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The HOPKINS Telescopes when used with sheaths are intended to be used to permit viewing of the cervical and uterine cavity for the purpose of performing diagnostic and surgical procedures.

The HOPKINS Telescopes are rigid telescopes that utilize the rod lens technology. At the distal end of the telescope's shaft is the lens and the other end of the shaft is attached to the eyepiece. Throughout the central lumen of the HOPKINS Telescopes, optical glass rods are used to transmit and magnify the image received from the lens. The HOPKINS Telescopes are available with 0°, 12° and 30° direction of view, 2mm, 2.9mm, 4mm diameter and 26cm, 30cm, 36cm working lengths.

The provided text is a 510(k) Summary for the HOPKINS Telescopes, a medical device. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study of its own performance against pre-defined acceptance criteria for a novel AI/software component.

Therefore, the provided document does not contain the information required to answer your request regarding acceptance criteria and a study proving the device meets those criteria, specifically concerning AI/software performance.

The document states:

• "Clinical studies were not required to demonstrate substantial equivalence to the predicate device." (Page 9)
• "The conclusions drawn from the nonclinical test demonstrate that the subject device is as safe and effective as the predicate device to support a substantial equivalence determination." (Page 9)

This means the submission relies on bench testing and comparison of technological characteristics to a legally marketed predicate device, not on a clinical performance study with defined acceptance criteria and human readers (or AI algorithms) in the way your prompt describes.

To directly answer your questions based only on the provided text, the answer is that this information is not present for the HOPKINS Telescopes device.

If this were an AI/software device and the information was present, here's how I would answer each point:

1. Table of acceptance criteria and reported device performance: This would be a table detailing metrics like sensitivity, specificity, AUC, or agreement rates, with specific target thresholds (acceptance criteria) and the actual performance achieved in the study.
2. Sample size and data provenance: This would specify the number of cases/patients in the test set (distinct from training data), whether the data was collected retrospectively or prospectively, and from which countries or institutions.
3. Number of experts and qualifications: This would state how many experts (e.g., radiologists, pathologists) were involved in establishing ground truth and their relevant experience (e.g., board-certified, years of experience, subspecialty).
4. Adjudication method: This would describe how discordant readings or interpretations among experts were resolved (e.g., 2+1 means two initial readers, with a third if they disagree; 3+1 means three initial readers, with a fourth for adjudication if necessary).
5. Multi-reader multi-case (MRMC) comparative effectiveness study: If conducted, this would provide details on how the AI system impacted human reader performance, including statistical metrics like the effect size (e.g., mean increase in AUC, sensitivity, or specificity when AI assistance was used compared to no AI assistance).
6. Standalone performance: This would report the performance of the algorithm itself, without any human interaction, against the ground truth.
7. Type of ground truth: This would specify the reference standard used (e.g., expert consensus, pathology results, follow-up clinical outcomes, surgical findings).
8. Sample size for training set: This would state the number of unique cases/patients used to train the AI model.
9. Ground truth for training set: This would explain the methodology used to label or establish the ground truth for the data utilized during the training phase of the algorithm.

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For telescopes with diameter ranging from 3mm-5mm
The HOPKINS Telescopes are intended to provide visualization during laparoscopy and general surgery in adults and pediatrics.
For telescopes with diameter ranging from 5.5mm- 11mm
The HOPKINS Telescopes are intended to provide visualization during laparoscopy and general surgery in adults.

The HOPKINS Telescopes are rigid telescopes that utilize the rod lens technology. At the distal end of the telescope's shaft is the lens and the other end of the shaft is attached to the eyepiece. Throughout the central lumen of the HOPKINS Telescopes, optical glass rods are used to transmit and magnify the image received from the lens. The HOPKINS Telescopes are available with 0°, 6°, 30° and 45° direction of view, diameters ranging from 3mm to 11mm and working lengths from 18cm- 50cm.

The provided text describes the submission of a 510(k) summary for the HOPKINS Telescopes, comparing it to a predicate device. It details non-clinical performance data and references published literature for clinical performance, but it does not contain information about acceptance criteria or a specific study proving the device meets those criteria, especially in the context of an AI/ML device.

The device described, "HOPKINS Telescopes," is a rigid endoscope that utilizes rod lens technology for visualization during surgery. There is no mention of AI or machine learning components. Therefore, many of the requested points, such as sample sizes for test/training sets, expert ground truth adjudication, MRMC studies, and standalone AI performance, are not applicable to the information provided in this document.

However, based on the non-clinical performance data section, we can infer some "acceptance criteria" related to standards compliance and general performance.

Here's an attempt to answer the questions based only on the provided text, recognizing that it does not pertain to AI/ML device performance:

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

Since this is not an AI/ML device, the "acceptance criteria" are related to compliance with recognized standards and successful bench testing. The document states:
"the HOPKINS Telescopes has met all its design specification and is substantially equivalent to its predicate device."

2. Sample size used for the test set and the data provenance

The document does not specify a "test set" in the context of data for an AI/ML model. The evaluation involves non-clinical bench testing and comparison to a predicate device. No specific sample sizes for these tests are mentioned, nor is there information about data provenance (country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This is not applicable as the device is a physical endoscope, not an AI/ML diagnostic tool requiring expert ground truth for image interpretation.

4. Adjudication method for the test set

Not applicable.

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, as this is not an AI-assisted 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/ML algorithm.

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

For the non-clinical testing, the "ground truth" would be established by physical measurements, chemical analyses, and adherence to the specified performance parameters outlined in the referenced standards. For the pediatrics indication expansion, the ground truth is based on "published literature" supporting safety and effectiveness, which would implicitly rely on clinical data outcomes.

8. The sample size for the training set

Not applicable, as this is not an AI/ML device with a training set.

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

Not applicable.

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The HOPKINS Telescopes are intended to provide visualization during laparoscopy and general surgery in adults and pediatrics.

The HOPKINS Telescopes are rigid telescopes that utilize the rod lens technology. At the distal end of the telescope's shaft is the lens and the other end of the shaft is attached to the eyepiece. Throughout the central lumen of the HOPKINS Telescopes, optical glass rods are used to transmit and magnify the image received from the lens. The HOPKINS Telescopes are available with 0°, 25° and 45° direction of view, 5mm diameter and 29cm working lengths.

The provided text is a 510(k) summary for the KARL STORZ HOPKINS Telescopes. This document is a premarket notification for a medical device to demonstrate substantial equivalence to a legally marketed predicate device. As such, it focuses on comparing the new device to an existing one based on technical characteristics, rather than establishing acceptance criteria and proving performance through a clinical study for an AI/ML-driven device.

The document discusses the physical and optical characteristics of the endoscope, reprocessing methods, and non-clinical performance data (e.g., adherence to ISO standards, biocompatibility, thermal safety, reprocessing validation). It explicitly states: "There are no performance standards or special controls developed under Section 514 of the FD&C Act for endoscopes." This indicates that the regulatory pathway for this device does not require the kind of performance validation typically seen for AI/ML devices, which would involve acceptance criteria for metrics like sensitivity, specificity, or AUC, and detailed clinical study designs.

Therefore, the document does not contain the information required to answer the prompt questions regarding acceptance criteria for an AI/ML device. The HOPKINS Telescopes are a traditional optical medical device, not an AI/ML product.

To reiterate, the provided text does not contain any information about:

1. Acceptance criteria or reported device performance for AI/ML metrics.
2. Sample sizes for test sets or data provenance (as it's not an AI/ML study).
3. Number and qualifications of experts for ground truth establishment.
4. Adjudication methods.
5. MRMC comparative effectiveness studies or effect sizes.
6. Standalone algorithm performance studies.
7. Type of ground truth (as it's not a diagnostic AI/ML device).
8. Sample size for training sets (no AI/ML training is mentioned).
9. How ground truth for a training set was established.

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