagnostic system for cervical lymph node recognition in oral and maxillofacial head and neck imaging education.
Methods:
A total of 33 students from College of Stomatology, Shanghai Jiao Tong University were selected as study participants. The students were randomly divided into group A and B, and tasked with reviewing the anatomy of cervical lymph nodes in contrast-enhanced CT images of two patients. Group A performed manual annotation, while group B used AI-assisted annotation, and then the groups crossed over to annotate the next patient. The annotation results of senior radiologists were used as the gold standard to compare the recall rate and accuracy between the manual annotation group and the AI-assisted annotation group. Additionally, a teaching quality questionnaire was used to assess students' learning experiences, focusing on the effectiveness of AI in enhancing engagement and comprehension.
Results:
Statistical analysis showed that the average recall rate for manual annotation was 31.0%, whereas the recall rate increased to 63.9% with AI assistance. All students completed the teaching questionnaire, and 93.9% of them believed that the AI-assisted diagnostic system stimulated their interest in learning, deepened their understanding of theoretical knowledge, and facilitated their study of head and neck imaging anatomy. Furthermore, students reported that AI assistance helped in reducing cognitive load, allowing them to focus more on understanding the complex anatomical relationships rather than manual tasks.
Conclusions:
The AI-assisted diagnostic system for cervical lymph nodes has significant educational advantages in oral and maxillofacial head and neck imaging teaching. It effectively improves medical students' understanding of head and neck imaging anatomy, reduces cognitive load, and enhances learning outcomes. This study demonstrates the high application value of integrating AI into radiology education, providing a model for future educational innovations and reforms.
