(28 days)
ME-APDS (Magentig Eye's Automatic Polyp Detection System) is intended to be used by endoscopists as an adjunct to the common video colonoscopy procedure (screening and surveillance), aiming to assist the endoscopist in identifying lesions during colonoscopy procedure by highlighting reqions with visual characteristics consistent with different types of mucosal abnormalities that appear in the colonoscopy video during the procedure. Highlighted regions can be independently assessed by the endoscopist and appropriate action taken according to standard clinical practice.
ME-APDS is trained to process video images which may contain regions consistent with polyps.
ME-APDS is limited for use with standard white-light endoscopy imaging only.
ME-APDS is intended to be used as an adjunct to endoscopy procedures and is not intended to replace histopathological sampling as means of diagnosis.
ME-APDS™MAGENTIQ-COLO is intended to be used as an adjunct to the common video colonoscopy procedure. The system application aims to assist the endoscopist in identifying lesions, such as polyps, during the colonoscopy procedures in real time. The device is not intended to be used for diagnosis or characterization of lesions, and does not replace clinical decision making.
The system acquires the digital video output signal from the local endoscopy camera and processes the video frames. It runs deep machine learning and additional supporting algorithms in real time on the video frames in order to detect and identify regions having characteristics consistent with different types of mucosal abnormalities such as polyps. The output video with the detected lesions is presented on a separate screen, highlighting the suspicious areas on the original video. The user can also take snapshots of the videos, with and without the highlighting of the suspicious areas, record videos and view in full screen mode.
Here's an analysis of the acceptance criteria and study details for the MAGENTIQ-COLO device, based on the provided document:
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria are implied by the reported performance metrics, particularly "Polyp-wise Recall" and "False Positives Per Frame (FPPF)". The study aims to demonstrate that the device performs comparably to or better than the predicate device.
| Acceptance Criteria / Metric | Reported Device Performance (Full Testing Dataset) |
|---|---|
| Polyp-wise Recall (PRecall1) | 97.9% [96.63%, 98.94%] |
| Polyp-wise Recall (PRecall3) | 95.3% [93.39%, 96.96%] |
| Polyp-wise Recall (PRecall5) | 93.2% [91.01%, 95.15%] |
| Polyp-wise Recall (PRecall7) | 90.6% [88.19%, 92.91%] |
| False Positives Per Frame (FPPF) | 0.0333 (threshold achieved) |
| Polyps with Histology: PRecall1 | 99.7% [99.12%, 100.0%] |
| Polyps with Histology: PRecall7 | 99.7% [99.11%, 100.0%] |
| Median Coverage of Polyps (with histology) | 81.7% |
| Marker Annotation Latency (Median) | 133 msec for FHD, 157 msec for 4K |
Note: The document states that "The testing results were observed to be as expected and support that the device has similar performance to the predicate device," implying that these reported values met the implicit acceptance criteria for substantial equivalence.
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size (Test Set): 212 unique full colonoscopy videos, containing 702 polyps (16 videos contained no polyps).
- Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
The document does not explicitly state the number of experts used to establish the ground truth or their specific qualifications (e.g., "radiologist with 10 years of experience"). However, it references polyps "verified by histology" and "reported in the procedure report," implying clinical expert input.
4. Adjudication Method for the Test Set
The document does not describe a specific adjudication method like 2+1 or 3+1. The ground truth seems to be derived from documented polyps in the "procedure report" and "histology findings," suggesting a standard clinical reporting process rather than a specific consensus method for this study.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No, a multi-reader multi-case (MRMC) comparative effectiveness study was not reported in this document. The study described is a standalone performance test of the algorithm. The document mentions that the clinical validation used to support the device's polyp detection functions was conducted in a previous submission (K223473). This K223473 submission might contain an MRMC study, but it's not detailed here.
6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study
Yes, a standalone performance study was done. The "Standalone Performance Testing" section describes how "The algorithm was tested offline" on an independent dataset to evaluate its recall, false positive performance, and false positives per full video rate without direct human interaction during the test.
7. Type of Ground Truth Used
The ground truth used for the test set was a combination of:
- Histopathology findings: For polyps with histology reports.
- Procedure reports: For polyps identified and documented during the colonoscopy procedure.
8. Sample Size for the Training Set
The document does not provide the sample size for the training set. It only states that "ME-APDS is trained to process video images which may contain regions consistent with polyps."
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for the training set was established. It only broadly states that the system "runs deep machine learning" and is "trained to process video images."
§ 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.