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The Foresee (4C) Imaging System is intended to be used as an imaging tool in the evaluation of external human tissue microstructure by providing two-dimensional, crosssectional, real-time depth visualization.

The Foresee (4C) Imaging System uses optical coherence tomography (OCT) to create images of tissue. OCT is an imaging technology similar to ultrasound except that images are formed using reflected light rather than reflected sound. In place of sound waves, OCT uses a near-infrared beam of light that penetrates tissue. The light reflects off of changes in tissue microstructure. Using the principles of low-coherence interferometry, a technique that measures the interference of light from a sample arm and a reference arm to create an image, OCT produces a high-resolution depth profile. Scanning the beam across tissue produces a detailed, two-dimensional image of tissue microstructure morphology.

The Foresee (4C) Imaging System uses a broadband, swept optical source and a fixed reference arm to acquire imaging data in the frequency domain. The Foresee (4C) Imaging System has a handheid probe for the scanning of tissue. The probe has a fixed focal depth with a correction for beam diffraction employing a physics-based, software signal processing technique.

The Foresee (4C) Imaging System consists of a cart-mounted imaging console with an isolation transformer; a foot pedal; a handheld imaging probe with a single-use non-sterile disposable probe tip: an imaging module containing a light source, interferometer, and detector; and an imaging computer that implements physics-based software signal processing; and dual monitors for image display. The system user interface allows the viewing, capture, review and export of images.

Here's an analysis of the acceptance criteria and the study used to demonstrate that the Diagnostic Photonics Foresee (4C) Imaging System meets these criteria, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The documentation primarily focuses on demonstrating substantial equivalence to predicate devices and adherence to international safety and performance standards, rather than defining specific numerical clinical performance acceptance criteria (e.g., sensitivity, specificity for a diagnostic task). The "acceptance criteria" here are implied by meeting the specified standards and exhibiting comparable or better physical imaging parameters.

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

• Sample Size (Test Set): For the imaging performance evaluation, the system was tested on "the esophagus, bladder, and colon from healthy New Zealand white rabbit." The exact number of rabbits or tissue samples is not specified.
• Data Provenance: The data is from in vitro testing using animal tissue (New Zealand white rabbit). This is not clinical data from human subjects. The study is prospective in the sense that the system was built and then tested on these samples.

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

The document does not mention the use of experts to establish a "ground truth" for the imaging performance tests on animal tissues. The assessment appears to be based on physical measurements of the system's imaging capabilities (resolution, range, linearity) and visual comparison of image quality to "published predicate data."

4. Adjudication Method for the Test Set

Not applicable, as expert adjudication for a ground truth is not described. The performance assessment is based on objective physical measurements and subjective comparison to predicate data.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study was done, and its Effect Size

No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not conducted or described. The device is an imaging system (Optical Coherence Tomography scanner), not an AI-powered diagnostic algorithm designed to assist human readers in interpretation.

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

Yes, the performance data presented is for the device operating in a standalone capacity (imaging system performance measurements). There is no mention of an algorithm in the context of diagnostic interpretation that would require a human-in-the-loop study. The "physics-based, software signal processing technique" mentioned corrects for beam diffraction and is an integral part of the image acquisition and reconstruction, not a separate diagnostic algorithm.

7. The type of Ground Truth Used

For the imaging performance, the ground truth or reference standard would be the inherent physical properties of the system (e.g., how small an object it can resolve, its depth penetration). For the comparative animal tissue imaging, the "ground truth" appears to be the known microstructure of the rabbit tissues as understood from biological knowledge, viewed through the lens of the Foresee system and compared qualitatively against published predicate data. There is no mention of, for example, corresponding histology (pathology) for the rabbit tissues used in the imaging study.

8. The Sample Size for the Training Set

Not applicable. This document describes the validation of an imaging device, not the training of a machine learning or AI algorithm in the typical sense that would involve a distinct "training set" of images to teach an algorithm to perform a task. The "physics-based, software signal processing technique" likely involves fixed algorithms based on optical principles, not data-driven machine learning that requires a training set.

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

Not applicable, as there is no mention of a training set or data-driven machine learning algorithm that requires ground truth establishment.

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