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The LADARWave® CustomCornea® Wavefront System is used for measuring, recording, and analyzing visual aberrations (such as myopia, astigmatism, coma and spherical aberration) and for displaying refractive error maps of the eye to assist in prescribing refractive corrections.

This device is enabled to export wavefront data and associated anatomical registration information to a compatible treatment laser where a wavefront-guided treatment is indicated.

This device can also export centration data and associated registration information for conventional phoropter-based refractive surgery with the LADAR6000™ System.

The LADARWave® CustomCornea® Wavefront System is an aberrometer, utilizing Hartmann-shack wavefront sensing to measure the aberrations in the human eye. The device contains four major optical subsystems used in the clinical wavefront examination.

• A fixation subsystem provides the patient with an unambiguous point of fixation. . Optics in this path automatically adjusts to correct for the patient's spherocylindrical error so that the target is clearly observed.
• A video subsystem provides the device operator with a view of the eye at the . measurement plane. The operator uses the video imagery to position the eye for the measurement and to record the geometry of the wavefront relative to anatomical features.
• A probe beam subsystem directs a narrow beam of eye-safe infrared radiation . into the eye to generate the re-emitted wavefront.
• A wavefront detection subsystem images the re-emitted wavefront onto the . entrance face of the Hartmann-shack wavefront sensor.

These subsystems are all under control of the device software.

Once the wavefront examination is complete, the operator may export the exam data to removable media. The exported electronic file contains all information necessary to perform customized ablative surgery using either the LADARVision®4000 or LADAR6000" System. The information includes the wavefront measurement, essential patient identification information, and geometric registration data. The electronic file is in a proprietary format and is encrypted so that wavefront data cannot be exported for use by an incompatible treatment device.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details:

1. Sample Size used for the test set and the data provenance:

   • The text states, "For each eye, one LADARWave wavefront session (centration photo and five wavefront measurements with associated photos) with eye marks and a session without eye marks were taken for the testing." This implies that multiple eyes were used, but a specific number is not provided.
   • Data Provenance: Not explicitly mentioned (e.g., country of origin). The study appears to be retrospective or a controlled prospective study specifically for this submission, as it compares two methods ("sputnik" and "overlay") on the same eyes.

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

   • This information is not provided in the document. The method of "comparing" the two registration methods ("sputnik" and "overlay") suggests a quantitative assessment rather than expert consensus for ground truth on registration accuracy directly.

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

   • This information is not provided in the document. The comparison seems to be based on direct measurement of registration accuracy between the two methods rather than an adjudication process involving human reviewers.

4. 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:

   • No, an MRMC comparative effectiveness study was not explicitly mentioned or described. The study focused on comparing two registration methods (manual "Sputnik" vs. automated "Overlay") for the device itself, rather than evaluating human reader performance with or without AI assistance. The "overlay" method is described as an "assisted" registration method, suggesting it might involve some automation, but the study design is not an MRMC study comparing human performance.

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

   • The "overlay" method is described as an "assisted" registration method, implying some level of automation. The testing compared this "overlay" method to the "current manual 'Sputnik' method." The study's focus was on establishing the equivalence of the automated/assisted registration process, which is a standalone function of the software, to the existing manual method. Therefore, a standalone performance assessment of the "overlay" method's accuracy in registration, compared to a manual method, was performed.

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

   • The ground truth for registration accuracy appears to be derived from direct comparisons between the outcomes of the "sputnik" and "overlay" methods. The statement "The primary test was to establish equivalence of the two methods of registration by testing that the accuracy in translational (x,y) and cyclotorsional registration for the 'overlay' method are at least as good as with the current 'sputnik' method" suggests that the "sputnik" method provides a baseline or reference against which the "overlay" method's accuracy is measured. This implies a comparative ground truth derived from a reference method rather than an independent expert consensus, pathology, or outcomes data.

7. The sample size for the training set:

   • This information is not provided in the document. The document describes testing the automated registration process, but does not detail the development or training of the "overlay" software.

8. How the ground truth for the training set was established:

   • This information is not provided in the document. Since the training set size is not mentioned, the method for establishing its ground truth is also absent.

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The OPD-Station software is indicated for use in analyzing the corneal shape and refractive powers measured by the OPD-Scan Models ARK-9000 or ARK-10000, and to display the data in the form of maps, and manage the data.

Nidek has developed a stand-alone software option for users of the OPD-Scan™ device called OPD-Station, which will run on an independent PC (i.e., separate from the OPD-Scan™ device). The OPD-Station software is able to access data measured by the OPD-Scan™ device via a separate Nidek data management software package called NAVIS.

The OPD-Station uses the same software as that used for the OPD-Scan device so that a physician can view OPD-Scan data on their PC of choice. However, the OPD-Station software has the following new functions:

• Maps of Point Spread Function (PSF), Modulation Transfer Function (MTF), MTF . graphing, and Visual Acuity mapping
• Improved color mapping .

The provided text describes the Nidek Incorporated OPD-Station software, which is a standalone software option for users of the OPD-Scan™ device. The software analyzes corneal shape and refractive powers measured by the OPD-Scan Models ARK-9000 or ARK-10000, displaying the data in various maps and managing the data.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. A table of acceptance criteria and the reported device performance

The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than setting specific performance acceptance criteria like sensitivity, specificity, or accuracy metrics. The primary "acceptance criterion" implied throughout this document is substantial equivalence to the predicate devices.

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

The document does not specify any sample size used for a test set (clinical or otherwise) or the data provenance (e.g., country of origin, retrospective/prospective). The performance data mentioned is described very generally as indicating "all specified requirements" without detailing the nature of this data or how it was gathered.

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

The document does not mention any experts used to establish ground truth or their qualifications. Given that this is a 510(k) for software intended to display and manage existing data from an already cleared device (OPD-Scan), the focus is on the software's functionality and its output being consistent with the predicate device's capabilities, rather than a clinical accuracy study requiring expert adjudication of a test set.

4. Adjudication method for the test set

The document does not describe any adjudication method.

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

An MRMC comparative effectiveness study was not conducted or mentioned. The OPD-Station software is not described as an AI-assisted device directly improving human reader performance but rather as a tool for analyzing and displaying existing data from another ophthalmic device.

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

The document is about a standalone software ("OPD-Station software") that operates independently on a PC to analyze and display data from the OPD-Scan device. While it's standalone software, the performance described is its ability to access and process data from the OPD-Scan and display it; it's not an "algorithm only" performance in the sense of an AI algorithm making diagnostic decisions without human involvement. The software itself is the "device" in question operating independently.

7. The type of ground truth used

The document does not specify the type of ground truth used. The verification process appears to rely on comparing the new software's functionality and output with that of the predicate devices. This implies that the "ground truth" for demonstrating equivalence would be the established functionality and output of the cleared predicate devices.

8. The sample size for the training set

The document does not mention a training set or its sample size. This suggests that the software development did not involve machine learning or AI models that require training sets. The software's design likely follows deterministic algorithms based on established ophthalmic principles and data processing techniques from the original OPD-Scan.

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

As there is no mention of a training set, there is no information on how its ground truth would have been established.

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The LADARWave" CustomCornea® Wavefront System is used for measuring, recording, and analyzing visual aberrations (such as myopia, hyperopia, astigmatism, coma and spherical aberration) and for displaying refractive error maps of the eye to assist in prescribing refractive corrections. This device is enabled to export wavefront data and associated anatomical registration information to a compatible treatment laser with an indication for wavefront-guided refractive surgery.

The LADARWave" CustomCornea® Wavefront System is an aberrometer, utilizing Hartmann-Shack wavefront sensing to measure the aberrations in the human eye. The device contains four major optical subsystems used in the clinical wavefront examination: a fixation subsystem, a video subsystem, a probe beam subsystem, and a wavefront detection subsystem. These subsystems are all under control of the device software.

The provided document is a 510(k) summary for the Alcon LADARWave™ CustomCornea® Wavefront System. This type of regulatory submission primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed clinical study results or performance against specific acceptance criteria in the manner one might find for novel or high-risk devices.

Therefore, much of the requested information regarding acceptance criteria, study details, sample sizes, and ground truth establishment is not available in this document. The document confirms that the device is an aberrometer used for measuring, recording, and analyzing visual aberrations and displaying refractive error maps to assist in prescribing refractive corrections. It also states that the device can export data to a compatible treatment laser for wavefront-guided refractive surgery.

Here's a breakdown of the available information based on your request:

Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics from a study designed to prove the device meets these criteria. Instead, it relies on demonstrating substantial equivalence to predicate devices.

Table of Acceptance Criteria and Reported Device Performance:

Study Details

The document does not describe a specific clinical study with test sets, ground truth, or statistical analysis in the way modern AI/ML device submissions would. It refers to the device's characteristics and its equivalence to other diagnostic devices.

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

• Not available. The document does not describe a test set or its provenance.

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

• Not available. Ground truth establishment is not discussed.

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

• Not available. Adjudication methods are not discussed.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• Not available. No MRMC study is mentioned.

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

• The device is a diagnostic tool designed to measure and analyze. Its function is inherently standalone in gathering the measurements, but the application of its output (prescribing corrections, guiding surgery) involves a human in the loop. The document doesn't detail a specific "standalone performance" study as would be expected for an AI algorithm. Its performance is based on the accuracy and reliability of its measurements compared to established methods.

7. The type of ground truth used:

• Not explicitly stated in the context of a "study" for acceptance. Given the nature of a refractometer, the "ground truth" for its measurements would typically be established through comparison with other accepted methods of refractive error measurement (e.g., subjective refraction, retinoscopy) or physical phaco-optics models, which the document alludes to by comparing it to predicate devices that measure refractive characteristics.

8. The sample size for the training set:

• Not applicable/Not available. This device predates the widespread use of large-scale machine learning and "training sets" in the modern sense. Its design and "knowledge" are based on optical physics and engineering principles, not statistical learning from a dataset.

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

• Not applicable/Not available. See point 8.

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