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The Wireless 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. The system is intended for armpit temperature monitoring for persons with all ages.

The products can be used in hospital and at home.

The MCU chip in the wireless electronic thermometer collects the voltage value of the NTC thermal resistor, a thermo-sensitivity resistance, whose resistance will change according to temperature. After collecting and filtering algorithm, a resistance value data is obtained. After that, the Bluetooth chip performs a table lookup operation to find the Celsius temperature value corresponding to the resistance value. The temperature data is transmitted through the Bluetooth transmission to the compatible smart phone, in order to achieve the function of temperature measurement.

The wireless thermometer is composed of a Patch and a Mobile App.

Patch: One side of Patch is medical adhesive which is used to fix the patch on the armpit of patients. The other side is foam. There is a circuit board inside the patch. The patch is used for detecting of human body temperature and then transferred to Mobile App for display and further management. There is no display function on the patch, except for a indicator to indicating power on/off status.
Patches are single use only, and available in different configuration. The models ECH-b1-S, ECH-b1-M and ECH-b1-L all share the same design principles, materials, mechanism of actions. However they are different in size and intended patient.

Mobile APP: The APP is intended for display and management of obtained human body temperature data.

The provided text is a 510(k) Summary for a Wireless Thermometer. It outlines the device description, intended use, and comparison to a predicate device to establish substantial equivalence.

Based on the provided document, here's a breakdown of the acceptance criteria and the study that proves the device meets them:

Important Note: The device described is a clinical electronic thermometer, not an AI-powered diagnostic device. Therefore, many of the typical criteria for AI/ML medical devices (such as MRMC studies, ground truth establishment by experts, and sample sizes for training/test sets for algorithm performance) do not apply to this submission. The "study" here refers to non-clinical testing and benchmarking against established standards and a predicate device.

Acceptance Criteria and Reported Device Performance

Study Details (Focusing on the type of device)

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

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

   • Test Set Sample Size: Not applicable in the context of an AI/ML test set. For non-clinical tests (e.g., biocompatibility, electrical safety, EMC, accuracy testing), the sample sizes are determined by the specific standards (e.g., number of units tested, number of in vitro samples). These details are not provided in the 510(k) summary but would be in the full test reports.
   • Data Provenance: Non-clinical test data generated at the manufacturer's or contracted testing facilities (not specified in detail, but standard for medical device testing). No direct patient data provenance (country, retrospective/prospective) is relevant as no clinical study was performed.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This is not an AI/ML device relying on human expert annotation for ground truth. Ground truth for thermometer accuracy is established by reference measurement instruments following metrological standards. For other tests (e.g., biocompatibility), results are determined by laboratory analysis.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This concept applies to human expert review for AI/ML ground truth, not device performance testing.

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: Not applicable. This device is a standalone thermometer, not an AI-assisted diagnostic tool that supports human readers. The submission explicitly states: "No clinical study is included in this submission."

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, in essence, the device's accuracy and safety were evaluated as a standalone product through non-clinical testing against standards. The "algorithm" here is the embedded firmware that converts sensor readings to temperature, and its performance (accuracy) was assessed.

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

   • For temperature accuracy: The ground truth is established by traceable reference temperature measurement apparatus and methods, as per ISO 80601-2-56. This is a metrological standard, not expert consensus or pathology.
   • For safety (biocompatibility, electrical safety): Ground truth/acceptance is based on compliance with the quantitative and qualitative acceptance criteria specified in the relevant international standards (ISO 10993, IEC 60601 series).

8. The sample size for the training set: Not applicable. This is not an AI/ML device that requires a training set in the typical sense. The device's internal algorithm is designed and calibrated based on engineering principles and sensor characteristics, not machine learning from a large dataset.

9. How the ground truth for the training set was established: Not applicable. As above, there is no "training set" for an AI/ML algorithm. The calibration and design of the thermometer are based on established physical laws and engineering practices for NTC thermistors.

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