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Application and validation of AI-assisted 3D-Printed gastroduodenal anatomical variation models in specialized
Hao Li1, Shaohua Sun1, Xiang Mao1
1Department of Hepatobiliary and Pancreatic Surgery, Sinopharm Dongfeng General Hospital Affiliated to Hubei University of Medicine, Shiyan, Hubei, China.
Objective:
To investigate the application effectiveness of AI-assisted 3D printing technology for modeling anatomically abnormal upper gastrointestinal-biliary systems in ERCP specialized nurse training.
Methods:
A total of 179 nursing staff were randomly assigned to an experimental group (n = 84) receiving AI-assisted 3D-printed biliary models or a control group (n = 95) receiving traditional instruction. Post-training outcomes included theoretical and practical examinations, a five-point Likert questionnaire (covering teaching model, content, learning interest, operational skills, and problem-solving abilities), and expert evaluations. Statistical analyses compared the two groups.
Results:
The experimental group showed significantly higher theoretical and practical assessment scores than the control group (both p < 0.05). In the teaching effectiveness feedback, the experimental group scored significantly higher in teaching model, stimulating learning interest and enhancing operational skills (all p < 0.05). ERCP physician feedback also revealed significantly higher satisfaction in cooperation awareness, procedural accuracy, and teamwork awareness for the experimental group (all p < 0.05).
Conclusion:
AI-assisted 3D printing technology demonstrated preliminary advantages in ERCP nurse training, but its broader application requires validation in larger, multi-center trials with long-term outcomes.
