Artificial intelligence-aided optical biopsy improves the diagnosis of esophageal squamous neoplasm

• Published in: World journal of gastroenterology : WJG Vol. 31; no. 13; p. 104370
• Main Authors: Ma, Tian, Liu, Guan-Qun, Guo, Jing, Ji, Rui, Shao, Xue-Jun, Li, Yan-Qing, Li, Zhen, Zuo, Xiu-Li
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 07.04.2025
• Subjects: Aged; Artificial Intelligence; Biopsy - methods; Diagnosis, Computer-Assisted - methods; Early Detection of Cancer - methods; Esophageal Neoplasms - diagnosis; Esophageal Neoplasms - diagnostic imaging; Esophageal Neoplasms - pathology; Esophageal Squamous Cell Carcinoma - diagnosis; Esophageal Squamous Cell Carcinoma - diagnostic imaging; Esophageal Squamous Cell Carcinoma - pathology; Esophagoscopy - methods; Esophagus - diagnostic imaging; Esophagus - pathology; Female; Humans; Male; Microscopy, Confocal - methods; Middle Aged; Prospective Studies; Prospective Study; Computer aided diagnosis; Optical biopsy; Artificial intelligence; Esophageal squamous neoplasm; Probe-based confocal laser endomicroscopy
• ISSN: 1007-9327; 2219-2840; 2219-2840
• DOI: 10.3748/wjg.v31.i13.104370

BACKGROUND Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains challenging. Probe-based confocal laser endomicroscopy (pCLE) enables accurate in vivo histological observation and optical biopsy of ESN. However, interpretation of pCLE images requires histopathological expertise and extensive training. Artificial intelligence (AI) has been widely applied in digestive endoscopy; however, AI for pCLE diagnosis of ESN has not been reported. AIM To develop a pCLE computer-aided diagnostic system for ESN and assess its diagnostic performance and assistant efficiency for nonexpert endoscopists. METHODS The intelligent confocal laser endomicroscopy (iCLE) system consists of image recognition (based on inception-ResNet V2), video diagnosis, and quality judgment modules. This system was developed using pCLE images and videos and evaluated through image and prospective video recognition tests. Patients between June 2020 and January 2023 were prospectively enrolled. Expert and non-expert endoscopists and the iCLE independently performed diagnoses for pCLE videos, with histopathology as the gold standard. Thereafter, the non-expert endoscopists performed a second assessment with iCLE assistance. RESULTS A total of 25056 images from 2803 patients were selected for iCLE training and validation. Another 2442 images from 226 patients were used for testing. iCLE achieved a high accuracy of 98.3%, sensitivity of 95.3% and specificity of 98.8% for diagnosing ESN images. A total of 2581 patients underwent upper gastrointestinal pCLE examination and were prospectively screened; 54 patients with suspected ESN were enrolled. Overall, 187 videos from 67 lesions were assessed by iCLE, three nonexpert and three expert endoscopists. iCLE achieved a high accuracy, sensitivity and specificity of 90.9%, 92.0%, and 90.2%, respectively. Compared to experts, iCLE showed significantly higher sensitivity (92.0% vs 80.4%; P < 0.001) and negative predictive value (94.4% vs 87.7%; P = 0.003). With iCLE assistance, nonexpert endoscopists showed significant improvements in accuracy (from 83.6% to 88.6%) and sensitivity (from 76.0% to 89.8%). CONCLUSION iCLE system demonstrated high diagnostic performance for ESN. It can assist nonexpert endoscopists in improving the diagnostic efficiency of pCLE for ESN and has the potential for reducing unnecessary biopsies.