Endoscopy 2022; 54(S 01): S183
DOI: 10.1055/s-0042-1745059
Abstracts | ESGE Days 2022
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REAL-TIME CHARACTERIZATION OF COLORECTAL POLYPS USING ARTIFICIAL INTELLIGENCE – A PROSPECTIVE PILOT STUDY COMPARING TWO COMPUTER-AIDED DIAGNOSIS SYSTEMS AND ONE EXPERT ENDOSCOPIST

Q.E. van der Zander
1   Maastricht University Medical Center, Department of Gastroenterology and Hepatology, Maastricht, Netherlands
2   Maastricht University, GROW, School for Oncology and Developmental Biology, Maastricht, Netherlands
,
R.-M. Schreuder
3   Catharina Hospital Eindhoven, Department of Gastroenterology and Hepatology, Eindhoven, Netherlands
,
A. Thijssen
1   Maastricht University Medical Center, Department of Gastroenterology and Hepatology, Maastricht, Netherlands
2   Maastricht University, GROW, School for Oncology and Developmental Biology, Maastricht, Netherlands
,
K.C. Kusters
4   Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, Netherlands
,
N. Dehghani
4   Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, Netherlands
,
T. Scheeve
4   Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, Netherlands
,
R. Fonollà
4   Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, Netherlands
,
B. Winkens
5   Maastricht University, Department of Methodology and Statistics, Maastricht, Netherlands
6   Maastricht University, CAPHRI, Care and Public Health Research Institute, Maastricht, Netherlands
,
M.C. van der Ende-van Loon
3   Catharina Hospital Eindhoven, Department of Gastroenterology and Hepatology, Eindhoven, Netherlands
,
P.H. de With
4   Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, Netherlands
,
F. van der Sommen
4   Eindhoven University of Technology, Department of Electrical Engineering, Eindhoven, Netherlands
,
A.A. Masclee
1   Maastricht University Medical Center, Department of Gastroenterology and Hepatology, Maastricht, Netherlands
,
E.J. Schoon
1   Maastricht University Medical Center, Department of Gastroenterology and Hepatology, Maastricht, Netherlands
3   Catharina Hospital Eindhoven, Department of Gastroenterology and Hepatology, Eindhoven, Netherlands
› Author Affiliations
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    Aims Artificial intelligence (AI) has great potential in gastrointestinal endoscopy. Aim was to evaluate real-time diagnostic performances of our Artificial Intelligence for ColoRectal Polyps (AI4CRP) computer-aided diagnosis system for optical diagnosis of diminutive colorectal polyps (CRPs) and compare it with CAD EYE and an expert endoscopist.

    Methods AI4CRP was developed using convolutional neural networks and previously trained and tested. In this prospective real-time pilot study, AI4CRP was compared with CAD EYE© (Fujifilm, Tokyo, Japan) and one expert endoscopist unaware of AI-output. Blue light imaging was used for characterization and histopathology as gold standard. CRPs were characterized as hyperplastic (hyperplastic polyps) or neoplastic (adenomas, sessile serrated lesions[SSLs]) by AI4CRP and the endoscopist, and as hyperplastic (hyperplastic polyps, SSLs) or neoplastic (adenomas) by CAD EYE. CAD EYE’s inconclusive diagnoses were excluded. Enabling self-critical AI4CRP, post-hoc analysis excluded low confidence scores.

    Results Real-time testing included 30 patients with 51 CRPs (32 adenomas, 6 SSLs, 12 hyperplastic polyps). AI4CRP had a diagnostic accuracy of 80.4%, sensitivity of 82.1%, and specificity of 75.0%. For self-critical AI4CRP (n=37) the diagnostic accuracy was 89.2%, sensitivity 89.7%, and specificity 87.5%. CAD EYE (n=49) had a diagnostic accuracy of 83.7%, sensitivity of 74.2%, and specificity of 100.0%. For the expert endoscopist the diagnostic accuracy was 88.2%, sensitivity 94.9%, and specificity 66.7%.

    Table 1

    AI4CRP,%

    Self-critical AI4CRP,%

    CAD-EYE,%

    Endoscopist,%

    (n=51)

    (n=37)

    (n=49)

    (n=51)

    Diagnostic accuracy

    80.4

    89.2

    83.7

    88.2

    Sensitivity

    82.1

    89.7

    74.2

    94.9

    Specificity

    75.0

    87.5

    100.0

    66.7

    Conclusions AI4CRP achieved promising results, but CAD EYE’s diagnostic performances were higher. CAD EYE was unable to refrain from generating diagnoses for inconclusive cases. AI4CRP provided calibrated confidences, giving the ability to reject uncertain classifications, enabling better interpretability of AI-outputs.


     

    Publication History

    Article published online:
    14 April 2022

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