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Novo is a software only device intended for viewing and management of digital images of scanned surgical pathology slides prepared from formalin-fixed paraffin embedded (FFPE) tissue. It is an aid to the pathologist to review, interpret, and manage digital images of these slides for primary diagnosis. Novo is not intended for use with frozen sections, cytology, or non- FFPE hematopathology specimens.

It is the responsibility of a qualified pathologist to employ appropriate procedures and safeguards to assure the quality of the images obtained and, where necessary, use conventional light microscopy review when making a diagnostic decision. Novo is intended for use with the Philips Ultra Fast Scanner and the Barco PP27QHD or Philips PS27QHDCR display.

The PathAI Novo device is a web-based software-only device that is intended to aid pathology professionals in the viewing, interpretation, and management of digital whole slide images (WSIs) of scanned surgical pathology slides prepared from formalin-fixed paraffin embedded (FFPE) tissue using the Philips IntelliSite Pathology Solution (PIPS) Ultra Fast Scanner (UFS).

The proposed device is typically operated as follows:

• 1. A user prepares and scans slides and reviews the slide quality in accordance with the PIPS UFS IFU and standard lab procedures. The Novo device workflow is initiated when a user uploads WSIs from the local file system to the cloud storage using Novo.
• 2. After uploading WSIs to cloud storage using Novo, a user builds a patient accession using the patient's medical record number (MRN), date of birth (DOB) and accession ID to support linkage of one or more slides from a single procedure using patient identifiers in Novo.
• 3. A pathologist uses the slide viewer to perform their primary diagnosis workflow including zooming and panning images.

After viewing all images belonging to a particular accession, the pathologist will make a diagnosis.

The provided text describes the regulatory clearance for the "Novo" device, a software-only whole slide imaging system, and references a clinical study conducted to establish its substantial equivalence to a predicate device. However, the document primarily focuses on regulatory approval and does not contain the detailed acceptance criteria table or comprehensive study breakdown as requested in the prompt.

Therefore, the following response will extract what is available and highlight where information is missing based on your request.

Acceptance Criteria and Device Performance for Novo (as described by available information)

Based on the provided FDA 510(k) summary, details regarding specific quantifiable acceptance criteria and performance beyond a non-inferiority finding are limited. The document focuses on demonstrating substantial equivalence to a predicate device (Philips IntelliSite Pathology Solution - PIPS).

Table of Acceptance Criteria and Reported Device Performance

Missing Information (Not Available in the Provided Text):

• Explicit, pre-specified quantitative acceptance criteria beyond the non-inferiority margin for major discordance.
• Detailed quantitative performance for features like color differences beyond a statement of similarity to JPEG-compressed images.

Study Details (Extracted from the provided text):

2. Sample size used for the test set and the data provenance:

• Test Set Sample Size: Not explicitly stated. The text mentions "WSIs of H&E stained FFPE tissue slides."
• Data Provenance: Not specified for the test set. (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The reference diagnosis (ground truth) was the "original sign-out pathologic diagnosis using MO [manual optical] rendered at the institution," implying involvement of qualified pathologists for routine diagnosis, but specific qualifications for ground truth establishment are not given.

4. Adjudication method for the test set:

• Adjudication Method: Not specified. The study compared "major discordance rates between MD [manual digital read, using Novo] and MO [manual optical] when compared to the reference (main) diagnosis, which was the original sign-out pathologic diagnosis using MO [ground truth, (GT)] rendered at the institution." This suggests a comparison against an existing diagnosis, not necessarily a separate adjudication process for the test set.

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:

• MRMC Study: Yes, a clinical study was conducted. It involved human pathologists making diagnoses using both the Novo device (MD) and conventional microscopy (MO).
• Effect Size of Improvement: The study focused on non-inferiority of the manual digital read (MD) using Novo compared to manual optical (MO) read. It did not measure "how much human readers improve with AI vs without AI assistance" because Novo is described as a "viewing and management" tool, not an AI-assisted diagnostic tool. Its purpose is to present the image for the pathologist's primary diagnosis, making it a replacement for conventional microscopy, not an enhancement tool for human readers in the context of an AI-assisted workflow. The primary outcome was maintaining diagnostic accuracy (non-inferiority) when switching from MO to MD.

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

• Standalone Performance: No, this device is a "viewer and management" software for human pathologists. It does not perform diagnostic algorithms independently. The performance data presented (non-inferiority) is explicitly human-in-the-loop (MD: "manual digital read").

7. The type of ground truth used:

• Ground Truth Type: "The original sign-out pathologic diagnosis using MO [manual optical] rendered at the institution." This can be categorized as expert consensus/clinical outcomes data based on standard practice.

8. The sample size for the training set:

• Training Set Sample Size: Not applicable/Not specified. Novo is a viewing and management system, not described as an AI/machine learning algorithm that requires a "training set" in the conventional sense for a diagnostic prediction model. The "pixel-wise comparison" tests were likely technical assessments, not dependent on a "training set."

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

• Ground Truth Establishment for Training Set: Not applicable, as there is no mention of an algorithm requiring a "training set" with ground truth in the provided information.

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