China Journal of Oral and Maxillofacial Surgery ›› 2026, Vol. 24 ›› Issue (4): 406-412.doi: 10.19438/j.cjoms.2026.04.014

• Dental Education • Previous Articles     Next Articles

Investigation on learning application and cognitive evaluation of AI segmentation of panoramic images of mandibular third molars among standardized training residents in stomatology

Zhou Qin1, Li Kexuan1, Sun Yun2, Fang Xiao1, Wang Xinbei3, Ji Changkai4, Du Guanhuan5   

  1. 1. Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine;College of Stomatology, Shanghai Jiao Tong University;National Center for Stomatology;National Clinical Research Center for Oral Diseases;Shanghai Key Laboratory of Stomatology;Shanghai Research Institute of Stomatology. Shanghai 200011;
    2. College of Stomatology, Shanghai Jiao Tong University. Shanghai 200011;
    3. Standardized Training Office, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. Shanghai 200011;
    4. School of Automation and Intelligent Sensing, Shanghai Jiao Tong University. Shanghai 200240;
    5. Department of Oral Mucosal Diseases, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine;College of Stomatology, Shanghai Jiao Tong University;National Center for Stomatology;National Clinical Research Center for Oral Diseases;Shanghai Key Laboratory of Stomatology;Shanghai Research Institute of Stomatology. Shanghai 200011, China
  • Received:2026-01-28 Revised:2026-04-08 Published:2026-08-05

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Abstract: PURPOSE: To analyze the learning effect and feedback of standardized training residents in stomatology on artificial intelligence (AI) segmentation of panoramic images of mandibular third molars, so as to provide evidence for the rational application of AI in teaching and the improvement of learning efficiency and teaching quality. METHODS: An anonymous online questionnaire using Wenjuanxing was distributed to stomatology (dental) residents receiving standardized training at Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. The questionnaire included general information, case-based image evaluation (4 sets of original panoramic radiographs and 4 sets of AI-segmented images of mandibular third molars), as well as participants' cognition of AI, application demands and teaching expectations. The residents' image interpretation results were compared with the expert panel criteria. RESULTS: A total of 65 valid questionnaires were collected. In the interpretation of the positional relationship between tooth roots and the inferior alveolar nerve canal, the AI segmentation group showed significantly higher consistency with the expert criteria than the panoramic radiograph group (P<0.05). No significant difference was found in the consistency of self-rated extraction difficulty with expert standards between the two groups (P>0.05). A total of 95.38% of the residents were willing to have more access to AI in the future, and over 95% of the residents supported the application and development of AI in dental education. CONCLUSIONS: AI segmentation of images can significantly enhance the anatomical visualization and learning experience for the study of third mandibular molars, with high acceptance among learners. It is recommended that AI be integrated with traditional teaching under the guidance of instructors to establish an interactive teaching model of "AI + instructor + student", and to expand into personalized learning and virtual simulation teaching.

Key words: Artificial intelligence, Mandibular third molar, Dental teaching, Cognitive survey

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