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Acu Vu GDT-1000 System is used to permit viewing of the adult cervical canal and uterine cavity for the purpose of performing diagnostic and operative procedures.

The AcuVu GDT-1000 System is a single-use hysteroscope intended for gynecologic procedures. It includes a disposable cannula module, a reusable handle module, a tower mounted swivel base medical grade touch screen PC for processing system software, operating the graphical user interface, performing image and video capture and display, as well as report creation for a session.

The Handle Module is reusable. Its distal end has a connecting interface, with self-locking mechanism, for attaching and detaching the disposable cannula. Its proximal end has a USB connection interface and cable length of 2.9 m for attaching the handle to the Display Module.

The disposable cannula module family includes two models. One for the purpose of diagnosis, and the other one for operative/therapeutic procedure. The diagnosis cannula has one water channel with two luers at proximal end, for water inflow and outflow. The therapeutic operative cannula has one shared working channel with three lures at proximal end, for water inflow and outflow, as well as for small operative instrument access. The disposable single-use cannula contains a miniature camera module and a pair of light- emitting diode (LED) illumination module at the tip. The cannula connects to the handle through an electrical connector for image data transfer, camera control and power supply. The disposable cannula has overall length of 300 mm to 350 mm.

This submission pertains to the AcuVu GDT-1000 System, a hysteroscope used for viewing the adult cervical canal and uterine cavity for diagnostic and operative procedures. The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than comprehensive clinical study results for novel acceptance criteria. Therefore, the information typically associated with complex AI/ML device studies (like MRMC studies, detailed ground truth establishment for large datasets, or specific performance metrics like AUC, sensitivity, and specificity for diagnostic tasks) is not entirely applicable or present for this type of medical device submission.

The "acceptance criteria" for this device are primarily related to its compliance with established performance standards and its substantial equivalence to the predicate device, not statistical performance against a specific diagnostic target.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally met through compliance with recognized standards and successful completion of verification and validation activities. The reported "device performance" is primarily confirmation of this compliance, rather than specific numerical metrics for diagnostic accuracy typically seen with AI/ML devices.

2. Sample Size Used for the Test Set and the Data Provenance

This document is a 510(k) summary for a hysteroscope, which is a physical medical device, not an AI/ML diagnostic software. The "test set" and "data provenance" as understood in the context of AI/ML performance evaluation (i.e., a discrete, often large, set of medical data cases used to evaluate an algorithm's diagnostic performance) are not directly applicable or described in this submission.

Instead, testing (verification and validation) for such devices typically involves:

• Engineering tests on physical prototypes/production units.
• Bench testing for mechanical, electrical, and optical properties.
• Biocompatibility testing on materials.
• Sterilization and shelf-life validation using batches of products.

The document does not specify sample sizes for these tests in terms of "cases" like an imaging dataset. The provenance of these test results would be internal laboratory testing and external accredited lab testing, as implied by the reference to various IEC, ISO, and ASTM standards. No specific country of origin for clinical data (retrospective or prospective) is mentioned because this is a hardware device review, not a clinical trial summary.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

This information is not applicable to the provided 510(k) submission. The device is a hysteroscope, an imaging tool, not a diagnostic algorithm that interprets images. Therefore, there's no "ground truth" to be established by experts in the sense of labeling a dataset for AI performance evaluation. The "ground truth" for this device's performance relates to its ability to meet engineering and safety standards, confirmed through objective measurements and standard compliance.

4. Adjudication Method for the Test Set

Not applicable. As explained above, this submission does not detail an AI/ML algorithm's performance evaluation against a diagnostic ground truth derived from expert adjudication. "Testing" involves compliance with standards and functional verification.

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

Not applicable. This device is a physical hysteroscope, not an AI-powered diagnostic software designed to assist human readers. Therefore, an MRMC study comparing human readers with and without AI assistance was not performed and is not relevant to this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not applicable. This device is a hysteroscope, which is an imaging device used by a human operator, not a standalone algorithm.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The concept of "ground truth" as applied to AI/ML diagnostic algorithms (e.g., pathology reports, long-term outcomes, expert consensus on images) is not directly relevant here. For this hysteroscope, the "ground truth" for its performance is implicitly derived from compliance with a wide range of engineering, safety, and performance standards (e.g., optical resolution is measured objectively, biocompatibility is assessed by chemical tests and biological responses, electrical safety by standardized electrical measurements).

8. The Sample Size for the Training Set

Not applicable. This device is a hardware hysteroscope. It does not involve a "training set" in the context of machine learning.

9. How the Ground Truth for the Training Set Was Established

Not applicable. As there is no training set for an AI/ML algorithm, there is no ground truth to be established for it.

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