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The Linemaster wireless foot switch is an accessory that is indicated for use to provide foot switch input control to any medical device that uses a wired foot switch with a switch closure (on/off), to activate said device.

The system is intended for use in hospitals, outpatient clinics and physicians offices.

The Linemaster wireless foot switch will be an accessory to and provide foot switch input control to any medical device that uses a foot switch to control on/off inputs. The system includes a wireless foot switch and a receiver. The wireless foot switches share the same transmission schemes, however each manufacturer will have a unique identification code that is programmed into the transmitter and receiver. This identification code will only allow communication of systems that have been programmed with the same identification code. Systems from different manufacturers will not communicate with each other.

When coupled with a system that uses a wired foot switch the wireless system will function in the same way as the wired version. Apart from the software, the technological characteristics are the same or similar to, those of the predicate wired device where LED-based systems and infrared signals are used to operate the device.

The elimination of wires on the floor of the operating room will improve the safety and efficiency by uncluttering the OR floor and reducing set-up and cleanup time.

The provided text describes the Linemaster IR Wireless Foot Switch and its conformity to various safety and performance standards. However, it does not detail specific acceptance criteria or a study proving the device meets those criteria in the way typically expected for an AI/ML device.

It's crucial to understand that this document is a 510(k) Summary for a non-AI/ML medical device (a wireless foot switch). Therefore, many of the requested categories for AI/ML device evaluation (like sample size for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) are not applicable and thus not present in this type of submission.

Here's a breakdown of the available information based on your request, highlighting where the document's content differs from what would be expected for an AI/ML device:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of "Study":
For this device, the "study" is the verification and validation testing performed to ensure compliance with the listed national and international consensus standards for electrical medical equipment and electromagnetic compatibility. This is a standard process for hardware devices, not an AI/ML performance study.

The following questions are not applicable or cannot be answered from the provided text, as they pertain to AI/ML device evaluation criteria which are not relevant to a physical wireless foot switch submission:

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. There isn't a "test set" in the context of data for an AI/ML model. The testing involved physical device performance against engineering and safety standards.

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. "Ground truth" for an AI/ML model is not relevant here. Compliance with standards is typically assessed by trained engineers and certified testing laboratories.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. There is no "adjudication method" in the context of data labeling for AI/ML.

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 is a hardware device; it does not "assist human readers" in interpreting data or images.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. This is a physical device that functions as an accessory. Its performance is inherent to its design and manufacturing, not an algorithm's output.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable. "Ground truth" in the AI/ML sense is not relevant. The "truth" here is adherence to specified engineering and safety parameters as defined by the standards.

8. The sample size for the training set
Not applicable. There is no "training set" for an AI/ML model.

9. How the ground truth for the training set was established
Not applicable. There is no "training set" or corresponding ground truth establishment process for a physical foot switch.

In summary, the provided document is a 510(k) summary for a traditional medical device, demonstrating its safety and effectiveness through compliance with established engineering and safety standards. It does not involve AI/ML technology, and therefore, the specific criteria for evaluating AI/ML models are not addressed.

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