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The MIDAScope™ and Introducer Kit, and MIDASystem™ are indicated for use in diagnostic and operative arthroscopic and endoscopic procedures to provide illumination and visualization of interior cavity joints and other body cavities through a natural or surgical opening.

The MIDAScope™ is a miniaturized, sterile, single-use disposable scope. The image is acquired by an image sensor located at the distal tip. Illumination is provided by an LED and fibers when the disposable scope is connected to the reusable MIDASystem™ via an electrical cable. The scope is available in different lengths to accommodate different treatment locations. The scope is introduced into the joint space using the IntraVu disposable Introducer Kit, consisting of a cannula, trocar and stopcock, which are provided sterile in the same package as the scope.

The MIDASystem™ is a reusable device consisting of an integrated LCD display, and software that receives, processes and displays the images collected by the scope.

This FDA 510(k) summary describes the substantial equivalence of the MIDAScope and Introducer Kit, and MIDASystem to predicate devices. It does not contain information about a study comparing AI vs. human readers or a standalone algorithm performance study. The performance testing section primarily focuses on engineering and regulatory compliance rather than clinical performance or diagnostic accuracy.

Here's a breakdown of the requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document lists various standards followed and states that "All tests met the pre-determined acceptance criteria at baseline and after aging." The specific acceptance criteria within each standard (e.g., specific thresholds for image resolution, strength of materials) are not detailed in this summary. Instead, it indicates that the device passed these standards.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This document does not provide information regarding sample sizes for clinical test sets or data provenance. The performance testing mentioned is primarily related to engineering standards (e.g., material testing, electrical safety, sterilization validation) rather than clinical accuracy or diagnostic performance evaluated on patient data.

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)

This document does not provide information on expert involvement for establishing ground truth, as the focus is on device engineering and safety standards, not diagnostic accuracy studies involving human experts interpreting images.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This document does not provide information on any adjudication methods.

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

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study, nor any information about AI assistance or its effect size on human reader performance. The device described, MIDAScope, is an arthroscope, which is a visualization and surgical tool, not an AI-powered diagnostic system.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

This document does not mention any standalone algorithm performance, as the device is not an AI algorithm but a medical instrument for direct visualization.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Since the document focuses on engineering and regulatory compliance rather than diagnostic accuracy, it does not specify a type of ground truth related to clinical findings. The "ground truth" in this context would refer to meeting the specified parameters of the engineering standards (e.g., sterilization effectively killed all microorganisms, electrical leakage was within limits).

8. The sample size for the training set

This document does not provide information about a training set, as it does not describe an AI or machine learning component.

9. How the ground truth for the training set was established

This document does not provide information about a training set or its ground truth establishment, as it does not describe an AI or machine learning component.

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