White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice

Emanuele Rondonotti; Irene Maria Bambina Bergna; Silvia Paggi; Arnaldo Amato; Alida Andrealli; Giulia Scardino; Giacomo Tamanini; Nicoletta Lenoci; Giovanna Mandelli; Natalia Terreni; SImone Rocchetto; Alessandra Piagnani; Dhanai Di Paolo; Niccolò Bina; Emanuela Filippi; Luciana Ambrosiani; Cesare Hassan; Loredana Correale; Franco Radaelli

doi:10.1055/a-2303-0922

Endoscopy International Open, Table of Contents

CC BY-NC-ND 4.0 · Endoscopy 2024; 12(05): E676-E683
DOI: 10.1055/a-2303-0922

Original article

White light computer-aided optical diagnosis of diminutive colorectal polyps in routine clinical practice

Emanuele Rondonotti

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Irene Maria Bambina Bergna

²University of Milan, Milano, Italy (Ringgold ID: RIN9304)

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

³Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy (Ringgold ID: RIN9337)

,

Silvia Paggi

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Arnaldo Amato

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

³Gastroenterology and Digestive Endoscopy Unit, Alessandro Manzoni Hospital, Lecco, Italy (Ringgold ID: RIN9337)

,

Alida Andrealli

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Giulia Scardino

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Giacomo Tamanini

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Nicoletta Lenoci

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Giovanna Mandelli

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Natalia Terreni

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

SImone Rocchetto

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

²University of Milan, Milano, Italy (Ringgold ID: RIN9304)

,

Alessandra Piagnani

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

²University of Milan, Milano, Italy (Ringgold ID: RIN9304)

,

Dhanai Di Paolo

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Niccolò Bina

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Emanuela Filippi

⁴Pathology Department, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Luciana Ambrosiani

⁴Pathology Department, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

,

Cesare Hassan

⁵Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy (Ringgold ID: RIN9268)

⁶Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy (Ringgold ID: RIN437807)

,

Loredana Correale

⁵Department of Gastroenterology, IRCCS Humanitas Research Hospital, Rozzano, Italy (Ringgold ID: RIN9268)

,

Franco Radaelli

¹Gastroenterology Unit, Valduce Hospital, Como, Italy (Ringgold ID: RIN9349)

› Author Affiliations

Abstract

Background and study aims Artificial Intelligence (AI) systems could make the optical diagnosis (OD) of diminutive colorectal polyps (DCPs) more reliable and objective. This study was aimed at prospectively evaluating feasibility and diagnostic performance of AI-standalone and AI-assisted OD of DCPs in a real-life setting by using a white light-based system (GI Genius, Medtronic Co, Minneapolis, Minnesota, United States).

Patients and methods Consecutive colonoscopy outpatients with at least one DCP were evaluated by 11 endoscopists (5 experts and 6 non-experts in OD). DCPs were classified in real time by AI (AI-standalone OD) and by the endoscopist with the assistance of AI (AI-assisted OD), with histopathology as the reference standard.

Results Of the 480 DCPs, AI provided the outcome “adenoma” or “non-adenoma” in 81.4% (95% confidence interval [CI]: 77.5–84.6). Sensitivity, specificity, positive and negative predictive value, and accuracy of AI-standalone OD were 97.0% (95% CI 94.0–98.6), 38.1% (95% CI 28.9–48.1), 80.1% (95% CI 75.2–84.2), 83.3% (95% CI 69.2–92.0), and 80.5% (95% CI 68.7–82.8%), respectively. Compared with AI-standalone, the specificity of AI-assisted OD was significantly higher (58.9%, 95% CI 49.7–67.5) and a trend toward an increase was observed for other diagnostic performance measures. Overall accuracy and negative predictive value of AI-assisted OD for experts and non-experts were 85.8% (95% CI 80.0–90.4) vs. 80.1% (95% CI 73.6–85.6) and 89.1% (95% CI 75.6–95.9) vs. 80.0% (95% CI 63.9–90.4), respectively.

Conclusions Standalone AI is able to provide an OD of adenoma/non-adenoma in more than 80% of DCPs, with a high sensitivity but low specificity. The human-machine interaction improved diagnostic performance, especially when experts were involved.

Keywords

Endoscopy Lower GI Tract - Polyps / adenomas / ... - Diagnosis and imaging (inc chromoendoscopy, NBI, iSCAN, FICE, CLE...) - Colorectal cancer

Full Text

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Supplementary Material

Supplementary Material