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The Infrared Body Thermometer, Model: HTD8823US, is an electronic clinical thermometer using an infrared sensor to detect body temperature from the forehead in people of all ages for home setting use.

Infrared (IR) Body Thermometer, model: HTD8823US, is a hand-held, battery powered, infrared thermometer that measures human body temperature from forehead. The reference body site is oral. The device can measure temperature with two modes, forehead mode and forehead scan mode, and both modes measure forehead temperature. The forehead mode measures temperature from center of the forehead. Forehead scan mode measures temperature by gently positioning the probe flush (flat) on the center of the forehead, midway between the eyebrow and the hairline, press and hold the On/Scan button. Lightly slide the thermometer across the forehead keeping the sensor flat and in contact with the skin until reaching the right hairline, release the On/Scan button and remove the thermometer from the forehead, then the temperature will display on the screen, the whole process takes 3~10 seconds.

The provided document is a 510(k) Summary for the HeTaiDa Technology Co., Ltd. Infrared Body Thermometer, Model: HTD8823US. It outlines the device's characteristics and its substantial equivalence to predicate devices, supported by non-clinical and clinical testing.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. A table of acceptance criteria and the reported device performance

The document references two primary performance standards for the Infrared Body Thermometer: ASTM E1965-98 (2016) and ISO 80601-2-56: 2017. While it states that the device "Meets" these standards, specific quantitative acceptance criteria from these standards and the device's reported performance against each of those specific numerical criteria (e.g., maximum permissible error at different temperature ranges) are not explicitly itemized in a table within the provided text.

However, the "Summary of technological characteristics of device compared to the predicate devices" (Pages 5-7) includes some key performance characteristics that imply acceptance criteria and performance, mostly by stating "Same" or "Similar" to predicates which are presumed to meet equivalent criteria.

Here's a curated table based on the available information, inferring acceptance criteria from the predicates and performance against those:

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

• Sample size for the test set: 140 subjects.
• Data provenance: The document does not explicitly state the country of origin of the data. It also does not explicitly state if the study was retrospective or prospective, but clinical accuracy studies for new devices are typically prospective. The text mentions "Each model was evaluated in 0 up to 3 months, 3 months up to one year, older than 1 year and younger than 5 years, and older than 5 years age groups," indicating a structured, likely prospective clinical trial.

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 document. For a clinical thermometer, the "ground truth" for temperature measurement is typically established by using a highly accurate reference thermometer (e.g., laboratory-grade thermometer or a rectal/oral thermometer considered the gold standard in a clinical setting by trained medical professionals). The document only states "The clinical accuracy test report and data analysis followed the requirements of the ASTM E 1965-98 (2016)," which is a standard for infrared thermometers. It doesn't detail the personnel involved in supervised measurements.

4. Adjudication method for the test set

This information is not provided in the document. Adjudication methods like 2+1 or 3+1 are typical for subjective interpretations (e.g., radiology reads). For quantitative measurements like temperature, the ground truth is usually established by direct measurement with a reference standard, not typically through expert adjudication of images or subjective findings.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done, nor would it be relevant for this type of device. This device is an infrared thermometer, not an AI-assisted diagnostic tool that involves human "readers" or interpretation of complex cases. Therefore, there's no mention of AI assistance or its effect size on human performance.

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

The device itself is a standalone algorithm, in the sense that it automatically measures and displays temperature without requiring human interpretation of an output from an underlying AI (beyond reading the numerical display). The "clinical accuracy testing" evaluates the device's performance in this standalone capacity.

7. The type of ground truth used

The ground truth for the clinical accuracy testing was established by comparing the device's measurements against a reference standard of body temperature measurement, as specified by ASTM E1965-98 (2016). While not explicitly stated, this usually involves a core body temperature measurement method (e.g., rectal, oral, or an equivalent highly accurate reference thermometry system) performed by trained personnel.

8. The sample size for the training set

This information is not applicable and therefore not provided. Infrared thermometers like the HTD8823US are hardware-based measurement devices that employ fixed algorithms and calibrations, not machine learning or AI models that require "training sets" in the conventional sense. Any "training" would refer to internal calibration and validation data used during the device design and manufacturing process, which is distinct from a machine learning training set.

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

As inferred above, this information is not applicable as the device does not utilize a machine learning model that requires an external "training set" with established ground truth. The device is calibrated and validated against physical temperature standards and clinical performance requirements.

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