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This software is a computer-assisted reading tool designed to aid endoscopists in detecting colonic mucosal lesions (such as polyps and adenomas) in real time during standard endoscopy examinations of patients undergoing screening and surveillance endoscopic mucosal evaluations. This software is used with standard White Light Imaging (WLI) and Linked Color Imaging (LCI) endoscopy imaging. This software is not intended to replace clinical decision making.

Device Description

The subject device represents application of AI technology to endoscopic images to assist in detecting the presence of potential lesions. This development greatly contributes to improving the quality of colonoscopy. In recent years, computer-aided diagnosis (CAD) systems employing AI technologies have been approved and marketed as radiological medical devices for use with computed tomography (CT), X-ray, magnetic resonance imaging (MRI), and mammogram diagnostic images. In endoscopy as well, many images for diagnosis are taken. Since increasing the polyp detection rate is also in demand, CAD systems for endoscopy are being actively developed. Against this background, the company has developed this software (EW10-EC02), a new AI-based CAD system, to support Health Care Provider (HCP) detection of large intestine polyps in colonoscopic images. EW10-EC02 detects suspected large intestine polyps in the endoscope video image in real-time.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study findings for the EW10-EC02 Endoscopy Support Program, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document describes two main types of studies: standalone performance testing (evaluating the algorithm only) and clinical testing (evaluating human-in-the-loop performance). The acceptance criteria for the standalone performance are explicitly stated and met, while the clinical study endpoints serve as the criteria for evaluating the device's clinical benefit when assisting human readers.

Standalone Performance Acceptance Criteria & Results:

Item	Acceptance Criteria (Implicit, based on "achieved all criteria")	Reported Performance WLI Mode	Reported Performance LCI Mode
Sensitivity per lesion (Lesion-based sensitivity)	Exceeds a defined lower limit of the 95% CI (Specific value not provided but stated as met)	95.1% (91.1 - 98.3% CI)	95.5% (91.5 - 98.7% CI)
FP Objects/Patient (Number of FPc per Case)	(Specific criteria not numerically stated, but described as "achieved all criteria")	1.42 (1.09 - 1.81 CI)	0.76 (0.42 - 1.21 CI)
Detection Persistence (Figure 1)	(Implicit: Robust correlation of detection persistence with sensitivity and FP objects/patient)	Demonstrated strong correlation	Demonstrated strong correlation
Frame-level performance	(Implicit: Acceptable values for TP, TN, FP, FN, sensitivity/frame, FPR/frame)	(See Table 7 for detailed values)	(See Table 7 for detailed values)
ROC AUC	(Implicit: High accuracy)	0.79 (0.77-0.80 CI)	0.87 (0.86-0.88 CI)
FROC Analysis	(Implicit: Supports performance)	(See Figure 4)	(See Figure 4)

Clinical Study Endpoints & Results (serving as criteria for human-in-the-loop):

Endpoint	Acceptance Criteria (Implicit: Superiority for APC or meeting margins for PPV; non-inferiority for FPR)	Reported Performance (CAC group vs. CC group)	P-Value / CI
Primary Endpoints
Adenoma per colonoscopy (APC)	Superiority (CAC vs. CC)	CAC: 0.990 ± 1.610; CC: 0.849 ± 1.484	0.018 (Superiority met)
Positive predictive value (PPV)	Meeting margin of -9.56%	CAC: 48.6%; CC: 54.0%	-9.56%, -1.48% (Margin met)
Positive percent agreement (PPA)	(Implicit: Acceptable performance)	CAC: 60.7%; CC: 66.2%	-10.50%, -2.30%
Secondary Endpoints of Note
Polyp per colonoscopy (PPC)	(Implicit: Acceptable performance, P-value

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