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The SL OCT is an add on for a slit lamp biomicroscope and is intended for use in eye examination of the anterior eye segment, from the corneal epithelium to the posterior capsule. It is used for diagnostic purposes.

The SL-OCT enables non-destructive in-depth measurements of the structure and/or form of the anterior segment of the human eye. The visible structures are distinguished on the basis of their varying optical characteristics at the light wavelength used.

The information can be displayed, analysed and documented by the SL-OCT. To this end, a preinstalled software package - which may only be operated on this system - is supplied with the examination system.

This software records and displays the data and controls the measurement process. In addition. the software allows subsequent editing and saving of data to a database provides no permanent and secure archiving of OCT measurement data. For the documentation, the data are printed out and attached to the patient's medical record.

The images provided by the SL-OCT allow qualitative statements to be made about the dimensions and structures of the anterior segment and the dimensions of the identified structures to be quantified.

The SL-OCT is particularly suitable for non-contact in vivo imaging of the chamber angle and the anterior chamber.

This document is a 510(k) premarket notification for the SL-OCT (Slitlamp Optical Coherence Tomography) device. It describes the device's intended use and compares its technological characteristics to a predicate device, the Haag Streit AG Optical Low Coherence Pachymeter Pachy-01 SL, to demonstrate substantial equivalence.

This submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the way a modern AI/ML device would. Therefore, much of the requested information (like specific performance metrics, sample sizes for test/training sets, expert qualifications, and ground truth establishment) is not applicable or explicitly stated in this type of regulatory document.

However, I can extract information related to the closest aspects of 'acceptance criteria' and 'study' as they appear in this 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are implicitly tied to demonstrating substantial equivalence to the predicate device and meeting safety standards. The performance reported primarily involves comparisons of technical specifications.

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

This document is for a traditional medical device (Optical Coherence Tomography system), not an AI/ML device that would typically involve test sets of data for algorithm performance. The "testing" referred to is for electrical safety and electromagnetic compatibility, not clinical performance on a dataset. Therefore, there is no mention of a test set sample size or data provenance in the context of algorithm performance.

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

Not applicable. As noted above, this device predates modern AI/ML regulatory frameworks and does not involve performance studies on clinical datasets with ground truth established by experts.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

Not applicable for the reasons stated above.

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

Not applicable. This is a standalone imaging device, not an AI-assisted diagnostic tool.

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

Not applicable. There is no "algorithm only" performance for this device in the context of image interpretation or diagnosis; it is an imaging system providing qualitative and quantitative data for human interpretation.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

Not applicable. This document does not describe the use of specific ground truth types for performance evaluation, as it's a hardware device demonstrating substantial equivalence, not a software algorithm being validated.

8. The Sample Size for the Training Set

Not applicable. This device does not use machine learning algorithms that require a training set.

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

Not applicable. This device does not use machine learning algorithms that require ground truth for a training set.

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