In Gastrointestinal endoscopy ; h5-index 72.0
BACKGROUND AND AIMS : Esophagogastroduodenoscopy (EGD) is essential for gastrointestinal disorders, and reports are pivotal to facilitating post-procedure diagnosis and treatment. Manual report generation lacks sufficient quality and is labor intensive. We first reported and validated an artificial intelligence-based endoscopy automatic reporting system (AI-EARS).
METHODS : The AI-EARS was designed for automatic report generation, including real-time image capturing, diagnosis and textual description. It was developed using multicenter datasets from eight hospitals in China, including 252111 images for training, 62706 images and 950 videos for testing.Twelve endoscopists and 44 endoscopy procedures were consecutively enrolled to evaluate the effect of AI-EARS in a multi-reader multi-case (MRMC) crossover study. The precision and completeness of the reports were compared between endoscopists using AI-EARS and conventional reporting systems.
RESULTS : In video validation, AI-EARS achieved completeness of 98.59% and 99.69% for esophageal and gastric abnormality records, accuracies of 87.99% and 88.85% for esophageal and gastric lesion location records, and 73.14% and 85.24% for diagnosis.Compared to the conventional reporting systems, AI-EARS achieved greater completeness (79.03% vs. 51.86%, p<0.001), accuracy (64.47% vs. 42.81%, p<0.001) of the textual description, and completeness of the photo-documents of landmarks (92.23% vs. 73.69%, p<0.001). The mean reporting time for an individual lesion was significantly reduced (80.13±16.12 vs. 46.47±11.68 seconds, P<0.001) after AI-EARS assistance.
CONCLUSIONS : AI-EARS showed its efficacy in improving the accuracy and completeness of the EGD reports. It might facilitate the generation of complete endoscopy reports and post-endoscopy patient management. (ClinicalTrails.gov, number NCT05479253).
Zhang Lihui, Lu Zihua, Yao Liwen, Dong Zehua, Zhou Wei, He Chunping, Luo Renquan, Zhang Mengjiao, Wang Jing, Li Yanxia, Deng Yunchao, Zhang Chenxia, Li Xun, Shang Renduo, Xu Ming, Wang Junxiao, Zhao Yu, Wu Lianlian, Yu Honggang
2023-Feb-25
Gastrointestinal endoscopy, artificial intelligence, endoscopy reporting, quality standardization
