(30 days)
The GI Genius System 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 white-light endoscopy examinations of patients undergoing screening and surveillance endoscopic mucosal evaluations. The GI Genius computer-assisted detection device is limited for use with standard white-light endoscopy imaging only. This device is not intended to replace clinical decision making.
GI Genius is an artificial intelligence-based device that has been trained to process colonoscopy images containing regions consistent with colorectal lesions like polyps, including those with flat (non-polypoid) morphology.
GI Genius is compatible with the following Video Processors: Fujifilm VP-7000, Olympus CV-180 EXERA II, Olympus CV-190 EXERA III, Fujifilm VP-4450HD, and Pentax EPK-i7000 Video Processor.
GI Genius is connected between the video processor and the endoscopic display monitor. When first switched on, the endoscopic field of view is clearly identified by four corner markers, and a blinking green square indicator appears on the connected endoscopic display monitor to state that the system is ready to function.
During live video streaming of the endoscopic video image, GI Genius generates a video output on the endoscopic display monitor that contains the original live video together with superimposed green square markers that will appear when a polyp or other lesion of interest is detected, accompanied by a short sound. These markers will not be visible when no lesion detection occurs.
The operating principle of the subject device is identical to that of the predicate device, this being 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 includes hardware to support interfacing with video endoscopy systems.
Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the comparison to the predicate device (GI Genius v1.0.2). The goal of the study for GI Genius v2.0.0 was to demonstrate non-inferiority or improved performance compared to the predicate.
| Characteristic | Acceptance Criteria (Predicate Device Performance) | Reported Device Performance (GI Genius v2.0.0) | Result (vs. Predicate) |
|---|---|---|---|
| Lesion-based sensitivity | 82.0 % | 86.5 % | Improved |
| Frame-level True Positive | 228,929 | 269,223 | Improved |
| Frame-level True Negative | 5,235,682 | 5,239,128 | Improved |
| Frame-level False Positive | 108,115 | 104,669 | Improved |
| Frame-level False Negative | 232,861 | 192,567 | Improved |
| True Positive Rate per frame Mean | 49.57 % | 58.30 % | Improved |
| True Positive Rate per frame % of polyps | 99.7 % | 100 % | Improved |
| False Positive Rate per frame Mean | 2.02 % | 1.96 % | Improved |
| Frame-Based TPr/FPr ROC curve, AUC | 0.723 | 0.796 | Improved |
| False positive clusters 500 ms | Baseline | 1 more than baseline | Similar |
| Video delay, signal in to signal out | 1.52 μs | 1.52 μs | Same |
2. Sample Size Used for the Test Set and Data Provenance
The non-clinical testing for standalone performance used 150 videos containing 338 polyps.
The data provenance is not explicitly stated regarding country of origin or whether it was retrospective or prospective. However, given that it's a re-training of a neural network with "additional procedure videos," it suggests a retrospective dataset of previously recorded endoscopy procedures.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the given text.
4. Adjudication Method for the Test Set
This information is not provided in the given text.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, and Effect Size
A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned for the GI Genius v2.0.0. The evaluation focuses on standalone performance relative to its predicate.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, a standalone performance test was done. The text states: "Standalone Performance Testing has been carried out to assess the performance of the subject device in accordance with the same test protocol as that used for the predicate device, the results of which demonstrate substantial equivalence to the predicate device."
7. The Type of Ground Truth Used
The ground truth used for performance evaluation appears to be based on the identified "polyps and adenomas" within the procedure videos. While not explicitly stated as "expert consensus" or "pathology," the context of "colonic mucosal lesions (such as polyps and adenomas)" generally implies a clinical or pathological reference standard.
8. The Sample Size for the Training Set
The document mentions that the neural network was re-trained with "additional procedure videos" and "improved data augmentation," but it does not specify the sample size of the training set (number of videos or lesions).
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly state how the ground truth for the training set was established. However, given that the device aids in "detecting colonic mucosal lesions (such as polyps and adenomas)," it implies that these lesions were identified and annotated within the training videos, likely by expert endoscopists or pathologists.
§ 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.