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The intended use of the DGH 6000 is the measurement of AL, ACD, LT of the human eye. The DGH 6000 is also intended to calculate the optical power of an IOL that is to be implanted during cataract surgery. The DGH 6000 is intended to be used solely by qualified medical professionals. Clinical consideration and judgment should be applied when using the DGH 6000.

The DGH 6000 A-Scan is a USB plug-in device that uses A-Mode, pulsed echo ultrasound technology to measure the axial length (AL), anterior chamber depth (ACD), and lens thickness (LT) of the human eye. The device includes formulas to calculate the implanted IOL power, using the axial length measurement.

Here's a breakdown of the acceptance criteria and study information for the DGH 6000 Scanmate A, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific numerical acceptance criteria for performance metrics like accuracy, precision, or agreement with a gold standard. Instead, it relies on the principle of substantial equivalence to a predicate device.

The "acceptance criteria" are implied to be that the DGH 6000 Scanmate A performs equivalently to its predicate device, the DGH 3000A, in terms of safety and efficacy for measuring axial length (AL), anterior chamber depth (ACD), and lens thickness (LT) of the human eye.

Therefore, the table will reflect this implicit criterion rather than precise numerical targets.

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

The document mentions "Comparative test block (phantom) tests" as a non-clinical test.

• Sample Size for Test Set: The specific sample size for the test block (phantom) tests is not provided in the document.
• Data Provenance: The tests were non-clinical phantom tests, not human subject data. Therefore, there is no country of origin or retrospective/prospective nature to describe in terms of human data.

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

• Number of Experts: This information is not provided as the primary performance evaluation was based on non-clinical phantom tests.
• Qualifications of Experts: Not applicable given the above.

4. Adjudication Method for the Test Set

• Adjudication Method: This information is not provided and is likely not applicable given the non-clinical nature of the comparative phantom tests.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The study described is a non-clinical comparison to a predicate device using phantom data.
• Effect Size: Not applicable as no MRMC study was performed and the device is not described as an AI-assisted diagnostic tool. Its function is to directly measure parameters using ultrasound.

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

• Standalone Performance: The core comparative performance was done using non-clinical phantom tests. This could be considered a form of standalone performance as it evaluates the device's measurements against known phantom values. However, the document doesn't detail this as a separate "standalone" study in the typical AI context. The device itself (ultrasound biometer) inherently provides standalone measurements. The software assists in IOL calculations, which is an output of these measurements.

7. The Type of Ground Truth Used

• Ground Truth: For the non-clinical tests, the ground truth was based on the known physical properties and measurements of the comparative test block (phantom).

8. The Sample Size for the Training Set

• Sample Size for Training Set: This information is not applicable/not provided. The DGH 6000 Scanmate A uses established ultrasonic pulse-echo technology and digital signal processing algorithms. It is not described as an AI/machine learning device that requires a distinct "training set" in the conventional sense. The algorithms are built upon scientific principles, not learned from a training dataset.

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

• Ground Truth for Training Set: This information is not applicable as there is no mention of a training set for an AI/machine learning algorithm. The device's operation is based on physical principles of ultrasound.

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