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The Patcom Distal Chip Endoscope is indicated when endoscopic visualization in the regions of mouth, nasal cavity, and upper airway is required.

The PatCom Distal Chip Endoscope is a portable video endoscope for visualization in the reqions of the mouth, nasal cavity, and upper airway. It is made up of the grip section and insertion tube. It requires connection via USB to a laptop computer, and gains power from the USB ports which supplies 5V of electricity with a maximum current of 0.5A. This is sufficient to power the endoscope. The endoscope does not need an external CCU for image processing, as the processing is done directly on the device on a small chip. It converts the light and color information received by the image sensor into binary numbers (patterns of zeros and ones). The USB Video Class (UVC) driver is a Microsoft-provided AVStream minidriver that provides driver support for USB Video Class devices. When a device uses UVC, it does not need to support their own driver, but instead works automatically with the system-supplied driver. It is compatible to all Windows and Mac computers, with a simple plug and play feature. The visualization from the distal chip is projected onto the computer monitor, at a resolution of 720px1280p high definition.

The insertion tube portion of the device is used for visualization within the natural orifices of the mouth and the nasal cavities and in the upper airway anatomy. The light emitted by the LED light source at the distal end of the tip is illuminated into the body cavity of the subject, and the image is displayed onto a screen.

The grip section of the device contains a control knob for moving the distal end of the insertion tube bend up or down, up to 130 degrees in either direction.

The proximal end of the grip section contains the USB connector, for plug and play with a computer, for power and to provide visualization of the camera chip found at the distal end of the insertion tube.

The provided text is a 510(k) summary for the "PatCom Distal Chip Endoscope," detailing its substantial equivalence to a predicate device. It primarily focuses on comparing the new device's technical specifications, materials, and performance characteristics to those of the predicate device, rather than presenting a study proving the device meets specific acceptance criteria for a new AI/algorithm system.

The document discusses performance characteristics from an engineering perspective (e.g., field of view, depth of field, noise, latency) and states that "extensive comparative imaging testing" was done against the predicate device to show substantial equivalence. However, it does not describe an AI/algorithm-based study with acceptance criteria related to clinical performance metrics like sensitivity, specificity, reader improvement, or expert adjudication. Therefore, most of the requested information regarding AI/algorithm study specifics (MRMC, standalone performance, ground truth establishment, sample sizes for training/test sets, expert qualifications, and adjudication methods) cannot be extracted from this document.

Here's what can be extracted and what is not available based on the provided text:

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

The document lists various performance testing categories and applicable standards, implying that the device was tested against criteria outlined in these standards and found to meet them, thereby supporting substantial equivalence to the predicate. However, it does not provide a specific table of acceptance criteria as clinical performance targets (e.g., minimum sensitivity or specificity) for an AI/algorithm, nor exact numerical results for each reported aspect explicitly stated as "device performance." Instead, it generally states that tests "support both devices to be substantially equivalent."

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

The document describes "extensive comparative imaging testing" against a predicate device. This is bench testing, not a clinical study on patient data. Therefore, there is no "test set" in the context of patient data, nor data provenance (country of origin, retrospective/prospective). The "sample size" would refer to the number of devices or test conditions, which is not specified.

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

Not applicable. This was not a study involving human interpretation of images to establish ground truth for clinical cases. The performance evaluation was technical bench testing of the endoscope's imaging capabilities.

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

Not applicable, as no human readers or clinical ground truth adjudication were involved in the described tests.

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. The document explicitly describes bench testing to demonstrate substantial equivalence of a medical imagery device (endoscope), not an AI algorithm. Therefore, no MRMC study or AI assistance effect size is mentioned.

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

No. This document is about an endoscope, not an AI algorithm.

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

For the imaging performance, the "ground truth" would be the known physical properties and measurements from the test targets and laboratory setups used for the imaging evaluations (e.g., resolution charts, color targets). For biocompatibility, it's the results from the specific ISO 10993 tests themselves. For electrical safety, it's compliance with standard electrical safety measurements. These are not clinical ground truths (e.g., pathology, outcomes).

8. The sample size for the training set

Not applicable. There is no AI/algorithm training set mentioned.

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

Not applicable. There is no AI/algorithm training set mentioned.

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