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510(k) Data Aggregation
(41 days)
The C Scope Visualization System is indicated to be used by a trained physician to provide illumination and visualization in arthroscopic procedures of an interior cavity of the body through a surgical opening.
The C Scope Visualization System Includes the following components: Tablet with custom software for image/video processing and display, capture and USB data management, and external power supply Reusable handpiece with LED light source. Disposable single-use sterile kit including a semi-rigid scope with integral drape, and supplemental instruments including a cannula, trocar/obturator, and syringe and tubing for flushing.
This document (K203526/S001) is a 510(k) premarket notification for the C Scope Visualization System, which is an arthroscope. The document primarily focuses on demonstrating substantial equivalence to a predicate device, not on presenting a standalone study proving the device meets specific performance criteria in a clinical setting with human readers or a detailed statistical analysis of AI performance.
Therefore, the requested information elements related to AI performance, ground truth establishment for a training set, human reader performance, sample sizes for test/training sets in the context of AI, and adjudication methods are not addressed in this document. This submission relies on non-clinical performance test data to demonstrate substantial equivalence to a predicate device, rather than a novel AI algorithm with specific acceptance criteria that needs to be "proven" through a clinical study.
Here's the information that can be extracted or inferred from the provided text:
1. A table of acceptance criteria and the reported device performance:
The document doesn't provide a specific table of "acceptance criteria" for a novel AI device with corresponding performance metrics (e.g., sensitivity, specificity, AUC). Instead, it lists various non-clinical tests conducted to demonstrate that the C Scope Visualization System meets regulatory requirements and performs similarly to its predicate device. The "reported device performance" is essentially that the device passed these tests.
| Test Category | Acceptance Criteria (Implied: Conformance to relevant standards) | Reported Device Performance |
|---|---|---|
| Environmental and Packaging | ASTM D4169-16 DC-13, ASTM D4332-14, ISO 11607-2 2019 (E) | Met all standards; seal formation validated. |
| Mechanical and Durability | IEC/EN 60529:2013, ISO 80369-7:2016 | Met all product requirements. |
| Human Factors | Guidance for Industry and FDA Staff "Applying Human Factors..." | Two formative studies and one summative validation study conducted. |
| Electrical Safety & EMC | IEC 60601-1:2012, IEC 60601-2-18:2009, IEC 60601-1-2:2014, AIM 7351731 | Complies with all standards. |
| Software Validation & Verification | FDA Guidance for Industry and Staff "Guidance for the Content...", AAMI/IEC 62304:2006/A1:2016, FDA Guidance "General Principles of Software Validation." | Validation and verification testing conducted. |
| Biocompatibility Testing | ISO 10993-1:2018 (parts 5, 10, 11, 17, 18) | Testing conducted as per ISO 10993-1:2018. |
| Sterilization and Shelf Life | ISO 11135-1:2014, ISO 10993-7:2008 Amd 1:2019 (E), ASTM F1980:2016 | Validated to SAL of 10-6; residuals below limits; shelf-life demonstrated. |
| Reprocessing (Handpiece) | AAMI TIR12:2010, AAMI TIR30:2011 (R2016), FDA Guidance "Reprocessing Medical Devices..." | Validation studies conducted. |
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 / Not Provided for AI performance. This document describes non-clinical testing of a physical medical device. There is no information about a "test set" in the context of an AI algorithm evaluated for diagnostic performance.
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 / Not Provided for AI performance. Ground truth establishment for a test set of medical images/data is not relevant to this type of device submission.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not Applicable / Not Provided for AI performance. Adjudication methods are not relevant to the non-clinical performance testing described.
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, an MRMC comparative effectiveness study was not done. This submission does not involve AI assistance for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not Applicable. The device is a "Visualization System" (arthroscope) for human physicians, not a standalone AI algorithm. While it has custom software for image/video processing, the submission does not present it as an AI-driven diagnostic tool with standalone performance metrics.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Not Applicable / Not Provided for AI performance. The "ground truth" in this context would be the specifications and requirements defined by the various engineering and biocompatibility standards to which the device was tested. For example, for sterilization, the ground truth is a SAL of 10^-6 validated according to ISO 11135-1:2014.
8. The sample size for the training set
- Not Applicable / Not Provided for AI performance. There is no mention of a training set for an AI algorithm.
9. How the ground truth for the training set was established
- Not Applicable / Not Provided for AI performance. There is no mention of a training set for an AI algorithm.
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