ArticleGastrointestinal endoscopy2021
Automated software-assisted diagnosis of esophageal squamous cell neoplasia using high-resolution microendoscopy.
Article in Gastrointestinal endoscopy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 18 citations in OpenAlex.
- A High-Resolution Microendoscope Improves Esophageal Cancer Screening and Surveillance: Implications for Underserved Global Settings Based on an International Randomized Controlled Trial.Gastroenterology · 2025Trial
- Carcinoma of Esophagus.Journal of gastroenterology and hepatology · 2025Review
- Deep-learning-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics.Journal of biomedical optics · 2024Article
- Utility of a Low-Cost 3-D Printed Microscope for Evaluating Esophageal Biopsies.Annals of 3D printed medicine · 2024Article
- Microendoscopy in vivo for the pathological diagnosis of cervical precancerous lesions and early cervical cancer.Infectious agents and cancer · 2023Article
- The Impact of Artificial Intelligence on Health Equity in Oncology: Scoping Review.Journal of medical Internet research · 2022Article
- Artificial intelligence in gastroenterology: A state-of-the-art review.World journal of gastroenterology · 2021Review
- Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases.Diagnostics (Basel, Switzerland) · 2021Review
- Systematic identification of key functional modules and genes in esophageal cancer.Cancer cell international · 2021Article
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Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
aimsHigh-resolution microendoscopy (HRME) is an optical biopsy technology that provides subcellular imaging of esophageal mucosa but requires expert interpretation of these histopathology-like images. We compared endoscopists with an automated software algorithm for detection of esophageal squamous cell neoplasia (ESCN) and evaluated the endoscopists' accuracy with and without input from the software algorithm.
methodsThirteen endoscopists (6 experts, 7 novices) were trained and tested on 218 post-hoc HRME images from 130 consecutive patients undergoing ESCN screening/surveillance. The automated software algorithm interpreted all images as neoplastic (high-grade dysplasia, ESCN) or non-neoplastic. All endoscopists provided their interpretation (neoplastic or non-neoplastic) and confidence level (high or low) without and with knowledge of the software overlay highlighting abnormal nuclei and software interpretation. The criterion standard was histopathology consensus diagnosis by 2 pathologists.
resultsThe endoscopists had a higher mean sensitivity (84.3%, standard deviation [SD] 8.0% vs 76.3%, P = .004), lower specificity (75.0%, SD 5.2% vs 85.3%, P < .001) but no significant difference in accuracy (81.1%, SD 5.2% vs 79.4%, P = .26) of ESCN detection compared with the automated software algorithm. With knowledge of the software algorithm, the specificity of the endoscopists increased significantly (75.0% to 80.1%, P = .002) but not the sensitivity (84.3% to 84.8%, P = .75) or accuracy (81.1% to 83.1%, P = .13). The increase in specificity was among novices (P = .008) but not experts (P = .11).
conclusionsThe software algorithm had lower sensitivity but higher specificity for ESCN detection than endoscopists. Using computer-assisted diagnosis, the endoscopists maintained high sensitivity while increasing their specificity and accuracy compared with their initial diagnosis. Automated HRME interpretation would facilitate widespread usage in resource-poor areas where this portable, low-cost technology is needed.
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