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MAQUET TRIOP VOLISTA surgical lights are intended to be used to provide visible illumination of the surgical area or the patient during surgical operations, diagnostics and treatment.

MAQUET TRIOP VOLISTA® Surgical Light Systems have been developed in order to provide any operating room with LED technology. An innovative design combined with a functional shape offers an efficient product to the surgical staff. The TRIOP VOLISTA® Surgical Lights are well-suited for installation in surgical suites, examining rooms, doctor's surgeries and external consultations. The TRIOP VOLISTA® product family is composed by two different lightheads, Volista 400 and Volista 600. The System is available on ceiling versions and may be composed by one, two or three lightheads, which can be every possible combination of VOLISTA 400 and VOLISTA 600. Accessories such as integrated cameras and screen supports can be included to the TRIOP VOLISTA® Surgical Light System.

Here's an analysis of the provided text regarding the TRIOP VOLISTA® Surgical Light System, focusing on acceptance criteria and supporting studies:

This submission is for a surgical light system, which is generally a low-risk device. As such, the "acceptance criteria" and "studies" are primarily focused on compliance with recognized performance and safety standards rather than clinical efficacy studies often seen with diagnostic or therapeutic AI/ML devices.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the device (a surgical light), the "acceptance criteria" are embodied in compliance with established national and international standards for medical electrical equipment and surgical luminaires. The reported performance is the device's conformance to these standards.

2. Sample Size Used for the Test Set and Data Provenance

The provided documentation does not detail a "test set" in the context of data-driven AI/ML models. For a physical device like a surgical light, the testing would involve validating prototypes or production units against the specified standards. The sample size would typically refer to the number of physical units tested to ensure design conformance, but this specific information (number of units tested) is not provided in the summary.

Data Provenance: Not applicable in the context of clinical or image-based data for an AI/ML device. The "data" here are the test results from physical evaluations of the device against engineering and safety standards.

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

This question is not applicable to this submission. The "ground truth" for a surgical light is its ability to perform according to established engineering and safety standards. This is typically assessed through technical measurements and testing by qualified engineers and technicians, not clinical experts establishing a ground truth for diagnostic accuracy.

4. Adjudication Method for the Test Set

This question is not applicable to this submission. Adjudication methods like 2+1 or 3+1 are used in studies where there is subjective interpretation (e.g., medical image reading) that requires expert consensus to establish ground truth. For a surgical light system's performance validation against standards, the results are typically objective measurements (e.g., light intensity, electromagnetic emissions, safety features), which do not require a subjective adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, a MRMC comparative effectiveness study was not done.
MRMC studies are relevant for diagnostic or screening devices where human readers interpret medical data, and the study aims to assess the impact of an AI aid on their performance. This surgical light system is a physical device providing illumination, not an interpretive aid for human readers.

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

Not Applicable.
This question refers to the performance of an AI algorithm operating independently. The TRIOP VOLISTA® Surgical Light System is a physical medical device, not an AI algorithm.

7. The Type of Ground Truth Used

For this device, the "ground truth" is defined by the safety and performance requirements outlined in the referenced national and international standards (e.g., UL 60601-1, IEC 60601-2-41, IEC 60601-1, IEC 60601-1-2, FCC Part 15). The device is deemed acceptable if it meets these objective, measurable criteria. It is not based on expert consensus, pathology, or outcomes data related to disease diagnosis or treatment efficacy in the human body.

8. The Sample Size for the Training Set

Not Applicable.
This device is a physical product (surgical light), not an AI/ML system that requires a "training set" of data for algorithm development.

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

Not Applicable.
As there is no AI/ML component or a "training set" for this device, the establishment of ground truth for such a set is irrelevant.

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MAQUET LUCEA LED® Surgical Light Systems are intended to be used to provide visible illumination of the surgical area or the patient during surgical operations, diagnostics and treatment.

MAQUET LUCEA LED® Surgical Light Systems have been developed in order to provide MAQUET EUGEAEED Surgied Lightnology. An innovative design combined with a functional shape offers an efficient product to the surgical staff. functional shape oners an cindical provide hights provide high quality illumination Designed for minor ourgory, without any compromises on the major enhancements offered by MAQUET surgical lights. The LUCEA LED® Surgical Lights are well-suited for installation in surgical suites, examining rooms, doctor's surgeries and external consultations.

Here's an analysis of the provided text regarding the acceptance criteria and study for the LUCEA LED® Surgical Light System:

1. Table of Acceptance Criteria and Reported Device Performance

This 510(k) summary focuses on demonstrating substantial equivalence to predicate devices for a surgical light system. As such, it does not present quantifiable acceptance criteria for specific performance metrics in the way a diagnostic AI device's summary might. Instead, the "acceptance criteria" are implied by conformance to various medical device standards, and the "reported device performance" is the claim of meeting these standards.

2. Sample Size Used for the Test Set and Data Provenance

This document does not describe a "test set" in the context of an AI device's performance evaluation (e.g., a set of images or cases). The testing described is engineering and regulatory compliance testing for a physical medical device (a surgical light). Therefore, concepts like sample size for a test set and data provenance (country of origin, retrospective/prospective) are not applicable in the way they would be for an AI algorithm.

The testing involved evaluating the physical device against the listed standards. The "data provenance" for this type of testing is typically the testing laboratory or manufacturer's internal quality assurance processes.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. For a surgical light system, "ground truth" as it relates to expert consensus on medical findings is not relevant. The device's performance is objectively measured against engineering and electrical safety standards.

4. Adjudication Method

Not applicable. This is not a study requiring adjudication of expert opinions.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, this document does not mention or describe an MRMC comparative effectiveness study. This type of study is typically conducted for diagnostic or AI-powered devices to assess the impact of AI on human reader performance.

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

Not applicable. The LUCEA LED® Surgical Light System is a physical medical device, not an AI algorithm.

7. The Type of Ground Truth Used

The "ground truth" for this device's evaluation is defined by established engineering standards, product specifications, and regulatory requirements. The device's performance is measured against these objective, quantifiable benchmarks. For example, light intensity is measured in lumens, color temperature in Kelvin, and electrical safety against specific resistance and leakage current limits.

8. The Sample Size for the Training Set

Not applicable. The LUCEA LED® Surgical Light System is a physical medical device, not an AI model that requires training data.

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

Not applicable. As described above, there is no AI training set for this device.

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