The LFR-260 is a portable manual refractor providing capabilities to the eyecare provider to perform distance vision testing and subjectively measure sphere, cylinder and axis refractive errors in patients aged from 12-65 years old with healthy visual systems. The device measures spherical error in the range of -10 to +15D and measures cylinder error within +/- 2.5D.

The LFR-260 is a portable digital refractor which allows for determination of refractive error as well as for fully remote refractions. The LFR-260 achieves the similar functionality as Comprehensive Phoropter, MDR-680, which is a standard refractor, but relies on the use of a micro lens array, a highdensity embedded display, and a tunable lens. The reduced number of lenses allows for higher portability and a smaller footprint.

Here's an analysis of the provided text regarding the acceptance criteria and study for the LFR-260 device:

1. Table of Acceptance Criteria and Reported Device Performance

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The Topcon Model BV-1000 Automated Subjective Refraction System provides sphere, cylinder, and axis measurements of the eye. The BV-1000 assists the eyecare professional in evaluating pre and post operative eye procedures and is used as an aid in prescribing eyeglasses and contact lenses.

The Topcon Model BV-1000 is a safe and effective instrument. In essence, it is a combination of three Class I devices:

1. Ophthalmic Refractometer ... an AC-powered device that consist of a fixation system, a measurement and recording system and an alignment system.
2. Visual Acuity Chart ... a device, with a Landolt "C" chart in graduated sizes to test visual acuity
3. Onthalmic Motorized Refractor ... a device that incorporates a set of lenses of various dioptric powers intended to measure the refractive power of the eyc.
   The BV-1000 is designed to perform binocular, simultaneous auto-refraction. It incorporates subjective refinement steps after the objective measurements have been obtained. The BV-1000 reduces the amount of time that eyecare professionals need to spend in refracting their patients as a substantial portion of the traditional refraction can be accomplished in the "pre test" room.

The provided document is a 510(k) summary for the Topcon Model BV-1000 Automated Subjective Refraction System. Based on the information available, a detailed description of acceptance criteria and the study proving it is not present in the typical format of an AI/ML device study.

This document describes a medical device from 2003, which predates the widespread regulatory framework for AI/ML medical devices. Therefore, the information requested, particularly regarding AI-specific criteria like training sets, ground truth establishment for AI, MRMC studies for AI assistance, and standalone AI performance, will not be found in this document.

However, I can extract information related to the device's performance specifications and how it was compared to predicate devices, which serves as a form of "acceptance criteria" and "study" in the context of a 510(k) submission from that era.

Here's the breakdown of what can be inferred and what is not available based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" as pass/fail thresholds against which the device was tested. Instead, it demonstrates substantial equivalence by comparing the BV-1000's performance specifications, particularly its measurement ranges, with those of legally marketed predicate devices. The implicit acceptance criteria are that the device's performance falls within a comparable and safe range to these predicates.

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

• Sample Size: Not explicitly stated. The document focuses on performance specifications and comparison to predicates, not a clinical trial with a specific patient sample size for testing.
• Data Provenance: Not specified. Given it's a 510(k) from 2003, such details were often less rigorously documented in the summary unless critical for equivalence demonstrations (e.g., specific clinical study data if equivalence was not clear from technological comparison). It's likely based on internal testing and engineering assessments rather than a large clinical test set described in this summary.

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

• This information is not available and is not relevant for this type of device and submission from this era. "Ground truth" in the context of refractive measurements is often established by established clinical methods (e.g., best-corrected visual acuity determined by an optometrist/ophthalmologist) or comparison to existing gold-standard devices. Experts would be involved in designing and interpreting the performance data, but not typically in the "ground truth" establishment as understood in AI/ML validation studies.

4. Adjudication method for the test set

• Not applicable/Not available. Adjudication methods are typically associated with resolving discrepancies in expert labeling or diagnoses, especially in AI/ML studies. This document doesn't describe a study design that would necessitate such a 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

• No, this device is not an AI-assisted device. Therefore, an MRMC study comparing human readers with and without AI assistance was not conducted and is not applicable. The device's purpose is to assist eyecare professionals by providing objective measurements and streamlining subjective refinement, not to provide AI diagnostics or interpretations.

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

• Not applicable. This device is a piece of hardware that takes measurements. It is not an algorithm designed to perform diagnostics "stand-alone." Its output (sphere, cylinder, axis measurements) still requires interpretation and use by an eyecare professional. The "Automated Subjective Refraction System" name implies it automates parts of the subjective refraction process, but the human is still in the loop for the overall prescription.

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

• For a device like this, ground truth would implicitly be established through comparison to established clinical methods and predicate devices. For example, the accuracy of its refractive measurements would be compared against the results obtained by experienced clinicians using traditional phoropters or other auto-refractors considered gold standards at the time. The document doesn't detail the specific ground truth process but relies on the device producing measurements within expected clinical ranges, comparable to predicates.

8. The sample size for the training set

• Not applicable/Not available. This is not an AI/ML device, so there is no "training set" in the sense of data used to train a machine learning model. The device's "training" would be its engineering design, calibration, and validation against physical measurement standards and clinical performance expectations.

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

• Not applicable. As a non-AI/ML device, there is no "training set ground truth" as understood in current AI/ML terminology.

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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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