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The Falck Medical Applanation Tonometer Model 2 (FAT 2) is indicated in the measurement of intraocular pressure and ocular pulsatile amplitude.

The Falck Medical Applanation Tonometer (FAT 2) measures the intraocular pressure by applanation of the cornea uses a disposable sterile plastic prism. It is used as an accessory to a slit lamp microscope. Internal software is used for error checking and calibration. The disposable prism is mounted on a free floating counterweighted arm. When the prism comes into contact with the cornea, the initial applanation area is determined by the biomechanics of the cornea. Force is then actively applied to the cornea and measurements are taken every 13.8 milliseconds. Multiple readings are taken and statistical analysis is performed to ensure repeatability. The measurement principle is the same as the Goldmann, but adjustments are reade for variations in surface wetness and corneal resistance. The size of the applanation area is objectively determined by using visible light and the principle of total internal reflectance (TIR). A new sterile disposable prism is required prior to each new patient. In addition to intraocular pressure, the device also measures ocular pulsatile amplitude by recording the change in the applanation signal that occurs with the cardiac cycle. The measurement of ocular pulsatile amplitude is similar to the Langham methodology.

Here's a breakdown of the acceptance criteria and study information for the Falck Medical Applanation Tonometer (FAT 2), based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly state "acceptance criteria" in a quantitative, pre-defined manner. Instead, performance is demonstrated through comparison to a reference standard (Goldmann applanation tonometer) and an in-vitro manometric study. The implicit acceptance is that the device's performance is comparable or superior to these standards.

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

• Clinical Trial (IOP measurement):
  • Sample Size: 205 eyes (number of patients is not specified, but typically one patient has two eyes)
  • Data Provenance: Not explicitly stated, but the "Clinical Trial Summary" implies prospective human clinical data. The country of origin is not specified.
• Manometric Study (In-vitro accuracy):
  • Sample Size: Not specified beyond "human eye bank eyes." The number of individual eyes or measurements is not provided.
  • Data Provenance: Retrospective for the usage of "eye bank eyes," as these would be from deceased donors. The country of origin for the eye bank is not specified.

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

• Clinical Trial (IOP measurement): The ground truth was established by a "reference Goldmann applanation tonometer." This implies expert operation of the Goldmann tonometer, but the number of operators/experts or their specific qualifications are not mentioned. The expertise is assumed to be inherent in the recognized performance of the Goldmann device.
• Manometric Study (In-vitro accuracy): The ground truth was established by a "reference u-tube mercury manometer." This is a physical standard, not requiring expert consensus in the same way clinical ground truth does. The operators' qualifications are not specified.

4. Adjudication Method for the Test Set

• Clinical Trial: Not applicable as the Goldmann tonometer serves as the single reference (ground truth). No explicit mention of independent adjudication of readings from the Goldmann or FAT 2 beyond basic statistical comparison.
• Manometric Study: Not applicable; the mercury manometer provides a physical reference.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and what was the effect size of how much human readers improve with AI vs. without AI assistance

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described. This device is a measurement instrument, and the studies focused on its direct measurement accuracy compared to established methods, not on how it assists human readers in making diagnoses or interpretations in an MRMC setting. Therefore, an effect size for human reader improvement with AI assistance is not relevant or reported here.

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

• Yes, a standalone performance assessment was effectively done. The Falck Applanation Tonometer (FAT 2) is a device that directly measures IOP. The clinical trial compared its measurements directly to a Goldmann tonometer, and the manometric study compared its measurements to a mercury manometer. These are direct, standalone performance assessments of the device's output. The "Internal software is used for error checking and calibration," which implies algorithmic processing for the final output.

7. The Type of Ground Truth Used

• Clinical Trial (IOP measurement): Reference standard measurements from a Goldmann Applanation Tonometer. This is a widely accepted clinical standard.
• Manometric Study (In-vitro accuracy): Reference measurements from a u-tube mercury manometer. This is a physical, highly accurate, and established in-vitro standard for pressure.

8. The Sample Size for the Training Set

• The 510(k) summary does not provide specific information about a "training set" in the context of machine learning. The device contains "Internal software" for error checking and calibration, but details on how this software was developed or if it involved a distinct supervised learning "training set" are not disclosed. Typically, applanation tonometers rely on physical principles (Imbert-Fick Law) and calibration rather than complex machine learning models that require large training datasets in the conventional sense.

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

• As a "training set" is not explicitly mentioned or detailed for a machine learning model, the method for establishing its ground truth is also not provided. The device likely relies on engineering and calibration data for its internal software rather than ground truth labels from a training dataset.

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