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The Braun ThermoScan IRT 3020/3520 One Second Ear Thermometer is intended for the intermittent measurement and monitoring of human body temperature by consumers in the home. It is intended for use on people of all ages.

The Braun Thermoscan IRT 3020/3520 Thermometer is a hand held instrument that measures temperature through the opening of the auditory canal. Operation is based on measuring the natural thermal radiation emitted from the tympanic membrane and adjacent surfaces.

Here's a breakdown of the acceptance criteria and study information for the Braun Thermoscan IRT 3020/3520 Thermometer, based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance:

Note on Accuracy: The summary explicitly states that the device "meets the requirements established in ASTM Standard E1965-98" for infrared thermometers, which requires ± 0.2°C (± 0.4°F) in the 36°C to 39°C range. The provided device performance directly matches this.

Study Details

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

• Sample Size: Not explicitly stated for the clinical comparison study and clinical repeatability testing. The document only mentions that the study was performed on four age groups: 0-3 yrs, 4-10 yrs, 11-65 yrs, and >65 yrs.
• Data Provenance: Not explicitly stated, but the submission is from Braun AG, 61476 Kronberg, Germany, implying the general origin of the product. The document does not specify whether the clinical data was retrospective or prospective, or the specific country of origin for the clinical study participants.

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

• This information is not provided in the summary. For a thermometer, "ground truth" typically refers to core body temperature, often measured via a highly accurate reference thermometer (e.g., rectal or arterial line), rather than expert interpretation of an image or signal.

4. Adjudication method for the test set:

• This information is not provided in the summary. Adjudication methods (like 2+1 or 3+1) are typically used for subjective assessments (e.g., image interpretation), which is not directly applicable to a thermometer's output. The "ground truth" for a thermometer would be a physical measurement.

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:

• No, an MRMC comparative effectiveness study was not done. This type of study assesses how AI (or computer-aided detection/diagnosis) impacts human performance in tasks like image interpretation. This is not relevant for a standalone temperature measuring device like the Braun Thermoscan thermometer. The clinical study was a comparison study to a known predicate device (IR-4000) and clinical repeatability testing.

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

• Yes, a standalone performance assessment was done. The entire submission details the performance of the device itself (the thermometer) independent of human interpretation or collaboration, as evidenced by the "Summary of Testing" for both non-clinical and clinical results. The accuracy and repeatability are inherent to the device's function.

7. The type of ground truth used:

• The ground truth for accuracy and comparison would implicitly be reference measurements of body temperature (e.g., from a highly accurate thermometer or standard clinical methods) against which the Braun Thermoscan IRT 3020/3520's readings were compared. The summary does not explicitly detail the method for establishing this ground truth but implies it through the accuracy specifications and comparison study to the IR-4000.

8. The sample size for the training set:

• This information is not applicable/not provided for a traditional training set as seen in machine learning. The Braun Thermoscan is a device based on a physical measurement principle (infrared radiation), not a machine learning algorithm that is "trained" on a dataset in the conventional sense. Any "calibration" or internal adjustments would be part of the device's design and manufacturing process, not a data training set.

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

• As mentioned above, this concept is not applicable to this type of device.

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