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The Reedgyn disposable hysteroscope RH-2D-01 is used to permit viewing of the cervical canal and uterine cavity for the purpose of performing diagnostic procedures in an outpatient or office setting. Generally recognized indications for diagnostic hysteroscopy include: abnormal bleeding, infertility and pregnancy wastage, evaluation of abnormal hysterosalpingogram, intrauterine foreign body, amenorrhea, and pelvic pain.

The Reedgyn disposable hysteroscope RH-2S-01 is used to permit viewing of the cervical canal and uterine cavity for the purpose of performing diagnostic procedures and to obtain an endometrial tissue sample (biopsy) in an outpatient or office setting. The sample is used for cytologic and histologic diagnosis. Generally recogmized indications for diagnostic hysteroscopy include: abnormal bleeding, infertility and pregnancy wastage, evaluation of abnormal hysterosalpingogram, intrauterine foreign body, amenorrhea, and pelvic pain.

The Reedgyn disposable hysteroscopes, which include models RH-2S-01 and RH-2D-01, are used to permit viewing of the cervical canal and uterine cavity for the purpose of performing diagnostic procedures (both RH-2S-01 and RH-2D-01) and to obtain an endometrial tissue sample (biopsy) in an outpatient or office setting (RH-2S-01 only).

RH-2S-01 and RH-2D-01 are similar in design. The only difference between RH-2S-01 and RH-2D-01 is: RH-2S-01 is intended for performing diagnostic procedures and obtaining an endometrial tissue sample (biopsy), while the RH-2D-01 is intended for performing diagnostic procedures only.

The Reedgyn disposable hysteroscopes consist of a lightweight handle, a cannula integrated with a camera and a light source at the distal end to illuminate the area for visualizing images. The distal end of the cannula is equipped with a camera module including a camera and LED lightling. LED irradiates the surface of the object, and the reflected light enters the image processor through a lens and converts it into electrical signals, and then converted into a digital image signal , which is transmitted to the digital signal processing chip. After chip processing, the signal is transmitted to PC through USB interface.

The Reedgyn disposable hysteroscopes should be operated together with Reedgyn hysteroscope imaging software which was installed to a Microsoft Windows PC station. (Note: the Windows PC station is not included in the product configuration.)

The provided text describes the Reedgyn disposable hysteroscope and its substantial equivalence to a predicate device, primarily through non-clinical performance testing. It focuses on engineering specifications and adherence to international standards rather than AI/algorithm performance. Therefore, I cannot provide a detailed answer to your request as it pertains to AI/algorithm acceptance criteria and studies for human performance improvement, ground truth establishment, or multi-reader studies, as this information is not present in the provided text.

However, I can extract and present the device's performance specifications mentioned in the text, which serve as acceptance criteria for its physical and optical characteristics.

Here's an attempt to fulfill the request based only on the provided document, acknowledging the limitations regarding AI/algorithm-specific details:

Device: Reedgyn disposable hysteroscope (Model RH-2S-01 and Model RH-2D-01)

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

The document does not explicitly present acceptance criteria in a pass/fail table format derived from a study. Instead, it lists the device's design specifications and then states that "The following performance data were provided to verify that the subject device met all design specifications. The results demonstrated that the subject device is as safe and effective as the predicate device."

Therefore, the "acceptance criteria" for the physical and optical performance of this non-AI medical device are its design specifications, and the "reported device performance" is the statement that it met these specifications through various non-clinical tests.

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

The document describes non-clinical performance data and compliance with standards. It does not mention a "test set" in the context of patient data or algorithm evaluation. Sample sizes for engineering tests (e.g., sterilization validation, biocompatibility) are not specified. Data provenance regarding country of origin or retrospective/prospective studies is not relevant to the described testing, which appears to be laboratory-based validation.

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. The device is a direct viewing instrument, and the testing described is engineering and performance-based, not involving interpretation of images by experts to establish a "ground truth" for diagnostic accuracy of an AI algorithm.

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

Not applicable, as no human expert review or adjudication of a test set for diagnostic performance is 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 MRMC study or evaluation of human reader performance with or without AI assistance was mentioned or conducted, as this is not an AI-powered device.

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

Not applicable, as this is not an AI-powered device.

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

For the engineering and performance testing described, the "ground truth" would be established by the specifications and measurable physical/chemical properties defined by relevant international standards (e.g., ISO, IEC, ASTM) and the device's design. This is not comparable to diagnostic ground truth (e.g., pathology for disease presence).

8. The sample size for the training set

Not applicable, as this is not an AI-powered device. There is no "training set" in the context of machine learning.

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

Not applicable, as this is not an AI-powered device.

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