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HTx disposable hysteroscope system is intended to be used for viewing of the cervical canal and uterine cavity for the purpose of performing diagnostic and operative procedures.

The HTx Disposable Hysteroscope System is a single use hysteroscope endoscope intended for gynecology procedure applications. It includes a disposable cannula module, a reusable imaging system, a medical grade display and optional software for processing patient management. The disposable cannula contains a miniature complementary metal-oxide-semiconductor (CMOS) camera, a light-emitting diode (LED) illumination module, and a channel for fluid and device. The cannula connects directly to the image system via an image cable. The cannula also contains three (3) buttons for video recording, picture taking, and light-emitting diode (LED) brightness control. The image system connects to a medical grade display module via DVI port. The external patient management software runs on an external PC that connects with the image system via LAN cable.

This document is a 510(k) premarket notification for the AcuVu HTx Disposable Hysteroscope System, indicating that the device is a Class II medical device. The document primarily focuses on demonstrating substantial equivalence to a predicate device (AcuVu GDT-1000, K180096) rather than detailing a clinical study with specific acceptance criteria and performance metrics for an AI-powered device.

Therefore, many of the requested categories for AI-specific studies are not applicable or cannot be extracted from this document, as the HTx Disposable Hysteroscope System, as described, does not appear to be an AI-powered device in the sense of an algorithm for diagnostic or prognostic purposes. It is a traditional medical imaging device (hysteroscope) for viewing the cervical canal and uterine cavity.

Here's an attempt to answer the questions based on the provided text, acknowledging the limitations:

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

The document does not provide a table of acceptance criteria with numerical performance metrics for a diagnostic or AI algorithm. Instead, it lists various non-clinical tests performed to ensure the device meets safety and functional requirements. The "acceptance criteria" can be inferred as compliance with the listed standards and demonstrating acceptable results in the tests.

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 describes technical and performance testing of a physical medical device (hysteroscope), not a clinical study on a patient population with a "test set" of data in the AI sense. No patient sample sizes or data provenance (country, retrospective/prospective) are mentioned. The testing involves laboratory verification and validation activities.

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. As noted above, this is not an AI-powered diagnostic device undergoing a clinical study with ground truth established by experts.

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

Not applicable. This document does not describe a clinical study requiring an adjudication method for a test set.

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. An MRMC study is relevant for evaluating the impact of AI on human reader performance. This document concerns a hysteroscope, which is an imaging device, not an AI algorithm assisting human readers.

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

Not applicable. This device is not an AI algorithm.

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

Not applicable. For the technical and performance testing described, the "ground truth" is measured against engineering specifications and international standards (e.g., optical properties, sterility, biocompatibility).

8. The sample size for the training set

Not applicable. This device does not use an AI algorithm that requires a training set.

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

Not applicable. This device does not use an AI algorithm that requires a training set and associated ground truth.

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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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