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Corneal thickness measurements are useful for screening potential laser refractive surgery patients, glaucoma screening, and monitoring corneal swelling.

The DGH 55 is a handheld ultrasonic pachymeter that uses echo spike techniques to measure the thickness of the cornea.

The provided text is a 510(k) summary for the DGH 55 Handheld Pachymeter. It outlines the device's technical characteristics, intended use, and the tests performed to demonstrate substantial equivalence to a predicate device. However, it does not provide detailed acceptance criteria or a specific study proving the device meets particular numerical performance targets.

Instead, the document focuses on demonstrating substantial equivalence to a previously cleared device (DGH 500 Ultrasonic Pachymeter, K920906A) through various performance tests. The "acceptance criteria" in this context are implicitly that the new device performs comparably to the predicate device and meets relevant safety and electromagnetic compatibility standards.

Here's a breakdown of the requested information based on the provided text, with explicit notes where information is not present:

1. Table of Acceptance Criteria and Reported Device Performance

Note on "Acceptance Criteria" for performance: The document does not specify quantitative thresholds (e.g., "accuracy within X microns" or "agreement within Y standard deviations"). Instead, the acceptance criterion for the comparative tests is implicit in the statement of substantial equivalence: the new device must perform comparably to the predicate.

2. Sample size used for the test set and the data provenance

• Sample size for the test set: Not specified.
• Data provenance (e.g. country of origin of the data, retrospective or prospective): Not specified.

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

• Number of experts: Not applicable. The "ground truth" for the comparative tests would likely be the measurements obtained by the predicate device or a known standard, not expert consensus in the typical sense of interpreting medical images.
• Qualifications of experts: Not applicable.

4. Adjudication method for the test set

• Not specified. Given the nature of a comparative measurement device, adjudication by consensus of multiple human readers is unlikely to be the method. The comparison would likely be statistical.

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

• MRMC study: Not applicable. This device is a handheld pachymeter, not an AI-assisted diagnostic tool that would involve human readers interpreting images. It performs a direct measurement.

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

• This device is a standalone measurement device. It provides corneal thickness measurements directly. The performance tests ("Comparative corneal thickness measurement tests," "Comparative water bath/phantom tests," "Comparative electronic calibration verification tests") would assess its standalone accuracy and consistency.

7. The type of ground truth used

• For "Comparative corneal thickness measurement tests": The ground truth would likely be established by measurements from the legally marketed predicate device (DGH 500 Ultrasonic Pachymeter) or possibly a highly accurate alternative measurement method, though this is not explicitly stated.
• For "Comparative water bath/phantom tests": The ground truth would be the known thickness of the phantoms/standards used in the water bath.

8. The sample size for the training set

• Not applicable. This is a measurement device based on a previously established algorithmic approach, not a machine learning model that requires a distinct "training set" in the common sense. The document states, "The new microprocessor and associated hardware, and the new software implement the same algorithmic approach as the predicate device."

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

• Not applicable, as there is no mention of a training set for a machine learning model.

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