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Creating a standardized tool for the evaluation and comparison of artificial intelligence-based computer-aided

Sanjay R V Gadi¹, Yuichi Mori², Masashi Misawa³

¹Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.

Gastrointestinal Endoscopy

|November 28, 2024

Summary

This study established key metrics for evaluating computer-aided detection (CADe) in colonoscopy. The top criteria include sensitivity and independent validation, aiming to improve colorectal cancer screening tools.

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Computer-aided detection (CADe) software is increasingly used for colorectal cancer screening.
Existing CADe algorithms lack standardized evaluation methods, creating uncertainty in performance comparison.
Regulatory approval has been granted for CADe in multiple global regions.

Purpose of the Study:

To establish priority scoring metrics for the evaluation and comparison of CADe algorithms in colonoscopy.
To develop a consensus-based framework for assessing CADe performance in clinical practice.

Main Methods:

A modified Delphi approach involving 25 global experts in CADe for colonoscopy.
An 8-month online survey process including criteria generation, open commenting, and multi-round priority scoring.
Utilized a 5-point Likert scale for ranking 121 initially generated scoring criteria, refined to a final set of 6.

Main Results:

Sensitivity and separate/independent validation of CADe algorithms were identified as the top priority metrics (tied).
Adenoma detection rate, false-positive rate, latency, and adenoma miss rate were also ranked as high-priority evaluation criteria.
The study identified 6 key metrics through international expert consensus.

Conclusions:

This research presents the first international consensus on priority scoring metrics for CADe in colonoscopy.
The identified metrics should guide the development and refinement of CADe software for improved colorectal cancer screening.
Future research should focus on validating these metrics using benchmark datasets to create a standardized scoring instrument.