Advertisement

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow band imaging

Published:December 09, 2022DOI:https://doi.org/10.1016/j.gie.2022.12.003
      This paper is only available as a PDF. To read, Please Download here.

      Abstract

      Background and Aims

      Although narrow band imaging (NBI) has been 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 four 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 10047 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.

      Conclusion

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

      Acronyms:

      Narrow band imaging (NBI), Esophageal squamous cell carcinoma (ESCC), Artificial intelligence (AI), Endoscopic resection (ER), Deep convolutional neural network (DCNN), White light endoscopy (WLE), West China Hospital of Sichuan University (WCHSCU), Endoscopic submucosal dissection (ESD), Positive predictive value (PPV), Negative predictive value (NPV), Mean intersection over union (mIoU), True positive (TP), False positive (FP), False negative (FN)
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Gastrointestinal Endoscopy
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect