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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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This device will be used in the same manner as all ophthalmic diagnostic devices used to obtain ocular measurements (for axial length, anterior chamber depth and corneal radius), and perform calculations to allow physicians to determine appropriate IOL power and type for implantation.

The IOLMaster is a non-invasive, non-contact system for measuring the parameters of the human eye required to determine the appropriate power of IOL for implantation, (axial eye length, anterior chamber depth, and corneal radius), and for calculating the optimal power of IOL.
Axial eye length is measured using the principle of partial coherence interferometry (also referred to as laser Doppler interferometry), with a Michelson interferometer.

Corneal radius is measured using traditional keratometery principles. whereby light from LEDs is projected on the cornea of the eve, and after image capturing of the reflected marks and image processing provides the measurement.

Anterior chamber depth is measured by slit lamp illumination. The slit light is scattered by the cornea and the eye lens, generating an image of the cornea and the lens. The image is captured by a CCD-camera. Image processing and edge detection algorithms allow for calculation of the distance between the anterior surface of cornea and lens ( == anterior chamber depth).

These three measurements provide the physician with the data required to calculate the power of IOL to use for a patient. The physician can then choose from one of up to five internationally accepted formulas, built into the IOLMaster, to perform the calculation. The IOL power is then calculated according to the IOL type.

Users can also enter information regarding the different IOL types into the IOLMaster database, which can then be used to suggest the optimal IOL. This calculation and selection process is already performed by ultrasound and other diagnostic devices. However, the choice of formula and final determination of the appropriate IOL is at the physicians' discretion.

Here's an analysis of the provided 510(k) summary regarding the IOLMaster device, focusing on its acceptance criteria and the study used to validate its performance:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly state "acceptance criteria" in a quantitative manner. Instead, it presents the device's performance as the deviation from established comparative devices. This deviation serves as the functional equivalent of performance criteria, where smaller deviations indicate better agreement and thus, acceptable performance relative to the established gold standards.

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

• Test Set Sample Size:
  • The second stage testing involved 155 human eyes.
  • Prior to this, a "first stage testing" involved 678 human eyes. It's important to note that the detailed performance metrics are given for the 155 human eyes in the second stage testing, which seems to be the primary dataset for the reported deviations.
• Data Provenance: The study was conducted at the University Eye Clinic, Wuerzburg, Germany in February 1999. The data is prospective, as it describes the measurement of human eyes with the IOLMaster prototype and comparative devices.

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

The document does not specify the number of experts used to establish the ground truth. The "ground truth" was established by measurements from existing, cleared devices (Grieshaber Biometry System, AL-1000 A-scan device, ALCON Ocuscan keratometer). These are considered established methods, and the expertise would lie in operating these devices and interpreting their results. There's no mention of a separate expert panel adjudicating the measurements from these objective devices.

4. Adjudication Method for the Test Set

There is no mention of an adjudication method in the traditional sense (e.g., 2+1, 3+1). The "ground truth" was established by direct measurements from the comparative devices, not by human consensus or review of IOLMaster results. The study directly compared IOLMaster measurements to these reference device measurements.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. This study focuses on the agreement between the IOLMaster device and existing measurement devices, not on the improvement of human readers with or without AI assistance. The IOLMaster is a diagnostic measurement device, not an AI-assisted diagnostic aid for human interpretation.

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

Yes, this was a standalone performance study. The IOLMaster directly generates measurements (axial eye length, corneal radii, anterior chamber depth) using its internal algorithms, and these measurements were directly compared against the measurements obtained from the predicate devices. The performance metrics (deviations) are for the device itself. While physicians use the device, the measurements themselves are output by the algorithm.

7. The Type of Ground Truth Used

The ground truth used was measurements obtained from established, high-accuracy comparative devices.

• For Axial Eye Length and Anterior Chamber Depth, the Grieshaber Biometry System ("GBS"), a high-accuracy ultrasound biometry unit in immersion technique, was used as the ground truth. The TOMEY "AL-1000" A-scan device was also mentioned as a comparative device in the broader first-stage testing.
• For Corneal Radii, the ALCON "Ocuscan" keratometer was used as the ground truth.

8. The Sample Size for the Training Set

The document does not explicitly state a sample size for a training set. The study describes validation and verification testing of a "prototype" with specific performance data. While the device utilizes image processing and algorithms, there's no mention of a separate set of data specifically used for machine learning model training. The development process likely involved an internal testing and calibration process, but this is not detailed as a "training set" in the submission. The "first stage testing of 678 human eyes" could be considered part of an early development or internal validation, but it's not explicitly called a training set.

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

Since a "training set" is not explicitly described, the method for establishing its ground truth is also not detailed. If the 678 human eyes in the first stage testing served as an internal development/training set, it would likely have used similar comparative devices as the reported validation study.

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