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The IDSS-ForceTriad Control Module is designed for integration in the IDSS(Integrated Digital Surgical Suite) and enables the Covidien ForceTriad Energy Platform to be controlled remotely.

The IDSS ForceTriad Control Module is an additional function to the IDSS, which is an integrated operating room system controlling video displays, observation cameras, audio video equipment, teleconferencing and the routing of video and images from multiple sources to multiple destinations via a touch screen interface. With the IDSS ForceTriad Control Module, operation room staff is able to control the ForceTriad setup from the touch panel location rather than using the ForceTriad unit itself.

The provided FDA 510(k) summary (K153205) describes the IDSS-ForceTriad Control Module. This device is an accessory that enables remote control of the Covidien ForceTriad Energy Platform within an Integrated Digital Surgical Suite (IDSS). The primary purpose of this submission is to demonstrate substantial equivalence to a predicate device, not to showcase the performance of an AI/ML algorithm that interprets medical images or data.

Therefore, many of the requested criteria regarding AI/ML performance studies, such as effect size of human readers with AI assistance, standalone algorithm performance, number of experts for ground truth, and training set information, are not applicable to this document.

Here's an analysis of the provided information, focusing on the context of a medical device accessory and its validation:

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

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

Not applicable. This device is a control module, not a diagnostic or AI image analysis system that would typically use a "test set" of medical images or patient data. The testing is focused on software functionality and risk management.

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)

Not applicable. As noted above, this is a control module, not an AI/ML system requiring expert-adjudicated ground truth for medical data.

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

Not applicable. This is not an AI/ML system requiring adjudication of medical cases.

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. This filing concerns a control module for an energy platform, not an AI-assisted diagnostic or imaging device. There is no mention of human readers or AI assistance in the context of clinical interpretation.

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

Not applicable. This is not an AI algorithm. Its function is to remotely control an existing medical device.

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

For the software validation and risk analysis, the "ground truth" would be related to the correct and expected functionality of the software according to its design specifications, user requirements, and safety standards (e.g., ISO 14971:2007). This is established through internal design documents, functional specifications, and compliance with regulatory standards, rather than clinical outcomes or expert consensus on medical findings.

8. The sample size for the training set

Not applicable. This is not an AI device that involves a "training set" in the machine learning sense.

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

Not applicable. This is not an AI device.

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IDSS SLC allows the control of surgical lighting during a surgical procedure from the IDSS, which is a centralized interface for voice, video, audio, observational camera and teleconferencing for the operating room.

IDSS SLC is an additional function of the IDSS, which is an integrated operating room system that controls video displays, observation cameras, audio video equipment, teleconferencing and the routing of videos and images from multiple sources to multiple destination via touch screen interface.

This document, a 510(k) Summary for the IDSS SLC device, does not contain a study proving the device meets acceptance criteria as typically understood for AI/ML medical devices.

The IDSS SLC is described as an additional function of an integrated operating room system that controls surgical lighting, video displays, observation cameras, audio/video equipment, and teleconferencing. Its primary function is to allow control of surgical lighting during a procedure from a centralized interface.

Instead of a typical performance study with acceptance criteria for an AI/ML diagnostic or prognostic device, this document describes a summary of nonclinical testing focused on the device's functional integrity, safety, and equivalence to predicate devices.

Here's a breakdown of the information that can be extracted, and what is not applicable (N/A) given the nature of this device and document:

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

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

• N/A. This document describes nonclinical engineering and safety testing of a control system, not a study involving patient data or a test set in the traditional sense for AI/ML performance evaluation. The "test set" would be the device itself and its components undergoing various engineering validation tests.

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)

• N/A. Ground truth for clinical interpretation or diagnostic accuracy is not relevant here. The "ground truth" for this device's performance would be whether it accurately implements its programmed controls, adheres to safety standards, and functions reliably. This would be established by qualified engineers and testers.

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

• N/A. Adjudication methods like 2+1 or 3+1 are used for resolving disagreements among human readers in interpreting clinical data. This is not relevant for the type of testing 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

• N/A. The IDSS SLC is a control system for surgical lights and other OR equipment. It is not an AI-assisted diagnostic or interpretative device that would be subject to an MRMC study.

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

• N/A. Not an AI algorithm in the context of clinical interpretation. The "standalone" performance would refer to the IDSS SLC system's ability to operate its functions as designed, which is covered by the validation testing.

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

• The "ground truth" for this device's performance is its conformance to engineering specifications, functional requirements, and established safety standards (e.g., EN 60601-1). This ground truth is based on predefined technical standards and the expected behavior of a physical control system.

8. The sample size for the training set

• N/A. This device does not use a training set for machine learning. The "training" for such a system would involve software development, component testing, and integration testing, which are distinct from AI model training.

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

• N/A. As there is no training set mentioned in the context of AI/ML, this question is not applicable.

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