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The Kinsa Smart Thermometer is intended to measure the human body temperature orally, rectally, or under the arm, and the devices are reusable for clinical or home use on people of all ages.

The Kinsa Smart Thermometer product is a thermometer that connects to a Smartphone or another mobile device (e.g. an iPod Touch). The product will read body temperature the same way a clinical digital thermometer does by being placed under the tongue in the mouth, rectum or alternatively, or under the arm. Like other clinical digital thermometers, the Kinsa Smart Thermometer is a thermistor-based product; however, it has the advantage of being read on a mobile device display. Unlike other clinical digital thermometers, the Kinsa Smart Thermometer product requires no batteries or LCD displays. The Kinsa Smart Thermometer is reusable for clinical or home use on people of all ages.

The Kinsa Smart Thermometer will connect to Smartphones or other mobile devices via a headphone jack that accepts a microphone input. In this document the terms Smartphone, smartohone and mobile device are used interchangeably and are defined to include the following products: Apple iPhones 5, 4S and the Apple iPod Touch 5.

The Kinsa Smart Thermometer consists of four components:

• A. Thermometer (probe).
• B. An adapter to setup each Smartphone for temperature reading (only needed once per Smartphone).
• C. An optional, flexible extension cord that can be used to lengthen the distance between the thermometer and Smartphone so users can see the Smartphone screen while taking a temperature.
• D. Software.

Here's an analysis of the acceptance criteria and study information for the Kinsa Smart Thermometer, based on the provided text:

Key Takeaway: The provided 510(k) summary focuses primarily on equivalence to a predicate device and compliance with established standards (ASTM E1112-00) for clinical electronic thermometers. It does not detail a specific study proving the device meets novel acceptance criteria in the way one might expect for a new AI/ML-driven medical device. Instead, the acceptance criteria are largely implied by compliance with the ASTM standard and comparison to the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

Given that the document emphasizes compliance with ASTM E1112-00 (reapproved 2011), the acceptance criteria are drawn from general requirements for clinical electronic thermometers specified in this standard and the performance metrics compared to the predicate device.

Note: The document explicitly states, "The Kinsa Smart Thermometer meets and exceeds ASTM standards for accuracy and meets ISO standards for accuracy." This indicates that the ASTM E1112-00 standard itself defines the primary acceptance criteria for accuracy.

2. Sample Size Used for the Test Set and Data Provenance

The provided document does not specify a separate test set sample size or data provenance (e.g., country of origin, retrospective/prospective) for a study demonstrating performance against acceptance criteria. The information focuses on compliance with standards rather than a clinical validation study with a defined patient cohort.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This information is not applicable and not provided in the document. The regulatory pathway for this device, a Class II thermometer, relies on established physical measurement standards (like ASTM E1112-00) and comparison to a predicate device, not on expert adjudication of diagnostic outputs from images or complex data. Therefore, "ground truth" in the context of expert consensus, as might be used for AI/ML imaging devices, is not relevant here.

4. Adjudication Method for the Test Set

Not applicable and not provided. As explained above, the assessment relies on metrological standards, not expert adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study is relevant for diagnostic imaging devices where human readers interpret data, often with AI assistance. The Kinsa Smart Thermometer is a direct measurement device; its function is to provide a temperature reading, not to assist humans in interpreting complex diagnostic information.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself is a measurement instrument. Its "standalone" performance is its ability to accurately measure temperature according to the specified standards, which is what the compliance with ASTM E1112-00 addresses. However, it's important to note that the device relies on a mobile device for display and processing, so it's not truly "standalone" in operation like a conventional digital thermometer. The "algorithm only" aspect refers to the software that processes the signal from the thermistor to display the temperature. The document states this software "displays the precise temperature on the screen" and implicitly meets the ASTM accuracy standards.

7. The Type of Ground Truth Used

The "ground truth" for the Kinsa Smart Thermometer's performance is established through metrological reference standards and calibrated instruments as defined by standards like ASTM E1112-00. This standard specifies the test methods and performance requirements for electronic thermometers, which would involve comparing the device's readings against highly accurate, traceable temperature references. It does not involve expert consensus, pathology, or outcomes data in the way an AI/ML diagnostic system would.

8. The Sample Size for the Training Set

The document does not specify a "training set" sample size. The Kinsa Smart Thermometer is a sensor-based device with a processing algorithm, not a machine learning model developed through training data in the conventional sense. Its "training" would be more akin to calibration and engineering development to ensure accurate signal processing, rather than learning from a large dataset of patient temperatures.

9. How the Ground Truth for the Training Set Was Established

Not applicable in the context of "training set" for a machine learning model. The fundamental "ground truth" for its development would be the physical principles of thermistor operation and the metrological standards for temperature measurement. Engineers would have designed and calibrated the device and its processing software to adhere to these known physical laws and the accuracy requirements of ASTM E1112-00 using reference thermometers.

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