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The Bioptigen EnFocus™ device is intended to acquire, process, display and save depth resolved images of ocular tissue microstructure using Spectral Domain Optical Coherence Tomography (SDOCT).

The EnFocus™ is indicated for use as an aid in the visualization of physiologic and pathologic conditions of the eye through non-contact optical imaging. It is indicated for use on patient populations from premature and neonatal infants to adult. The system is indicated for use in supine imaging, mounted to a surgical microscope, with cooperative patients or patients under anesthesia.

The EnFocus™ is a non-contact, noninvasive ophthalmic imaging device that includes an OCT engine, a scan head and a system computer with system software. The EnFocus™ uses Spectral Domain Optical Coherence Tomography (SD-OCT) and a near infrared light source to image ocular tissue microstructures.

The EnFocus™ is coupled to a surgical microscope for OCT imaging during ophthalmic surgical procedures. The software, InVivoVue™, works with the hardware and the controller to offer intuitive, flexible system control for high-speed volume data acquisition and imaging.

The EnFocus™ system includes two OCT-compatible objective lenses for use with the surgical microscope: a 175mm lens and 200mm lens. The system also offers a choice of accessory masks that may be deployed to manage illumination glare artifacts when necessary.

Using the EnFocus™, OCT imaging may be acquired during the surgical procedure, without stopping a procedure or repositioning the surgical microscope. The surgical microscope position is stationary relative to the surgical procedure, and the surgical view is unaltered by the scanning of the OCT beam.

The provided document describes a 510(k) submission for the Bioptigen EnFocus™ 2300 and EnFocus™ 4400 devices, which are Spectral Domain Optical Coherence Tomography (SD-OCT) systems. The submission is for a modification to an already cleared device (K150722), involving repackaging optical and computing subsystems into a single enclosure.

The core of the submission is to demonstrate substantial equivalence to the predicate device, not necessarily to prove effectiveness against clinical outcomes in a new performance study. Therefore, traditional "acceptance criteria" for a new AI/CADe device, and a study proving those criteria, are not presented in the same way. Instead, the focus is on maintaining the safety and performance of the existing predicate device.

Here's an analysis based on the provided text, addressing your questions where possible:

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

The document does not explicitly state "acceptance criteria" with numerical targets for clinical performance (e.g., sensitivity, specificity for a diagnostic task) because this is a 510(k) for a hardware and software modification to maintain equivalence, not to establish new clinical performance.

Instead, the performance evaluation in this 510(k) is aimed at demonstrating that the modified device retains the same performance characteristics as the predicate device. The section "VII. Substantial Equivalence" provides a table comparing the predicate device and the subject (modified) device across various categories, noting "Same as predicate" for most performance metrics.

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

The document states under "IX. Non-Clinical Testing" that "Image quality testing validates that the OCT image quality of the modified EnFocus™ device is statistically equivalent to the original device." It does not specify the sample size of images used for this testing, nor does it mention the provenance (country of origin, retrospective/prospective) of these images. The study's primary goal was engineering validation of the modifications, not a new clinical performance study.

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)

No information is provided about experts establishing ground truth or their qualifications. The testing described focuses on technical performance metrics (electrical safety, optical performance, image quality equivalence), rather than diagnostic accuracy requiring expert interpretation.

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

No information about an adjudication method is provided, as the testing was not clinical diagnostic performance testing.

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 MRMC study was performed or is mentioned. The device is an imaging system (SD-OCT), not an AI-assisted diagnostic tool, and the submission is for a modification to the device's hardware and software, not for a new AI feature.

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

This section is not applicable. The device is a diagnostic imaging instrument; its function is to acquire and display images for human interpretation, not to provide an automated diagnostic output. The "software" mentioned (InVivoVue™) controls data acquisition and imaging, not image analysis for diagnostic purposes in an AI sense.

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

Given the nature of the validation tests (electrical safety, optical performance, image quality equivalence), the "ground truth" would be established by technical measurement standards and comparison to the predicate device's measured performance. For "Image quality testing," it means establishing that the modified device's image quality metrics statistically match those of the predicate, rather than being compared against a clinical ground truth like pathology for a specific disease.

8. The sample size for the training set

This is not applicable since this is not an AI/machine learning device that requires a training set in that context. The "software" mentioned (InVivoVue™) is operating system and image acquisition/display software, not a deep learning model.

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

Not applicable for the reasons stated above.

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