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510(k) Data Aggregation
(149 days)
FLEXSCOPE
FlexScope is intended to be used for visualization of tissue in The general surgical applications.
The FlexScope™ is a flexible deflectable endoscope intended to allow physicians to visualize tissue in body cavities and through natural body orifices. The device is designed to be advanced to the site of interest through a cannula or trocar or may be used in an open procedure. The device will is available in various diameters.
The method of construction and materials used for these flexible fiber optic endoscopes are substantially equivalent to previous endoscope products.
This document describes a traditional medical device (an endoscope), not an AI/ML powered device. Therefore, the requested information regarding acceptance criteria, study details, ground truth, and AI/ML specific aspects (like multi-reader multi-case studies, standalone performance, training sets, etc.) is not applicable.
The provided text focuses on the physical characteristics, materials, and intended use of the FlexScope™ Fiber Optic Endoscope, comparing it to predicate devices to establish substantial equivalence.
Here's a breakdown of the relevant information from the provided text, recognizing the absence of AI/ML specific details:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Biocompatibility | All materials are biocompatible and suitable for this application. |
| Physical Testing | Included: dimensional inspection, bond strength testing, optical clarity, and performance under simulated conditions. |
| Test Results | All testing of the product yielded acceptable results. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not applicable. This is a physical device, and the testing described is primarily engineering and material-based, not data-driven (like an AI/ML model). There's no "test set" in the context of data for an algorithm.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)
Not applicable. Ground truth, in the context of AI/ML, refers to labels or diagnoses. For a physical endoscope, "ground truth" relates to engineering specifications and performance standards which are met through physical testing.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This refers to adjudication of expert opinions for ground truth in AI/ML studies.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
No. This type of study is specifically for AI/ML algorithms and their impact on human reader performance. The FlexScope™ is a physical visualization tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This refers to the performance of an AI/ML algorithm by itself.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable in the AI/ML sense. The "ground truth" for the FlexScope™ would be objective engineering standards and observed physical performance during testing.
8. The sample size for the training set
Not applicable. There is no AI/ML model to train.
9. How the ground truth for the training set was established
Not applicable. There is no AI/ML model to train.
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