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The RTA 5 & RTA Model E Retinal Thickness Analyzer ("RTA 5 & RTA Model E") is a computerized slitlamp biomicroscope that is intended to provide manual and computerized tomography of the retina in vivo. The RTA 5 & RTA Model E scans successive slit images on the fundus, without the need for a contact lens, to determine the thickness and the inner structure of the retina, both by observation of the slit images and by computer analysis of these images. It is indicated for assessing the area and location of retinal thickness abnormalities, such as thickening due to macular edema and atrophy associated with degenerative diseases, and for visualizing other retinal pathologies.

The RTA 5 & RTA Model E is a computerized electro-optical system comprised of two primary components, namely the optical head and the computer system. The main elements of the optical head include laser and conventional light sources, optics, a scanner, and a digital camera. The RTA 5 & RTA Model E is a computerized slitlamp biomicroscope that provides manual and computerized tomography of the retina in vivo. The RTA 5 & RTA Model E scans successive slit images of the fundus to determine the thickness and the inner structure of the retina, both by observation of the slit images and by computer analysis of these images. The RTA 5 & RTA Model E uses a solid-state laser source that emits green light at a wavelength of 532 nm. The beam is focused into a thin slit and, by means of a beam-splitter, is directed toward the eye. The scanner and optics then detect the image of the illuminated portion of the retina and transmit the image to the digital camera. The digital camera then captures the image, where it can then be stored and analyzed by the computer system.

The provided 510(k) summary for the Talia Technology, Ltd.'s RTA 5 & RTA Model E Retinal Thickness Analyzer does not contain specific acceptance criteria or a dedicated study section detailing how the device meets such criteria in the way a modern medical device submission typically would.

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (Talia Technology Ltd.'s RTA Retinal Thickness Analyzer, Model D) by highlighting modifications and stating that these changes do not raise new questions of safety or effectiveness.

Here's an analysis based on the provided text, addressing your points where possible, and noting where information is absent:

Acceptance Criteria and Device Performance (Based on Substantial Equivalence Claim)

The document doesn't outline specific numerical acceptance criteria (e.g., accuracy, sensitivity, specificity thresholds) or present a formal study comparing the RTA 5 & RTA Model E's performance against these criteria. Instead, the "acceptance" is based on demonstrating that the modifications made do not negatively impact the device's original performance or safety, thereby maintaining substantial equivalence to its predicate.

Therefore, the "reported device performance" implicitly refers to the performance of the predicate device, which is assumed to be safe and effective.

Study Details Based on Provided Information:

Given the nature of this 510(k) submission (substantial equivalence for modifications), a formal "study" with a distinct test set, ground truth experts, etc., as one might find for a de novo device, is not explicitly described. Instead, the submission relies on design control assessment, verification, and validation testing of the modifications to ensure they do not alter the substantial equivalence.

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

   • Not explicitly stated. The document mentions "verification and validation testing" for the modifications. However, it does not specify the number of cases or patients used in these tests, nor their provenance.

2. 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 explicitly stated. Since a formal clinical study or performance study with expert-established ground truth is not detailed, this information is not provided. The device's output (retinal thickness measurements and images) is intended for interpretation by a healthcare professional.

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

   • Not explicitly stated. No information is provided regarding an adjudication method for a test set, as a formal performance assessment against a clinical ground truth is not detailed.

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 evidence of an MRMC study. The RTA 5 & RTA Model E is described as a diagnostic imaging device that provides data for human interpretation and computer analysis, not an AI-assisted diagnostic tool that augments human readers in the way an MRMC study would typically evaluate. The "computer analysis" mentioned is for determining thickness and structure from the images, not for providing an AI-driven diagnosis or improving human reader performance.

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

   • No evidence of a standalone algorithm performance study. The device's operation inherently involves "manual and computerized tomography," with output for "observation of the slit images and by computer analysis of these images," implying human involvement in interpretation. It's not presented as a fully automated diagnostic algorithm.

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

   • Not explicitly stated. For the "verification and validation testing" of the modifications, the ground truth would likely refer to engineering specifications, previous RTA Model D performance standards, or internal benchmarks rather than clinical outcomes or pathology. For the predicate device (RTA Model D), the implicit ground truth for its original clearance would have been its ability to accurately measure retinal thickness and image retinal structures, presumably validated against established clinical methods or other reference instruments, but this is not detailed in this document.

7. The sample size for the training set

   • Not applicable / not stated. This submission is for a device with a "computer analysis" component, not necessarily a machine learning or AI algorithm that requires a "training set" in the modern sense. The "software change" is mentioned as a modification, but no details on training data for an AI component are provided.

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

   • Not applicable / not stated. (See point 7).

In summary, the provided 510(k) summary is for a device seeking clearance based on substantial equivalence. It outlines the device's intended use, technological characteristics, and focuses on demonstrating that modifications from a predicate device do not introduce new safety or effectiveness concerns. It does not contain the detailed, formal performance study data typically found in submissions for novel devices or AI/ML-driven diagnostics, which would include specific acceptance criteria, test set details, ground truth establishment, and clinical performance metrics.

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