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A ViTA Agil IT101 Instant Digital Thermometer is used for the intermittent measurement and monitoring of human body temperature , orally, rectally and under the arm, by consumers in the home.

AviTA Agil IT101 Instant Digital Thermometer is a hand-held, non-sterile, reusable clinical thermometer intended for the determination of human temperature in either rapid mode (4-seconds orally, 6-seconds rectally and under the arm -- Predicative Temperature), or standard mode(actual determination of temperature),

AVITA Agil IT101 Instant Digital Thermometer is a hand-held, non-sterile, reusable clinical thermometer and is designed to instantly take the body temperature orally, rectally or under the arm. With its rapidity and precisemenss, Agil IT101 gives a temperature reading in merely 4 seconds orally and 6 seconds rectally and under the arm respectively.

The provided 510(k) summary for the AVITA Agil IT101 Instant Digital Thermometer indicates that the device was evaluated based on compliance with established standards, but it does not include a detailed study proving the device meets specific acceptance criteria for accuracy and precision in the way typically expected for AI/ML-driven devices or more complex medical instruments.

Instead, the performance summary highlights conformity to general medical device standards.

Here's an attempt to extract and present the requested information based solely on the provided text, noting where information is missing for a detailed AI/ML-type study.

Acceptance Criteria and Reported Device Performance

The submission states conformity to various standards as its performance summary, implying these standards contain the acceptance criteria for a digital thermometer. Specific numerical performance criteria (e.g., accuracy range, precision) are not detailed in the provided text.

Note: The specific numerical tolerance for temperature accuracy as per these standards (e.g., ±0.1°C) is not provided in the summary. The acceptance criteria are implicitly met by "conforms to" the standards.

Study Details

The submission mentions "bench testing contained in this submission and clinical testing supplied" but does not provide details about these studies. Therefore, most of the requested information regarding the study design is not available in the provided text.

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

   • Test Set Size: Not specified in the provided document.
   • Data Provenance: Not specified in the provided document.

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

   • Not applicable/Not specified. For a simple digital thermometer, ground truth typically refers to a reference thermometer reading rather than expert interpretation of images or other complex data.

3. Adjudication method for the test set:

   • Not applicable/Not specified. Ground truth establishment for a thermometer's accuracy usually involves comparing readings to a highly accurate reference standard, not a consensus among experts, unless the "clinical testing" involved something more complex, which is not described.

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 is not an AI/ML device subject to MRMC studies.

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

   • Not applicable. This is a standalone device in the sense that it provides a temperature reading, but it does not employ an algorithm that would typically be evaluated in the context of AI/ML or image interpretation. Its "algorithm" is likely the predictive logic for rapid mode temperature.

6. The type of ground truth used:

   • Likely involved a comparison to a highly accurate reference thermometer in a controlled environment, as is standard for thermometer validation, but explicitly not stated in the document.

7. The sample size for the training set:

   • Not applicable. This device does not use machine learning algorithms that require a "training set" in the conventional sense. The "predictive" aspect likely comes from engineering and calibration based on known physiological temperature curves, not data-driven machine learning.

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

   • Not applicable, as there is no mention of a training set for machine learning.

Conclusion based on provided text:

The provided 510(k) summary focuses on demonstrating substantial equivalence to a predicate device and compliance with relevant medical device standards (ASTM, PrEN, IEC) for safety and electromagnetic compatibility. It mentions "bench testing" and "clinical testing" but does not detail the specific methodologies, sample sizes, or ground truth establishment processes for these tests in a way that would answer the requested questions for a typical AI/ML device study. For a digital thermometer, the "study" demonstrating performance would primarily involve calibration and accuracy tests against a known standard under various conditions defined by the cited standards.

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