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Wearable Digital Thermometer is a battery-operated electronic device with intended use of measuring and monitoring human armpit temperature continuously via wireless signal transmission of the measuring result. Wearable Digital Thermometer is reusable and intended for armpit temperature monitoring for persons of all age. The temperature data of device is not intended to replace the advice, diagnosis, nor treatment recommendations of doctor. Wearable Digital Thermometer can be used at home and healthcare center.

The Wearable Digital Thermometer consists of a host, App software and stickers. A comprehensive Android and iOS App are provided to access the host from a smart device. It is used for measuring and monitoring armpit temperature in real-time continuously and remotely via Bluetooth to smart device. The medical double-sided sticker are used to fix and stick the host in the user`s axilla. The NTC temperature sensor is located closely to the stainless steel sheet that make sure the accuracy of temperature data measured. The subject device could measure and monitor temperature in real-time continuously and remotely via Bluetooth to smart phone. The Temp Pal is the combination device of thermometer and Bluetooth communication unit intended to be worn at axilla to monitor the armpit temperature continuously. The subject device is a direct mode clinical thermometer where the output temperature is not adjusted. For the monitoring operation, switch the thermometer on and stick the thermometer in the user's axilla. The thermometer will make a Bluetooth connection between the thermometer and the receiver automatically (User should setup Bluetooth properly on receiver). Then the thermometer starts to measure the body temperature. The wireless thermometer uses a rechargeable battery for operation. When the battery is low, internal circuit will detect the low battery condition automatically and send "low battery" signal through Bluetooth communication unit to receiver.

The provided document is a 510(k) summary for the Wearable Digital Thermometer (T31). While it lists performance data and standards met, it does not contain a detailed study report with specific acceptance criteria beyond general accuracy requirements, nor does it provide sample sizes, ground truth establishment methods for a test set, or information about expert involvement typically found in clinical validation studies for AI/ML devices.

The document refers to the device as a "Clinical Electronic Thermometer" and a "Wearable Digital Thermometer." It doesn't describe an AI or Machine Learning component. The "Performance Data" section primarily focuses on engineering and regulatory compliance testing rather than clinical study results that would typically be detailed for an AI/ML product.

Here's a breakdown of the information that can be extracted, and what is missing:

1. Table of acceptance criteria and the reported device performance:

Study Details:

Since the device is a "Wearable Digital Thermometer" and not an AI/ML-driven diagnostic or prognostic tool, the "study" described is a series of engineering and regulatory compliance tests rather than a clinical trial in the traditional sense for AI/ML validation.

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

• Sample size for accuracy testing: Not specified. The document only states that "Performance test" was conducted and "meet the specification of the product and the relevant standards." It doesn't explicitly mention the number of subjects or measurements for clinical accuracy validation.
• Data provenance: Not specified if a specific clinical test set was used, beyond the general standards compliance.

3. 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 digital thermometer, ground truth is typically established by comparing the device's readings against a highly accurate reference thermometer, rather than expert consensus on images or clinical assessments. The specific methodology for this comparison (e.g., how many reference measurements were taken) is not detailed.

4. Adjudication method for the test set:

• Not applicable / Not specified. Adjudication methods like 2+1 or 3+1 are relevant for interpreting subjective data (e.g., radiology images) where experts might disagree. For a quantitative measurement like temperature, the ground truth is typically a direct measurement from a calibrated reference.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No. An MRMC study is relevant for AI systems that assist human readers in interpreting complex data (e.g., medical images). This document describes a digital thermometer, not an AI-assisted diagnostic tool.

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

• Yes, in essence. The performance tests described (accuracy, electrical safety, EMC, biocompatibility, shelf life, usability) are evaluating the device's inherent function, without human interpretation as part of its core measurement capability. The "algorithm" here is the device's internal temperature measurement and display mechanism.

7. The type of ground truth used:

• Reference standard/calibration: For accuracy testing, the ground truth would be established by comparing the device's temperature readings against a certified reference thermometer or a temperature standard, as outlined in standards like ISO 80601-2-56 and ASTM E1112. The specific details beyond "met the requirement" are not provided.

8. The sample size for the training set:

• Not applicable / Not specified. This device, as described, is a traditional electronic thermometer. It does not appear to utilize machine learning or need a "training set" in the context of AI/ML. Its function is based on fixed physical principles (NTC resistor).

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

• Not applicable. As a non-AI/ML device, there is no "training set" for an algorithm.

In summary: The provided document is a regulatory submission for a conventional medical device (a digital thermometer), not an AI/ML-powered one. Therefore, many of the questions asking for specifics related to AI/ML study design (like training/test sets, expert adjudication, MRMC studies) are not applicable or not detailed in this type of submission. The performance data focuses on demonstrating compliance with relevant electrical, safety, biocompatibility, and measurement accuracy standards for clinical thermometers.

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