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The Microlife Digital Electronic Thermometer MT16K1 is used for the intermittent measurement and monitoring of human body temperature, oral, rectal and under the arm. The device is for the adult and pediatric population.

This Digital Electronic Thermometer enables easy and highly accurate readings over the body temperature range and has been designed to provide maximum user-friendliness. The body temperature is measured by the thermistor and displayed as numbers on the LCD (liquid crystal display) through microprocessor of IC.

The basic principle of this thermometer is that change of thermistor resistance, caused by changes of temperature, are converted to changes of frequency of R-C oscillator circuit, Therefore, temperature can be given by measuring the frequency of oscillator.

For a given time period, by applying the R-C oscillator circuit, changes of temperature will correspond to changes of pulse number.

The provided document is a 510(k) summary for the Microlife Digital Electronic Thermometer, Model MT16K1. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed acceptance criteria and a robust study report as might be seen for novel AI/software devices. Therefore, some of the requested information, particularly regarding AI-specific criteria like multi-reader multi-case studies, ground truth establishment for training data, and expert qualifications, is not applicable or not present in this document.

Here's an analysis based on the provided text:

Acceptance Criteria and Reported Device Performance

The core of the acceptance criteria is compliance with recognized voluntary standards for clinical electronic thermometers and a demonstration of substantial equivalence to a predicate device.

Note: The document does not provide specific numerical thresholds for "acceptance" for clinical bias, uncertainty, or repeatability, only that they were evaluated according to a standard.

Study Details

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated in the provided summary. The document mentions "Controlled human clinical studies were conducted in accordance with ASTM E1965-03," but does not give the number of subjects or measurements.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The studies appear to be prospective clinical studies as they are described as "controlled human clinical studies" using the device.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable/Not mentioned. For a digital electronic thermometer, the ground truth for temperature is typically established using a reference standard thermometer, not expert consensus as would be the case for image interpretation.

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

   • Not applicable/Not mentioned. Adjudication methods are typically used when subjective interpretations are involved, such as in image reading. For a medical device measuring a physical parameter, the "truth" is typically determined by a reference measurement instrument.

4. 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 device is a digital electronic thermometer, not an AI-powered diagnostic imaging tool that would involve human readers and AI assistance.

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

   • Yes, in essence. The study evaluates the device's performance (temperature measurement algorithm) directly against a reference standard. The "algorithm only" performance is the core of the evaluation for this type of device.

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

   • The ground truth for temperature measurement would be established by a highly accurate and calibrated reference thermometer or established methods for taking body temperature, in accordance with standards like ASTM E1965-03. This is an objective measurement rather than expert consensus or pathology.

7. The sample size for the training set:

   • Not applicable/Not mentioned. For a simple electronic thermometer, there isn't typically a "training set" in the machine learning sense. The device's algorithm for converting thermistor resistance to temperature is based on physical principles and calibration, not learned from a dataset.

8. How the ground truth for the training set was established:

   • Not applicable. As there's no training set in the AI sense, there's no ground truth established for one. The device is calibrated and its algorithm is fixed.

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