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Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video) - 21/03/23

Doi : 10.1016/j.gie.2022.12.003 
Xiang-Lei Yuan, MD 1, , Xian-Hui Zeng, MD 1, , Wei Liu, MD 1, Yi Mou, MD 1, Wan-Hong Zhang, MM 2, Zheng-Duan Zhou, MM 3, Xin Chen, MB 4, Yan-Xing Hu, PhD 5, Bing Hu, MD 1,
1 Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan, China 
2 Department of Gastroenterology, Cangxi People’s Hospital, Guangyuan, Sichuan, China 
3 Department of Gastroenterology, Zigong Fourth People’s Hospital, Zigong, Sichuan, China 
4 The First People's Hospital of Shuangliu District, Chengdu, Sichuan, China 
5 Xiamen Innovision Medical Technology Co, Ltd, Xiamen, China 

Reprint requests: Bing Hu, MD, West China Hospital, Sichuan University, Department of Gastroenterology, No. 37 Guo Xue Alley, Wu Hou District, Chengdu, Sichuan, China 610041.West China HospitalSichuan UniversityDepartment of GastroenterologyNo. 37 Guo Xue AlleyWu Hou DistrictChengduSichuan610041China

Abstract

Background and Aims

Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI.

Methods

Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated.

Results

The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents.

Conclusions

The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

Il testo completo di questo articolo è disponibile in PDF.

Abbreviations : AI, ER, ESCC, FN, FP, mIoU, NBI, NPV, PPV, TP, WCHSCU


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 DISCLOSURE: All authors disclosed no financial relationships. Research support for this study (B. Hu) was provided by the National Natural Science Foundation of China (grant no. 82170675) and 1·3·5 project for disciplines of excellence, West China Hospital, Sichuan University (grant no. ZYJC21011).


© 2023  American Society for Gastrointestinal Endoscopy. Pubblicato da Elsevier Masson SAS. Tutti i diritti riservati.
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