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The Scanmate Flex is a multi-purpose computer-based ultrasonic diagnostic system for ophthalmic application, intended to both visualize the interior of the eye by means of ultrasound and to make measurements inside the eye, including the measurement of axial length for determination of IOL Power. The Scanmate Flex is intended for the examination of adult patients.

The Scanmate Flex is a diagnostic ultrasound system that allows eyecare professionals to visualize and measure internal structures of the eye. The technology is based on ultrasonic pulse echo technology, whereby short bursts of ultrasonic energy are transmitted and the resulting echoes are captured, amplified, filtered and processed. The timing of the echoes is analyzed and converted into distance information (when the A-Scan probe is used) or images (when the B-Scan and UBM probes are used). The distance information and images are displayed on a PC screen. The Scanmate Flex consists of an interface module (which is connected to a standard Windows PC, not included with the Scanmate Flex system) and one or more optional ultrasound probes.

The provided text describes the DGH Technology Scanmate Flex, an ophthalmic ultrasound system. However, the document does not contain a detailed study with acceptance criteria and reported device performance in the format requested, specifically for an AI-based system. The information focuses on regulatory approval (510(k) submission) based on substantial equivalence to predicate devices, rather than a prospective study demonstrating specific performance metrics against an acceptance threshold for an AI component.

Here's what can be extracted based on the provided text, while also highlighting what is NOT present:

1. Table of Acceptance Criteria and Reported Device Performance:

• Acceptance Criteria: Not explicitly stated in the provided document in a quantitative manner for specific performance metrics (like sensitivity, specificity, accuracy). The document focuses on demonstrating substantial equivalence to predicate devices and adherence to medical device standards.
• Reported Device Performance:
  • Acoustic Output: Verified by Acertara Acoustic Laboratories and Sonora Medical Systems. (No specific numerical values or acceptance criteria for acoustic output are provided).
  • Measurement Performance: Verified using precision test blocks and monofilament phantoms. (No specific numerical accuracy, precision, or acceptance criteria are provided).
  • Thermal, Mechanical, and Electrical Performance: Conforms to a list of specified standards (e.g., AAMI / ANSI ES60601-1, IEC 60601-1-2, IEC 60601-2-37, NEMA UD 2-2004, NEMA UD-3 2004). (Compliance with standards is stated, not specific performance values against internal acceptance criteria).
  • Biocompatibility Testing: Patient-contacting materials were found to be biocompatible. (Statement of outcome, not specific performance data against acceptance criteria).
  • Software Verification and Validation Testing: Verified and validated in accordance with internally developed test plans. (Statement of process, not specific performance results).

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

• This information is not provided in the document. The document describes verification and validation using test phantoms and standard-based testing, not clinical data sets with sample sizes or provenance.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• This information is not provided as the testing described does not involve expert-established ground truth in a clinical context. Verification was done using physical phantoms and adherence to engineering standards.

4. Adjudication Method for the Test Set:

• This information is not provided as there is no clinical test set adjudication described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

• No, an MRMC comparative effectiveness study was not done according to the provided text. The device is an ultrasound system for imaging and measurement, and the submission focuses on its technical equivalence to existing predicate devices. There is no mention of AI assistance for human readers or an effect size for human improvement.

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

• The document describes a standalone ultrasound system. However, there is no mention of an "algorithm only" performance study in the context of AI. The device itself is an algorithm (hardware and software) that performs imaging and measurements. The performance testing (acoustic output, measurement accuracy with phantoms) could be considered standalone performance of the device's measurement capabilities.

7. The Type of Ground Truth Used:

• For acoustic output: Likely measurements from calibrated acoustic sensors and comparison to regulatory limits.
• For measurement performance (Axial Length, ACD, LT, VCD): Likely known dimensions of precision test blocks and monofilament phantoms.
• For thermal, mechanical, electrical, and biocompatibility: Compliance with established industry and regulatory standards.
• For software: Internally developed test plans.

8. The Sample Size for the Training Set:

• This information is not applicable/not provided. The device described is a traditional ultrasound system. There is no indication of an AI model being trained on a dataset.

9. How the Ground Truth for the Training Set was Established:

• This information is not applicable/not provided as there is no mention of a training set for an AI model.

In summary, the provided FDA 510(k) summary focuses on demonstrating that the Scanmate Flex ophthalmic ultrasound system is substantially equivalent to legally marketed predicate devices through engineering and safety testing, rather than presenting a clinical study with detailed performance metrics, acceptance criteria, and ground truth establishment for an AI component.

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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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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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