Autonomous Artificial Intelligence vs Artificial Intelligence–Assisted Human Optical Diagnosis of Colorectal Polyps: A Randomized Controlled Trial

• Published in: Gastroenterology (New York, N.Y. 1943) Vol. 167; no. 2; pp. 392 - 399.e2
• Main Authors: Djinbachian, Roupen, Haumesser, Claire, Taghiakbari, Mahsa, Pohl, Heiko, Barkun, Alan, Sidani, Sacha, Liu Chen Kiow, Jeremy, Panzini, Benoit, Bouchard, Simon, Deslandres, Erik, Alj, Abla, von Renteln, Daniel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-07-2024
• Subjects: Adult; Aged; Artificial Intelligence; Autonomous AI; CADx; Colonic Polyps - diagnosis; Colonic Polyps - diagnostic imaging; Colonic Polyps - pathology; Colonoscopy; Colonoscopy - methods; Colorectal Cancer; Colorectal Neoplasms - diagnosis; Colorectal Neoplasms - pathology; Diagnosis, Computer-Assisted; Female; Humans; Male; Middle Aged; Optical Diagnosis; Predictive Value of Tests; Reproducibility of Results; CRC; NPV; Colonoscopy; Optical Diagnosis; Autonomous AI; PPV; Colorectal Cancer; AI; PIVI; CADx; AI-H; SSL
• ISSN: 0016-5085; 1528-0012; 1528-0012
• DOI: 10.1053/j.gastro.2024.01.044

Artificial intelligence (AI)–based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies. However, CADx systems have not yet been validated for autonomous performance. Therefore, we conducted a trial comparing autonomous AI to AI-assisted human (AI-H) optical diagnosis.
We performed a randomized noninferiority trial of patients undergoing elective colonoscopies at 1 academic institution. Patients were randomized into (1) autonomous AI-based CADx optical diagnosis of diminutive polyps without human input or (2) diagnosis by endoscopists who performed optical diagnosis of diminutive polyps after seeing the real-time CADx diagnosis. The primary outcome was accuracy in optical diagnosis in both arms using pathology as the gold standard. Secondary outcomes included agreement with pathology for surveillance intervals.
A total of 467 patients were randomized (238 patients/158 polyps in the autonomous AI group and 229 patients/179 polyps in the AI-H group). Accuracy for optical diagnosis was 77.2% (95% confidence interval [CI], 69.7–84.7) in the autonomous AI group and 72.1% (95% CI, 65.5–78.6) in the AI-H group (P = .86). For high-confidence diagnoses, accuracy for optical diagnosis was 77.2% (95% CI, 69.7–84.7) in the autonomous AI group and 75.5% (95% CI, 67.9–82.0) in the AI-H group. Autonomous AI had statistically significantly higher agreement with pathology-based surveillance intervals compared to AI-H (91.5% [95% CI, 86.9–96.1] vs 82.1% [95% CI, 76.5–87.7]; P = .016).
Autonomous AI-based optical diagnosis exhibits noninferior accuracy to endoscopist-based diagnosis. Both autonomous AI and AI-H exhibited relatively low accuracy for optical diagnosis; however, autonomous AI achieved higher agreement with pathology-based surveillance intervals. (ClinicalTrials.gov, Number NCT05236790)
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In this study, autonomous artificial intelligence performed with high accuracy in diagnosing small (1–5-mm) colorectal polyps during colonoscopies and allowed follow-ups to be scheduled more correctly than when humans decided the final diagnosis.