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The CONVIVO In Vivo Pathology Suite is a system that contains a surgical endo-microscope and a cloud software module. The Suite allows for real-time, remote collaboration between various clinical teams (i.e., neurosurgical and pathology). The device is not intended for diagnostic purposes or to replace standard practices for tumor margin analysis and frozen sections procedures as part of intra-operative consultation.

CONVIVO Surgical Workplace is a surgical endo-microscope that acquires data and creates in-vivo images and image sequences of tissue microstructure. CONVIVO's fiber optic scanner probe is placed in direct contact with tissue during cranial procedures to create in-vivo confocal laser scanning images of the internal microstructure of tissues.

CONVIVO Pathology Workplace (cloud software module) can categorize, archive, and store images created by the acquisition device (such as CONVIVO Surgical Workplace).

The CONVIVO Surgical Workplace is a Confocal Laser Endomicroscopy (CEM) system intended to create in vivo confocal laser scanning images of the microvasculature and microstructures of the tissue. The system can be applied during surgical procedures and is to be used in direct contact with the tissue. The system is comprised of a confocal processor, handheld scanner probe, computer, touchscreen monitor, cart, and foot control panel.

Additionally, CONVIVO Surgical Workplace has been configured as a part of a larger CONVIVO In Vivo Pathology Suite which contains the previously cleared system and now includes a cloud-based medical image management and processing system software module (Pathology Workplace) that allows for real-time intraoperative collaboration between surgical teams and pathologists.

The provided FDA 510(k) summary for the CONVIVO In Vivo Pathology Suite focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific acceptance criteria through a clinical study.

Therefore, much of the requested information regarding acceptance criteria, device performance, sample sizes, ground truth establishment, expert qualifications, and MRMC studies is not available in the provided text.

The document primarily describes a technical and functional comparison between the subject device and its predicate (CONVIVO, K181116) and a reference device (AI Metrics, K202229), along with verification and validation testing for software and hardware changes.

Here's a breakdown of the available information based on your request:

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

This information is not explicitly provided in terms of numerical performance metrics linked to specific acceptance criteria for diagnostic accuracy or clinical utility. The submission focuses on demonstrating equivalence in intended use and technical characteristics.

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

This information is not available in the provided text. No details are given about a test set or data provenance for a 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)

This information is not available in the provided text. Ground truth establishment for a test set is not described.

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

This information is not available in the provided text. Adjudication methods are not discussed.

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

There is no indication that an MRMC comparative effectiveness study was done. The device's cloud software module (Pathology Workplace) is described as allowing "real-time, remote collaboration between various clinical teams (i.e., neurosurgical and pathology)," but this is a functionality description, not a claim of AI assistance or improved reader performance that would necessitate an MRMC study.

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

The "CONVIVO Pathology Workplace (cloud software module)" can categorize, archive, and store images. However, the device "is not intended for diagnostic purposes or to replace standard practices for tumor margin analysis and frozen sections procedures as part of intra-operative consultation." Furthermore, "The medical professional retains the ultimate responsibility for making the pertinent diagnosis based on their standard practices. The software is a complement to these standard procedures." This strongly suggests the device is not a standalone diagnostic algorithm requiring standalone performance evaluation. Its role is collaborative and supportive, not AI-driven interpretation.

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

This information is not available in the provided text, as no clinical performance study with defined ground truth is described.

8. The sample size for the training set

This information is not available in the provided text. Since no AI/algorithm for diagnostic purposes is described, a "training set" in the context of machine learning is not mentioned.

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

This information is not available in the provided text.

Summary of available study information:

The document states:

• Study Title: "Summarv of the Studies"
• Purpose: To ensure that all requirements for proposed changes have been met for the CONVIVO In Vivo Pathology Suite.
• Studies Performed:
  • Updated Shelf-Life testing for an extension of shelf-life for a system component.
  • Updated EMC (Electromagnetic Compatibility) and Electrical Safety Testing for hardware life-cycle changes.
  • Updated System and Software Testing for software and interoperability changes.
• Methodology: All testing followed internally approved procedures and processes, which are in compliance with referenced standards and FDA guidance documents.
• Conclusion: The subject device was deemed substantially equivalent to the predicate and reference devices in terms of safety and effectiveness based on intended use, technological characteristics, and testing methods.

In essence, the "study" described is a series of verification and validation tests for engineering and software changes to demonstrate equivalence, not a clinical performance study with diagnostic accuracy or reader performance as endpoints.

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The ZEISS CONVIVO is a surgical endomicroscope intended for viewing intra-operative blood flow in the cerebral vascular area, including microvasculature and capillaries.

The CONVIVO's fiber optic scanner probe is placed in direct contact with tissue during cranial diagnostic and therapeutic procedures, such as tumor biopsy and resection, to create in-vivo confocal laser scanning images of the internal microstructure of tissues.

The CONVIVO is a Confocal Laser Endomicroscopy (CEM) system intended to create in vivo confocal laser scanning images of the microvasculature and microstructures of the tissue. The system can be applied during surgical procedures and is to be used in direct contact with the tissue.

The system is comprised of a confocal processor, handheld scanner probe, computer, touchscreen monitor, cart and foot control panel.

The Sterile Sheath for CONVIVO is a mandatory single use accessory to the system, intended to provide a sterile barrier between the scanner and the patient's tissue. Before the scanner is used on the patient, the Sterile Sheath is fitted to the scanner using sterile techniques.

Sodium fluorescein is used as a fluorescence contrast agent to visualize intraoperative cerebral vascular area, including microvasculature/capillaries. Sodium fluorescein can be used as contrast agent with CONVIVO without changes to the formulation, mode of action, approved dose or route of administration; it is systemically administered and its delivery is independent of CONVIVO.

The provided text is a 510(k) summary for the CONVIVO device. While it details various aspects of device testing, it does not contain a specific table of acceptance criteria and reported device performance in the way typically seen for a study proving a device meets acceptance criteria related to a diagnostic or AI-assisted system's performance metrics (e.g., sensitivity, specificity, accuracy).

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (Cellvizio 100 Series System) and a reference device (YELLOW 560) through engineering and safety testing, rather than a clinical performance study with specific quantitative acceptance criteria for diagnostic efficacy.

Therefore, many of the requested details cannot be extracted directly from this document. However, I can infer some information based on the provided text.

Here's what can be gathered and what is explicitly not available from the provided text, in relation to your questions:

1. Table of Acceptance Criteria and Reported Device Performance:

A specific table with quantitative clinical performance acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) and corresponding reported device performance values is not provided in this document. The document describes various types of testing but not in a format that directly addresses a clinical performance study's acceptance metrics. The performance data mentioned are primarily related to safety and functionality.

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

• Sample Size: The document mentions "In vivo Testing" was conducted but does not specify the sample size (e.g., number of patients or images) used for this testing.
• Data Provenance: Not explicitly stated. The in-vivo testing likely occurred in a controlled environment as part of the device development, but details regarding country of origin or whether it was retrospective/prospective are not detailed. It's presented as part of design verification, suggesting internal company testing rather than a large-scale clinical trial.

3. Number of Experts and Qualifications for Ground Truth:

• The document mentions "In vivo Testing" confirming the ability to view microstructures. However, it does not specify the number or qualifications of experts used to establish ground truth for this testing. Given it's a device for direct visualization, the "ground truth" is likely the direct observation by the device itself, validated by experts, but the process is not elaborated.

4. Adjudication Method:

• Since a formal clinical performance study with expert reads/assessments is not described in detail, an adjudication method (2+1, 3+1, etc.) for a test set is not mentioned.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• A MRMC study comparing human readers with and without AI assistance was not conducted or described in this document. This device is a direct imaging tool, not an AI-diagnostic aid in the typical sense that would necessitate an MRMC study to show improvement in human reader performance.

6. Standalone (Algorithm Only) Performance:

• This device is an endomicroscopy system for direct visualization, not an AI algorithm. Therefore, a "standalone algorithm performance" study is not applicable and was not conducted or described. The device's performance is inherently tied to its operation as an imaging system.

7. Type of Ground Truth Used:

• For the "In vivo testing" that "confirmed the ability to view microstructure of tissues and blood flow intraoperatively," the "ground truth" would implicitly be the direct visualization capability of the device itself during the procedure, likely assessed by qualified personnel. However, the document does not specify if this was through pathology confirmation, other imaging modalities, or a consensus of experts retrospectively labeling images.

8. Sample Size for the Training Set:

• This document describes a medical device, not an AI/ML algorithm. Therefore, the concept of a "training set" for machine learning is not applicable, and no sample size for such a set is provided.

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

• As above, the concept of a training set is not applicable here.

In summary, this 510(k) submission primarily focuses on demonstrating substantial equivalence through engineering, safety, and functionality testing. It does not present a clinical performance study with quantitative diagnostic metrics and associated acceptance criteria that would involve detailed information on ground truth establishment, expert reader studies, or AI algorithm training/testing. The "In vivo Testing" mentioned is a high-level confirmation of the device's basic function.

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