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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 VISX WaveScan™ Wavefront System is a diagnostic instrument indicated for the automated measurement and analysis of refractive errors of the eye including hyperopia and myopia from +6.00 to -8.00 diopters spherical, and astigmatism from 0.00 to -6.00 diopters.

The WaveScan™ Wavefront System Model HS 1 autorefractor device is a diagnostic instrument designed to measure refractive error of the eye automatically by use of wavefront technology. Light travels in a procession of flat sheets known as wavefronts. As these wavefronts pass through an imperfect refractive medium including the cornea and the lens, the aberrations which are created by the irregular surfaces "wrinkle" the light rays and create wavefront errors or distortions. The instrument contains tiny sensors which measure the gradient, or slope, of the wavefront which emanate from the eye. After light travels through the eye's optical system and out again, the sensors accurately detect slight variations of wavefront irregularities as they exit the eye. The sensors then provide additional information within the confines of the instrument through a series of lenses and apertures which are subject to mathematical algorithms and software. Once analyzed by the computer, a refractive error read-out is provided to the user. This analysis is made from multiple points of light which precisely pinpoint variations in refractive status across the entrance pupil of the eye. This allows for the high level of accuracy of the instrument thus providing the user with very precise readings of refractive error.

Here's an analysis of the provided text, broken down by your requested categories:

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

The document doesn't explicitly state "acceptance criteria" with numerical targets for accuracy, reproducibility, or other performance metrics. Instead, it describes a
comparison study against a predicate device (Canon R-50m) to demonstrate equivalence. The reported performance is relative to this predicate.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: The tests were conducted using a "model test eye developed by VISX, Inc. and modeled after the Gullstrand Standard Test Eye Model." Each test condition (combinations of myopic, hyperopic, and astigmatic errors) was repeated five times. The exact number of "test conditions" or specific refractive error combinations is not quantified, so a precise sample size for the test set in terms of individual measurements cannot be determined from the provided text.
• Data Provenance:
  • Origin: The model test eye was "developed by VISX, Inc."
  • Retrospective/Prospective: Neither. The testing was a bench study using a physical eye model, not human data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable. The ground truth for the test set was established by the design of the "model test eye" which was constructed to represent specific refractive errors. There were no human experts involved in establishing the ground truth for this bench testing.

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

Not applicable. There was no human adjudication as the testing was done against a physical model with known characteristics.

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 document describes the performance of an automated diagnostic instrument (autorefractor) directly measuring refractive error. It is a standalone device, and no human-in-the-loop or MRMC study comparing human readers with and without AI assistance was performed or described.

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

Yes, a standalone performance evaluation was conducted. The WaveScan™ Wavefront System Model HS 1 is an "autorefractor device" designed to "automatically measure refractive error of the eye." The testing described is directly evaluating the device's ability to measure refractive errors on its own using a test eye model.

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

The ground truth used was known, precisely engineered refractive errors as embodied in the "Gullstrand Standard Test Eye Model" on which the VISX model eye was based.

8. The sample size for the training set

Not applicable. This device is an autorefractor, which uses optical principles and mathematical algorithms to determine refractive error. It is not described as a machine learning or AI device that requires a "training set" in the conventional sense. Its algorithms are based on established physics and optics.

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 algorithm.

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