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The Falck Medical Multi-function Tonometer (FAT1) is indicated for the measurement of intraocular pressure, ocular pulsatile amplitude, force (Ophthalmodynamometry) and estimating conventional outflow facility with calibrated force application (Indentation Tonography).

The Falck Medical Multi-function Tonometer (FAT1) mounts on the slit lamp microscope. The device has a floating prism arm which holds a disposable plastic prism. The device has a detection system located in the prism arm head that prevents re-use of the prism.

The prism optical system uses the principle of total internal reflectance. Sampling is every 7 milliseconds and multiple samples are obtained for each measurement. Each measurement is statistically analyzed for repeatability and accuracy. Intraocular pressure is measured using the Imbert-Fick method of applanation. Force application is controlled by the microprocessor. The ocular pulsatile amplitude and central retinal artery pulsation with force application is optically captured. Indentation tonography methodology using calibrated force application is used to measure conventional outflow facility.

This document does not contain the detailed information necessary to fully answer all aspects of your request, particularly regarding specific acceptance criteria values and a comprehensive study report. However, I can extract and infer information based on the provided text.

Here's an analysis of the acceptance criteria and study that demonstrates the device meets them, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance:

The document mentions that the Falck Medical Multifunction Tonometer, FAT1, is substantially equivalent to two predicate devices: Falck Medical, Inc. FAT2 (K071755) and Model 30 Classic Pneumatonometer with Tonography Option (K002395). This implies that the FAT1's performance is expected to be comparable to or within established ranges of these predicate devices, which would have their own acceptance criteria.

However, specific quantitative acceptance criteria for the FAT1 itself (e.g., "IOP measurement accuracy within X mmHg of gold standard") and the corresponding reported performance values are not explicitly stated in this document. The document primarily focuses on establishing substantial equivalence based on technical equivalence and prior testing of the predicate device (FAT2) and a separate IDE study for tonography.

Here's a table based on the information provided, with inferred criteria where direct values are missing:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• For IOP, Ocular Pulsatile Amplitude, and Ophthalmodynamometry (based on FAT2 equivalence): The document refers to validation data for the predicate device, FAT2 (K071755), and additional correspondence (11/03/2009 and 01/10/2010). The sample sizes and exact data provenance (country, retrospective/prospective) for the FAT2's testing are not provided in this document.
• For Conventional Outflow Facility (IDE 1080757): "Bench testing and clinical testing done under FDA guidance with IDE (1080757) for the measurement of conventional outflow facility demonstrated the safety, repeatability, precision and accuracy of the FAT1." The sample size, country of origin, and whether it was retrospective or prospective for this specific IDE study are not provided in this document.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not specify the number or qualifications of experts used to establish ground truth for any of the performance tests mentioned (either for FAT2 or the IDE study for FAT1's tonography).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not specify any adjudication method used for establishing ground truth or evaluating device performance.

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

The device is a tonometer, a measurement device, not an image-reading AI system where "human readers improve with AI vs without AI assistance" would be a relevant concept. Therefore, an MRMC comparative effectiveness study of this nature was not done, nor would it be applicable.

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

This device is a physical instrument for measuring physiological parameters, operated by a human. The concept of "standalone algorithm only" is not applicable in the context of this tonometer. However, the device itself performs the measurement calculations automatically (e.g., using the Imbert-Fick method, statistical analysis of multiple samples). The closest equivalent would be the accuracy of the device's measurement output compared to a reference standard, which is implied by the performance testing.

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

The document does not explicitly state the type of ground truth used. However, for a tonometer, the ground truth for intraocular pressure measurement would typically involve a reference standard tonometer known for its accuracy (e.g., Goldmann applanation tonometer, or other highly calibrated master tonometers). For biocompatibility, the ground truth is established through standard biocompatibility testing methodologies (cytotoxicity, systemic toxicity, ocular irritation assessments) as per ISO standards.

8. The sample size for the training set

The document does not mention a "training set" in the context of machine learning, as this device's function is explained based on physical principles (Imbert-Fick applanation, optical systems) and specific methodologies for tonography. Therefore, the concept of a "training set" is not directly applicable as described in modern AI/ML development. The device's calibration and validation would involve engineering specifications and clinical performance data, but not a "training set" in the AI sense.

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

As the concept of a "training set" as understood in machine learning is not applicable to this device, the method for establishing its ground truth is not provided and not relevant in this context.

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