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The Medrobotics Flex® System is a device that is intended for robot-assisted visualization and surgical site access to the oropharynx, hypopharynx, and larynx in adults (≥ 22 years of age). The Flex System also provides accessory channels for compatible flexible instruments used in surgery.

The Flex® Robotic System is an operator-controlled flexible scope that provides the benefits of both a rigid endoscope and a computer assisted controller. The Flex® Robotic System allows for the scope to be introduced via an operator-controlled user interface easily providing visualization and access of structures within the oropharynx and hypopharynx and larynx. Visualization is provided by a HD 2D/3D digital camera attached at the distal end of the scope. The Flex Robotic System's scope also provides two accessory channels for use of varied flexible instruments.

The provided document does not describe a study proving the device meets specific acceptance criteria in terms of diagnostic performance or clinical outcomes. Instead, it details the verification and validation (V&V) testing performed to demonstrate that the Medrobotics Flex® Robotic System (K170453), a modified version of a previously cleared predicate device, maintains its functional, performance, and safety specifications. The device is a surgical robotic system for visualization and access, not a diagnostic AI.

Therefore, many of the requested categories, such as "reported device performance," "sample size for test set," "number of experts," "adjudication method," "MRMC study," "standalone performance," "type of ground truth," "training set sample size," and "how ground truth for training set was established," are not applicable to the regulatory submission described. The acceptance criteria relate to engineering and safety standards, rather than diagnostic accuracy or clinical effectiveness in the way an AI model for diagnosis would be evaluated.

Here's an overview of the information that is present in relation to acceptance criteria and "studies" (which are primarily engineering and usability tests):

1. Table of Acceptance Criteria and Reported Device Performance

As noted, this device is a surgical robotic system, and its "performance" is assessed against engineering, safety, and functionality standards. There are no performance metrics directly comparable to those for a diagnostic AI (e.g., sensitivity, specificity). The "acceptance criteria" are compliance with established standards, and the "reported device performance" is that it successfully met these standards.

2. Sample Size for Test Set and Data Provenance

• Test Set Sample Size: Not applicable. The "tests" described are engineering, safety, and functionality validations, not evaluations of diagnostic accuracy on a case dataset.
• Data Provenance: Not applicable. The tests are bench tests, usability studies (likely in a simulated environment with human participants, but not using clinical patient data in the typical sense of AI model evaluation), and compliance testing against international standards for medical devices.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

• Number of Experts: The usability/human factors testing involved "representative end users (i.e., surgeons and nurses/technicians)" (p. 5). The exact number is not specified in this summary.
• Qualifications of Experts: "Surgeons and nurses/technicians" are mentioned as representative end-users for usability testing. Further specific qualifications (e.g., years of experience, subspecialty) are not provided in this document.
• Ground Truth: For usability, the "ground truth" would be safe and effective interaction and meeting user needs, based on observation and feedback from these representative users.

4. Adjudication Method for the Test Set

• Not applicable in the context of diagnostic performance. For usability testing, adjudication methods would typically involve observers and analysis of user performance against predefined tasks and error rates, but specific details are not provided beyond the general statement that testing "demonstrated that the Flex® Robotic System design meets the intended user requirements and facilitates safe and effective user interactions."

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, an MRMC comparative effectiveness study was not done. This device is a surgical robotic system for visualization and access, not an AI for image interpretation or diagnosis. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not applicable.

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

• No, standalone performance testing (in the sense of an AI algorithm performing a diagnostic task independently) was not done. The primary function of this device is to assist a human surgeon in performing a procedure.

7. The Type of Ground Truth Used

• For biocompatibility: Compliance with ISO 10993 standards and a rationale for not testing for specific components.
• For sterilization: Validation to a Sterility Assurance Level (SAL) of 10-6 and compliance with relevant ISO/AAMI standards.
• For electrical safety and EMC: Compliance with IEC standards.
• For usability: Safe and effective user interaction as observed and assessed by human factors experts (not detailed).
• For functional and performance testing: Meeting internal design specifications and comparing favorably to the predicate device.
• No clinical ground truth (e.g., pathology, outcomes data) was used in the context of diagnostic accuracy, as this is not a diagnostic device.

8. The Sample Size for the Training Set

• Not applicable. This document describes a robotic surgical system, not a machine learning algorithm that requires a "training set" in the conventional sense. The "training" for the system refers to its design and engineering iterations, not data-driven model training.

9. How the Ground Truth for the Training Set Was Established

• Not applicable, as there is no "training set" for an AI model in this context.

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The Medrobotics Flex Robotic System is intended to provide robot-assisted control of the Flex Colorectal Drive during visualization of and surgical site access to the anus, recturn and distal colon. The Flex Robotic System is intended for use in adults (≥22 years of age).

The Flex Colorectal Drive is intended for robot-assisted visualization of and surgical site access to the anus, rectum, and distal colon in adults (≥22 years of age). The Flex Colorectal Drive also provides accessory channels for compatible flexible instruments used in surgery.

The Medrobotics Flex Robotic System is an operator controlled flexible scope that include the benefits of both a rigid scope and a computer assisted controller. This allows for the Flex Colorectal Drive to be introduced via an operator controlled user interface, easily providing transanal access to the anus, rectum and distal colon. Visualization is provided by a user selectable 2D or 3D HD camera incorporated in distal end of the scope. The Flex Robotic System's scope also provides accessory channels for the use of varied flexible surgical instruments.

This document is a 510(k) summary for the Medrobotics Flex Robotic System. It details the device, its intended use, and substantial equivalence to a predicate device, but does not contain specific acceptance criteria or a study proving the device meets those criteria in the context of clinical performance or diagnostic accuracy. Instead, it focuses on general performance, safety, and regulatory compliance.

Therefore, many of the requested details about acceptance criteria, study design, and ground truth establishment cannot be extracted from this document as they are not present.

Based solely on the provided text, here is what can be extracted:

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

The document mentions that the device "has been successfully tested for function, performance, and safety as per FDA recognized Standards" and "met acceptance criteria." However, it does not provide a table of specific acceptance criteria or the numerical performance results against those criteria. It lists only the categories of testing performed.

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

This information is not provided in the document. The document refers to engineering and biocompatibility testing, not clinical performance studies with patient data.

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 in the document. This type of information would be relevant for studies evaluating diagnostic accuracy or clinical outcomes, which are not detailed here.

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

This information is not provided in the document.

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 information is not provided in the document. The device is a robot-assisted surgical system, not an AI-powered diagnostic tool, so an MRMC study comparing human readers with and without AI assistance is not applicable in this context and is not mentioned.

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

This information is not provided in the document. As a robot-assisted system, it inherently involves human operators, so a standalone algorithm performance without human-in-the-loop would not be applicable in the sense of a diagnostic algorithm.

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

For the mechanical, electrical, and materials testing mentioned, the "ground truth" would be established by reference to engineering specifications, validated test methods, and regulatory standards (e.g., IEC 60601-1, ISO 10993-1). The document doesn't detail what specific performance metrics were used as "ground truth" but implies compliance with these standards. For sterility, the ground truth is a Sterility Assurance Level (SAL) of 10-6.

8. The sample size for the training set

This information is not provided in the document. "Training set" is typically relevant for machine learning or AI models, which is not the focus of the testing described here.

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

This information is not provided in the document.

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