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The Auris Robotic Endoscopy System (ARES) is intended to provide bronchoscopic visualization of patient airways.

The Auris Robotic Endoscopy System (ARES) is intended to be used by qualified physicians to provide visualization to the Bronchial Tree during Bronchoscopic procedures. The ARES consists of four major components, (1) the Patient Side System (PSS), (2) Controller Cart, (3) Surgeon Console (also known as the Master Device Workstation) and (4) the Bronchoscope and Accessories.

The Patient Side System (PSS) includes the robot cart, two robot arms, both Endoscope and Sheath IDMs, IDS. servo drives box, Endoscope camera control box, power control box. illumination controller, and necessary cabling between the IDM/IDS and the robot cart.

The Controller Cart houses the electronic systems required to power and operate the robotic systems. The Controller Cart is broken into two smaller carts, the system controller cart, and the arm controller cart.

The Master Device Workstation is the workstation from which the surgeon drives the ARES. The console consists of a pendant that allows the surgeon to control various aspects of the system during a procedure.

The system is based on a master - slave model, where the user (i.e. physician) is controlling the robots (slaves) using a pendant (master). The flexible bronchoscope is attached at the end effector of a robotic arm with multiple degrees of freedom. The flexible bronchoscope has a working channel and a camera at the tip. The Bronchoscope has an articulated tip that can bend in four directions. The working channel of the Bronchoscope is used for irrigation and aspiration.

Each Slave includes a robotic arm with 6 degrees of freedom and an IDM (Instrument Drive Mechanism) with 4 actuated axes. The robotic arms are used to steer the Bronchoscope.

The provided text is a 510(k) Premarket Notification for the Auris Robotic Endoscopy System (ARES). It details the device's indications for use, comparison to predicate devices, and testing completed. However, it does not contain specific acceptance criteria, reported device performance data, information about sample sizes for test sets, data provenance, number or qualifications of experts, adjudication methods, MRMC studies, standalone performance data, or details about the training set.

The document focuses on demonstrating substantial equivalence to predicate devices through technical comparisons and compliance with relevant medical device standards.

Therefore, I cannot fulfill the request to describe the acceptance criteria and study proving the device meets them with the level of detail requested, as that information is not present in the provided text.

Here is what can be extracted or inferred from the text related to your request, with significant limitations:

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

This information is not explicitly stated in the document. The document mentions "Preclinical testing included standard bench or in vitro testing confirming functionality and durability (e.g., tensile and other durability and functional evaluation)." and "Verification and validation testing was completed in compliance with the following standards: ... All clinical input requirements were validated." These statements imply that acceptance criteria were met, but the specific criteria and corresponding performance results are not provided.

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

• Sample Size for Test Set: Not specified for bench or in vitro testing.
• Data Provenance: The document states that a "Porcine animal model was used to validate system performance in vivo." This indicates some in vivo testing was performed in an animal model, but no details on the number of animals or the country of origin are provided. The broader preclinical testing would be considered prospective for the device's development.

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):

This information is not provided. Given the nature of the preclinical and animal model testing described, "ground truth" would likely be established by veterinary pathologists or engineers, but no specifics are given.

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

This information is not provided.

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:

This device is not an AI/CAD-based diagnostic system. It is a robotic endoscopy system for visualization. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance would not be applicable to this type of device and is not mentioned or implied.

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

This device is a robotic system that is explicitly described as a "master - slave model, where the user (i.e. physician) is controlling the robots (slaves) using a pendant (master)." Therefore, it is inherently a "human-in-the-loop" device, and a standalone algorithm-only performance study would not be applicable. Its performance is tied directly to the physician's control.

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

For the "in vivo" validation mentioned, the ground truth would likely be based on direct observation, procedural success (e.g., successful navigation, visualization), and potentially post-procedure pathology or findings in the porcine model. However, the document does not specify the exact nature of the ground truth. For the in vitro testing, ground truth would be based on engineering measurements and specifications.

8. The sample size for the training set:

The document mentions "Preclinical testing" and "Verification and validation testing." These types of studies are typically performed after development (which includes training/optimization) is complete. The document does not provide any information about a "training set" or how the device's control algorithms or visual processing might have been "trained." This is not an AI/machine learning device in the context of typical training sets.

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

As no training set is described, this information is not provided.

In summary, the provided document details the regulatory submission (510(k)) for a robotic endoscopy system, focusing on its substantial equivalence to predicate devices and compliance with safety and performance standards. It broadly states that "clinical input requirements were validated" and "Preclinical testing included standard bench or in vitro testing confirming functionality and durability" and an "animal model was used to validate system performance in vivo." However, it does not provide the specific quantitative acceptance criteria or detailed study results for these tests that would address most of the points in your request.

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