(273 days)
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.
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.
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 |
§ 876.1520 Gastrointestinal lesion software detection system.
(a)
Identification. A gastrointestinal lesion software detection system is a computer-assisted detection device used in conjunction with endoscopy for the detection of abnormal lesions in the gastrointestinal tract. This device with advanced software algorithms brings attention to images to aid in the detection of lesions. The device may contain hardware to support interfacing with an endoscope.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Clinical performance testing must demonstrate that the device performs as intended under anticipated conditions of use, including detection of gastrointestinal lesions and evaluation of all adverse events.
(2) Non-clinical performance testing must demonstrate that the device performs as intended under anticipated conditions of use. Testing must include:
(i) Standalone algorithm performance testing;
(ii) Pixel-level comparison of degradation of image quality due to the device;
(iii) Assessment of video delay due to marker annotation; and
(iv) Assessment of real-time endoscopic video delay due to the device.
(3) Usability assessment must demonstrate that the intended user(s) can safely and correctly use the device.
(4) Performance data must demonstrate electromagnetic compatibility and electrical safety, mechanical safety, and thermal safety testing for any hardware components of the device.
(5) Software verification, validation, and hazard analysis must be provided. Software description must include a detailed, technical description including the impact of any software and hardware on the device's functions, the associated capabilities and limitations of each part, the associated inputs and outputs, mapping of the software architecture, and a description of the video signal pipeline.
(6) Labeling must include:
(i) Instructions for use, including a detailed description of the device and compatibility information;
(ii) Warnings to avoid overreliance on the device, that the device is not intended to be used for diagnosis or characterization of lesions, and that the device does not replace clinical decision making;
(iii) A summary of the clinical performance testing conducted with the device, including detailed definitions of the study endpoints and statistical confidence intervals; and
(iv) A summary of the standalone performance testing and associated statistical analysis.