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The TyTron C-3000 is indicated for use in detecting, measuring, recording, and graphing the skin surface temperature on both sides of the spine of adults and children by professional health care providers. It is to be used before and after therapy is rendered as part of monitoring the patient's response to care.

The TyTron C-3000 is a hand held paraspinal thermographic scanning system. It consists of two probes which each contain infrared sensors designed to be placed on either side of the spinal column to detect temperatures. With the aid of a computer assisted program the system is capable of recording and graphing thermal differentials (right to left thermal asymmetries) on a horizontal scale. It also records direct temperatures on the vertical scale.

Here's an analysis of the provided information regarding the TyTron C-3000, addressing your specific questions:

Acceptance Criteria and Study for TyTron C-3000

The provided document describes the TyTron C-3000, a hand-held paraspinal thermographic scanning system, and compares it to a predicate device, Thermatrax, to establish substantial equivalence. The "study" in this context is a technical comparison of device specifications to demonstrate that the TyTron C-3000 performs as well as or better than the predicate device for specified technical parameters. This is not a clinical trial in the sense of patient outcomes or diagnostic effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the performance characteristics of the predicate device, Thermatrax. The TyTron C-3000 demonstrates performance that meets or exceeds these criteria.

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

The document does not describe a clinical "test set" in the traditional sense of patient data. The "testing" referred to is a comparison of technical specifications against a predicate device. Therefore, a sample size for a test set and data provenance (country of origin, retrospective/prospective) are not applicable in this context. The testing is focused on the inherent device characteristics and performance.

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

This information is not provided. Given that the "study" is a technical comparison of device specifications, it's unlikely that external experts were used to establish a "ground truth" for a test set in the way one would for diagnostic imaging. The performance parameters (accuracy, sensitivity, reproducibility) would typically be verified by engineering and quality control processes.

4. Adjudication Method

Not applicable. There is no mention of a human-adjudicated test set or a need for an adjudication method like 2+1 or 3+1.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. This document outlines a 510(k) submission for substantial equivalence based on technical specifications and indications for use, not a clinical effectiveness study involving human readers and interpretation. Therefore, an effect size of human readers improving 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

Yes, in a way. The performance metrics of the device (accuracy, sensitivity, reproducibility) are standalone measures of the device's technical capabilities without inherent human-in-the-loop performance measurement. The device itself performs the sensing and data recording. The "algorithm" here is the device's inherent measurement and processing capability.

7. The Type of Ground Truth Used

The "ground truth" for the technical specifications is the established engineering standards for measurement accuracy, sensitivity, and reproducibility. These are fundamental physical properties and performance characteristics of the sensors and the system as a whole, typically verified through calibration and controlled laboratory testing. The predicate device's specifications serve as the benchmark for substantial equivalence.

8. The Sample Size for the Training Set

Not applicable. The TyTron C-3000 is a measurement device, not an AI/machine learning algorithm requiring a separate training set of data. Its performance is based on its physical design, sensor technology, and electronic processing, all of which are established during the device's design and manufacturing process, not through statistical learning from a training dataset.

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

Not applicable, as there is no training set in the context of an AI/machine learning algorithm. The "truth" for the device's design and engineering would be based on fundamental physics, sensor calibration standards, and established electronic design principles.

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