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.
Abbreviations:
AI (artificial intelligence), ER (endoscopic resection), ESCC (esophageal squamous cell carcinoma), FN (false negative), FP (false positive), mIoU (mean intersection over union), NBI (narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), TP (true positive), WCHSCU (West China Hospital of Sichuan University)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 09, 2022
Accepted:
December 1,
2022
Received:
July 31,
2022
Footnotes
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).
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