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The NanoZoomer S360MD Slide scanner system ("NanoZoomer System") is an automated digital slide creation, viewing, and management system. The NanoZoomer System is intended for in vitro diagnostic use as an aid to the pathologist to review and interpret digital images of surgical pathology slides prepared from formalin-fixed paraffin embedded ("FFPE") tissue. The NanoZoomer System is not intended for use with frozen section, cytology, or non-FFPE hematopathology specimens.

The NanoZoomer System comprises the NanoZoomer S360MD Slide scanner, the NZViewMD Software and a compatible display that has been 510(k) cleared for use with the NanoZoomer system or a 510(k)-cleared display that has been assessed in accordance with the Predetermined Change Control Plan (PCCP) for qualifying additional compatible displays. The NanoZoomer System is for creation and viewing of digital images of scanned glass slides that would otherwise be appropriate for manual visualization by conventional light microscopy. It is the responsibility of a qualified pathologist to employ appropriate procedures and safeguards to assure the validity of the interpretation of images obtained using NanoZoomer System.

The NanoZoomer S360MD Slide scanner system is an automated system for creating, viewing, and managing digital slides. The NanoZoomer S360MD Slide scanner system creates diagnosticquality digital images of glass slides containing formalin-fixed paraffin-embedded ("FFPE") tissue. Each digital image covers an entire slide and typically contains billions of image pixels. Slide images may be viewed, stored, retrieved, duplicated, and/or shared, permitting the pathologist to make a primary diagnosis without needing to view the original glass slides through a light microscope.

The NanoZoomer S360MD Slide scanner system is comprised of the NanoZoomer S360MD Slide scanner, NZViewMD image viewing software and compatible display.

The document describes the NanoZoomer S360MD Slide scanner system (K233027), which is an automated digital slide creation, viewing, and management system. This submission primarily focuses on adding compatibility with the BARCO MDPC-8127 Display to the existing NanoZoomer S360MD Slide scanner system (K213883) and establishing a Predetermined Change Control Plan (PCCP) for qualifying additional FDA-cleared displays. No new clinical studies were conducted as part of this submission, as substantial equivalence was demonstrated through non-clinical testing.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are derived from the special controls described under 21 C.F.R. § 864.3700 for Whole Slide Imaging Systems, and color reproducibility testing. The reported device performance indicates that the system met these criteria.

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

• Test Set Sample Size: The document specifies that color reproducibility testing "used three NanoZoomer Systems with BARCO MDPC-8127 displays and was conducted using a color calibration slide and a chroma meter." For the other non-clinical tests (spatial resolution, pixel defects, etc.), the document states "compatibility... was determined based on testing the below specified parameters," but it does not explicitly state the number of displays or specific test slides used for each parameter.
• Data Provenance: Not explicitly stated, but the submission is from Hamamatsu Photonics K.K. (Japan), implying the testing was likely conducted by the manufacturer. The tests are non-clinical, so "retrospective or prospective" is not directly applicable in the typical clinical study sense.

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

Not applicable. The tests conducted were non-clinical, related to display and system compatibility parameters. Ground truth for these tests would be established through objective measurement against technical standards and specifications (e.g., using a color calibration slide and chroma meter for color reproducibility), rather than expert assessment of pathological slides.

4. Adjudication Method for the Test Set

Not applicable. As noted above, the tests were non-clinical technical evaluations and would not involve expert adjudication as typically understood in diagnostic accuracy studies.

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 done. The document explicitly states: "No clinical studies were required to demonstrate substantial equivalence of the modified NanoZoomer S360MD Slide scanner system." This submission focuses on adding a compatible display to an already cleared device and establishing a PCCP, not on evaluating AI assistance or human reader performance.

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

Not applicable. The NanoZoomer S360MD Slide scanner system is a whole slide imaging system intended as an aid to a pathologist, not a standalone AI algorithm for diagnosis. The non-clinical tests evaluated the system's technical performance with a new display component.

7. The Type of Ground Truth Used

For the color reproducibility testing, the ground truth was based on a color calibration slide and objective measurements obtained using a chroma meter. For the other non-clinical tests, the ground truth would be against general engineering and display performance specifications and standards relevant to 21 C.F.R. § 864.3700.

8. The Sample Size for the Training Set

Not applicable. This submission concerns compatibility of a new display with an existing cleared system and a PCCP. There is no mention of a machine learning algorithm being trained as part of this submission. The system is a slide scanner and viewer, not an AI diagnostic tool that requires a training set in the typical sense.

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

Not applicable, as there was no training set for an AI algorithm in this submission.

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