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510(k) Data Aggregation
(42 days)
The Kinsa QuickCare Thermometer is used for the intermittent measurement and monitoring of human body temperature, orally, rectally and under the arm. The device is for the adult and pediatric population.
The Kinsa QuickCare Thermometer is a battery powered, thermistor based predictive Bluetooth low energy (BLE) enabled thermometer used for the measurement and monitoring of human body temperature. Body temperature can be measured with the Kinsa QuickCare Thermometer orally, axillary (under the arm), and rectally. The thermometer is reusable for clinical and/or home use on people of all ages with adult supervision. The device can be used with as a standalone device or in conjunction with the Kinsa App on a compatible BLE enabled smartphone.
The Kinsa QuickCare Thermometer is a clinical electronic thermometer. The provided document is a 510(k) summary for its premarket notification to the FDA. The document focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study proving the device meets individual acceptance criteria in the manner one might expect for a new algorithmic device with novel performance claims.
Here's a breakdown of the information requested, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Standard) | Reported Device Performance (Compliance) |
|---|---|
| Accuracy (Temperature Measurement) | ± 0.2°C within measurement range of 32 to 42.8°C (89.6 to 109.2°F) |
| ISO 80601-2-56:2009 (Medical Electrical Equipment – Part 2-56: Particular Requirements For Basic Safety And Essential Performance Of Clinical Thermometers For Body Temperature Measurement) | Design Verification results confirmed the device meets the product requirements set by Kinsa and the performance standard requirements of ISO 80601-2-56:2009. Performance comparison with the predicate device demonstrates laboratory accuracy of the subject device is the same as the predicate device over the same temperature range. |
| Biocompatibility (AAMI/ANSI/ISO 10993-5:2009 /(R)2014 for cytotoxicity; ISO 10993-10:2010 for irritation and sensitization) | All skin contacting materials have been tested successfully for biocompatibility. |
| Electrical and Mechanical Safety & Essential Performance (AAMI/ANSI ES 60601-1:2005/(R)2012; IEC 60601-1-11:2015) | Electrical and Mechanical Safety as well as essential performance was confirmed through compliance testing. |
| Electromagnetic Compatibility (IEC 60601-1-2:2014) | Electromagnetic Compatibility was confirmed through compliance testing. |
| Software Verification and Validation (FDA "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" (May 11, 2005)) | Software Verification and Validation results confirmed the firmware and software units meet the software requirements specifications and the system performs as intended. |
| Labeling Verification (FDA "Guidance on the Content of Premarket Notification [510(k)] Submission for Clinical Electronic Thermometers) | Labeling verification per the recommendations. |
| Cybersecurity Management (FDA guidance document "Content of Premarket Submissions for Management of Cybersecurity in Medical Devices (October 2, 2014)") | Cybersecurity management has been addressed. |
2. Sample size used for the test set and the data provenance
The document does not specify a separate "test set" in the context of clinical studies for performance metrics like sensitivity, specificity, or predictive values. The testing described is primarily design verification and validation against established standards.
For accuracy, which is the most relevant performance metric here, the document states: "Performance comparison with the predicate device demonstrates laboratory accuracy of the subject device is the same as the predicate device over the same temperature range." It does not provide the sample size of individuals or the number of measurements taken during this laboratory accuracy comparison. The data provenance is implied to be laboratory testing rather than real-world clinical data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)
This information is not applicable or provided. The device measures a physical parameter (temperature), and the "ground truth" for accuracy is established by a reference thermometer in a laboratory setting, not by human expert interpretation.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. Temperature measurement accuracy is not typically adjudicated by expert consensus in this manner.
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. The Kinsa QuickCare Thermometer is a direct measurement device, not an AI-assisted diagnostic tool that would involve human readers interpreting images or data with or without AI assistance. The Bluetooth functionality is for data transmission, not for an AI-powered interpretive aid.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
The device itself is a "standalone" measurement device. The performance data presented (accuracy, safety, EMC, etc.) relates to the device's intrinsic operation. While it can connect to an app, its core function of temperature measurement and predictive algorithm acts "stand-alone" in terms of measurement generation. No specific "algorithm only" study report is detailed beyond stating that "Software Verification and Validation results confirmed the firmware and software units meet the software requirements specifications and the system performs as intended."
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For accuracy, the ground truth is established by reference temperature measurements in a laboratory setting, typically using a calibrated high-precision thermometer. For other aspects like safety and EMC, the ground truth is compliance with the specified international standards.
8. The sample size for the training set
This is not applicable as the Kinsa QuickCare Thermometer is a traditional electronic thermometer that uses a thermistor and a predictive algorithm, not a machine learning or AI model that requires a "training set" in the typical sense for image interpretation or pattern recognition. The predictive algorithm is likely based on mathematical modeling and calibration, not statistical learning from a large dataset.
9. How the ground truth for the training set was established
Not applicable, as there is no "training set" cited for a machine learning model. The predictive algorithm's parameters would have been established through engineering design, calibration against known temperature references, and validation against a variety of temperature profiles.
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