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The device is used for measuring body temperature in the mouth (oral) in the anus (rectal) and in the armpit (axillary). It is intended for adults, children and infants.

The ri-former® Predictive Thermometer is a fast, highly accurate, and easy to use clinical instrument for measuring patient temperatures by oral, axillary or rectal means. The thermometer is driven by the power supply of our ri-former® diagnostic station. There are rectal (red) and oral, axillary (blue) probes available. To prevent from cross contamination the thermometer needs to be used with disposable probe covers. The thermometer can be used for the temperature measurement of all patients and is designed for the use in hospitals, clinics, medical practices or similar facilities.

This FDA 510(k) premarket notification for the "ri-former Predictive Thermometer" indicates that no clinical studies were performed. The device's substantial equivalence to a predicate device ("FILAC FAS TEMP ELECTRONIC THERMOMETER", K003313) was established through non-clinical performance data.

Therefore, the requested information regarding acceptance criteria, study details, sample sizes, ground truth establishment, expert involvement, and MRMC studies cannot be extracted from the provided document.

However, based on the non-clinical testing performed, we can infer some performance criteria that the device met:

1. Table of Acceptance Criteria and Reported Device Performance

• ISO 10993-10:2013 (Tests for irritation and skin sensitization) compliant | Human contacting components (probe, probe cover) were tested in accordance with ISO 10993-5 (cytotoxicity) and ISO 10993-10 (irritation and skin sensitization). (Implies compliance as it was submitted in support of substantial equivalence). |
  | Electrical Safety | - IEC 60601-1:2005+CORR.1:2006+CORR.2:2007+AM1:2012 compliant (General Requirements for Basic Safety and Essential Performance) | Device was tested in accordance with IEC 60601-1. (Implies compliance as it was submitted in support of substantial equivalence and cited as evidence for different power source and materials). |
  | Electromagnetic Compatibility (EMC) | - IEC 60601-1-2:2007 (Third edition) compliant (Collateral Standard: Electromagnetic Disturbances -- Requirements and Tests) | Device was tested in accordance with IEC 60601-1-2. (Implies compliance as it was submitted in support of substantial equivalence and cited as evidence for different power source). |
  | Thermometer Performance (Overall) | - ISO 80601-2-56 First Edition 2009-10-01 compliant (Particular requirements for basic safety and essential performance of clinical thermometers for body temperature measurement)
• ASTM E1112-00 (Reapproved 2011) compliant (Standard Specification for Electronic Thermometer for Intermittent Determination of Patient Temperature)
• Consistent with Predicate Device | Tests determined that the ri-former® Predictive Thermometer performance is substantially equivalent to the predicate device.
  The device was tested in accordance with ISO 80601-2-56 and ASTM E1112-00. (Implies compliance with these standards and substantial equivalence). |
  | Software Verification & Validation | - FDA's Guidance Document "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices" compliant (Moderate Level of Concern)
• All software requirements met Pass/Fail criteria. | System level software verification and validation protocol developed. Results of each test recorded and compared to pass/fail criteria. All software verification and validation activities show that the software meets product requirements documentation. (Implies all requirements were met). |
  | Power Source (Differences from Predicate) | The difference in power source (ri-former diagnostic station vs. battery) does not raise issues of product's safety and effectiveness. Electromagnetic compatibility and electrical safety demonstrated by testing. | Demonstrated electromagnetic compatibility and electrical safety by testing (IEC 60601-1 and IEC 60601-1-2). Validated to be electrical safety. |
  | Enclose Cover Material (Differences from Predicate) | The ABS material is widely used in medical devices and does not raise issues of product's safety and effectiveness. | (Implied: ABS D-1000 is acceptable and does not raise new safety or effectiveness concerns, in line with its wide use in medical devices). |

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

Not applicable. No clinical test set was used. The substantial equivalence was based on non-clinical performance data and comparison to a predicate device.

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

Not applicable. No clinical test set was used, and thus no ground truth established by experts for a test set.

4. Adjudication Method for the Test Set

Not applicable. No clinical test set was used.

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

No. The document explicitly states "No clinical testing data is included in this submission."

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

Not applicable in the context of an AI/ML algorithm. This device is a predictive thermometer, which is a hardware device with embedded software, not typically evaluated as a standalone AI algorithm in the same way imaging or diagnostic AI is. The software was verified and validated to meet product requirements.

7. The Type of Ground Truth Used

Not applicable in the context of clinical "ground truth" derived from patient data. For the non-clinical performance tests, such as those against ISO and ASTM standards, the "ground truth" would be established by the reference conditions and measurement techniques defined within those standards (e.g., calibrated water baths for temperature accuracy, standard test procedures for electrical safety).

8. The Sample Size for the Training Set

Not applicable. The document does not describe an AI/ML model that would require a "training set" in the conventional sense. The device is a "predictive thermometer," suggesting a model or algorithm for temperature prediction, but information on how this internal prediction algorithm was developed or trained is not provided in this 510(k) summary. The software verification and validation focused on meeting product requirements rather than describing a training process for an AI model.

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

Not applicable, as a training set for an AI/ML model is not described.

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