Accuracy of polyp characterization by artificial intelligence and endoscopists: a prospective, non-randomized study in a tertiary endoscopy center

Author:

Baumer Sebastian1,Streicher Kilian1,Alqahtani Saleh A.23,Brookman-Amissah Dominic1,Brunner Monika1,Federle Christoph1,Muehlenberg Klaus1,Pfeifer Lukas1,Salzberger Andrea1,Schorr Wolfgang1,Zustin Jozef45,Pech Oliver1

Affiliation:

1. Department of Gastroenterology and Interventional Endoscopy, Krankenhaus Barmherzige Brüder Regensburg, Regensburg, Germany

2. Department of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, United States

3. Liver Transplant Center, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia

4. Private Practice, Histopathology Service Private Practice, Regensburg, Germany

5. Gerhard-Domagk-Institute of Pathology, Universitätsklinikum Münster, Munster, Germany

Abstract

Abstract Background and study aims Artificial intelligence (AI) in gastrointestinal endoscopy is developing very fast. Computer-aided detection of polyps and computer-aided diagnosis (CADx) for polyp characterization are available now. This study was performed to evaluate the diagnostic performance of a new commercially available CADx system in clinical practice. Patients and methods This prospective, non-randomized study was performed at a tertiary academic endoscopy center from March to August 2022. We included patients receiving a colonoscopy. Polypectomy had to be performed in all polyps. Every patient was examined concurrently by an endoscopist and AI using two opposing screens. The AI system, overseen by a second observer, was not visible to the endoscopist. The primary outcome was accuracy of the AI classifying the polyps into “neoplastic” and “non-neoplastic.” The secondary outcome was accuracy of the classification by the endoscopists. Sessile serrated lesions were classified as neoplastic. Results We included 156 patients (mean age 65; 57 women) with 262 polyps ≤10 mm. Eighty-four were hyperplastic polyps (32.1%), 158 adenomas (60.3%), seven sessile serrated lesions (2.7%) and 13 other entities (normal/inflammatory colonmucosa, lymphoidic polyp) (4.9%) on histological diagnosis. Sensitivity, specificity and accuracy of AI were 89.70% (95% confidence interval [CI]: 84.02%-93.88%), 75.26% (95% CI: 65.46%-83.46%) and 84.35% (95% CI:79.38%-88.53%), respectively. Sensitivity, specificity and accuracy for less experienced endoscopists (2–5 years of endoscopy) were 95.56% (95% CI: 84.85%-99.46%), 61.54% (95% CI: 40.57%-79.77%) and 83.10% (95% CI: 72.34%-90.95%) and for experienced endoscopists 90.83% (95% CI: 84.19%-95.33%), 71.83% (95% CI: 59.90%-81.87%) and 83.77% (95% CI: 77.76%-88.70%), respectively. Conclusion Accuracy for polyp characterization by a new commercially available AI system is high, but does not fulfill the criteria for a “resect-and-discard” strategy.

Publisher

Georg Thieme Verlag KG

Subject

Obstetrics and Gynecology

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