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. 2024 May 21;12(5):E676-E683.
doi: 10.1055/a-2303-0922. eCollection 2024 May.

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

Emanuele Rondonotti  1 , Irene Maria Bambina Bergna  2   1   3 , Silvia Paggi  1 , Arnaldo Amato  1   3 , Alida Andrealli  1 , Giulia Scardino  1 , Giacomo Tamanini  1 , Nicoletta Lenoci  1 , Giovanna Mandelli  1 , Natalia Terreni  1 , SImone Rocchetto  1   2 , Alessandra Piagnani  1   2 , Dhanai Di Paolo  1 , Niccolò Bina  1 , Emanuela Filippi  4 , Luciana Ambrosiani  4 , Cesare Hassan  5   6 , Loredana Correale  5 , Franco Radaelli  1
Affiliations

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

Emanuele Rondonotti et al. Endosc Int Open. .

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

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Conflict of interest statement

Conflict of Interest Paggi S and Amato A: speaker honoraria from Fujifilm Co. Rondonotti E: speaker honoraria from Fujifilm Co., Medtronic Co. consultancy. Radaelli F: Speaker honoraria from Fujifilm Co; research grant from Fujifilm Co; endoscopy equipment loan from Medtronic Co. Hassan C: Medtronic Co, Fujifilm Co and Odin Co. consultancy. All the other authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
This frame shows how the artificial intelligence (AI) system provides the optical diagnosis output; when the polyp is framed within the detection box, an optical diagnosis (OD) label is overlaid (a adenoma, b non-adenoma, c no prediction).
Fig. 2
Fig. 2
Patient flow.
See this image and copyright information in PMC

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