Evaluation of novel LCI CAD EYE system for real time detection of colon polyps

Helmut Neumann,Andreas Kreft,Fareed Rahman,Peter R Galle,Visvakanth Sivanathan,Ruxandra Stoean

doi:10.1371/journal.pone.0255955

Abstract

BackgroundLinked color imaging (LCI) has been shown to be effective in multiple randomized controlled trials for enhanced colorectal polyp detection. Recently, artificial intelligence (AI) with deep learning through convolutional neural networks has dramatically improved and is increasingly recognized as a promising new technique for enhancing colorectal polyp detection.AimThis study aims to evaluate a newly developed computer-aided detection (CAD) system in combination with LCI for colorectal polyp detection.MethodsFirst, a convolutional neural network was trained for colorectal polyp detection in combination with the LCI technique using a dataset of anonymized endoscopy videos. For validation, 240 polyps within fully recorded endoscopy videos in LCI mode, covering the entire spectrum of adenomatous histology, were used. Sensitivity (true-positive rate per lesion) and false-positive frames in a full procedure were assessed.ResultsThe new CAD system used in LCI mode could process at least 60 frames per second, allowing for real-time video analysis. Sensitivity (true-positive rate per lesion) was 100%, with no lesion being missed. The calculated false-positive frame rate was 0.001%. Among the 240 polyps, 34 were sessile serrated lesions. The detection rate for sessile serrated lesions with the CAD system used in LCI mode was 100%.ConclusionsThe new CAD system used in LCI mode achieved a 100% sensitivity per lesion and a negligible false-positive frame rate. Note that the new CAD system used in LCI mode also specifically allowed for detection of serrated lesions in all cases. Accordingly, the AI algorithm introduced here for the first time has the potential to dramatically improve the quality of colonoscopy.

Highlights

Colonoscopy is the gold standard for the detection of colorectal adenomas, which are considered to be the precursor lesions of colorectal cancer [1]
The new computer-aided detection (CAD) system used in Linked color imaging (LCI) mode could process at least 60 frames per second, allowing for real-time video analysis
The detection rate for sessile serrated lesions with the CAD system used in LCI mode was 100%

Summary

Introduction

Colonoscopy is the gold standard for the detection of colorectal adenomas, which are considered to be the precursor lesions of colorectal cancer [1]. Various image-enhanced endoscopy systems have been shown to be effective for reducing adenoma miss rates, thereby significantly improving the quality of colonoscopy examinations [4, 5]. These systems have only been shown to be effective in the hands of so-called experts; they have failed to be effective in community practice [6, 7]. None of the systems have yet been shown to be effective in detecting sessile serrated lesions. Artificial intelligence (AI) with deep learning through convolutional neural networks has dramatically improved and is increasingly recognized as a promising new technique for enhancing colorectal polyp detection

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Conclusion