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VITALConnect, Version 4.1 is a medical diagnostic software system intended to process. analyze, review, and distribute multi-dimensional digital Images acquired from a variety of imaging devices including: CT, MR, CR/DR/DX, SC, US, NM, PET, XA, and RF, etc. VTALConnect is not meant for primary image interpretation in mammography. In addition. the VITALConnect system has the following specific intended use:

Vessel Probe is intended for viewing the anatomy and pathology of a patient's coronary arteries. Clinicians select any artery to view the following anatomical references: the highted vessel in 3D, two rotate-able curved MPR vessel views displayed at angles orthogonal to each other, and cross sections of the vessel. Cross sectional measurements can be obtained using standard Vital Images software measuring tools. Clinicians can manually measure the lumen width to obtain percentage stenosis calculations, based on the ratio of the smallest to the largest diameter. In addition, clinicians can manually measure vessel length along the centerline in standard curved MPR views and examine Hounsfield Units statistics.

CT Coronary Artery Analysis is intended for viewing the anatomy and pathology of a patient's coronary arteries. Clinicians can select any coronary artery to view the following anatomical references: the highlighted vessel in 3D, two rotatable curved MPR vessel views displayed at 90 degree angles to each other, and cross sections of the clinician can semiautomatically determine contrasted lumen boundaries, stenosis measurements, and maximum and minimum lumen diameters. In addition, clinicians can edit lumen boundaries and examine Houndsfield unit statistics.

The ViTALConnect system is a medical diagnostic device that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a variety of imaging devices.

The ViTALConnect system provides multi-dimensional visualization of digital images to aid clinicians in their analysis of anatomy and pathology. The ViTALConnect system user interface follows typical clinical workflow patterns to process, review, and analyze digital images, including:

• Retrieve image data over the network via DICOM .
• Display images that are automatically adapted to exam type via dedicated protocols .
• Select images for closer examination from a gallery of up to six 2D or 3D views .
• . Interactively manipulate an image in real-time to visualize anatomy and pathology
• . Annotate, tag, measure, and record selected views
• Output selected views to standard film or paper printers, or post a report to an Intranet . Web server or export views to another DICOM device
• Retrieve reports that are archived on a Web server .

The provided text does NOT describe specific acceptance criteria with numerical targets (e.g., sensitivity, specificity, accuracy thresholds) or a detailed study proving the device meets said criteria.

Instead, it offers a general overview of the device, its intended use, a comparison to predicate devices, and a high-level summary of the validation process.

Here's a breakdown of the requested information based on the provided text, highlighting what is missing:

• "The ViTALConnect 4.1 system will successfully complete integration testing/verification testing prior to Beta validation."
• "Software Beta testing/validation will be successfully completed prior to release."
  This indicates a general requirement for successful completion of tests, but no quantifiable performance metrics are reported. |
  | 2. Sample size used for the test set and the data provenance | Not specified. The document mentions "Software Beta testing/validation" but does not provide details on the sample size of cases/images used in this testing, nor the country of origin of the data or whether it was retrospective or prospective. |
  | 3. Number of experts used to establish the ground truth for the test set and their qualifications | Not specified. The document does not mention the use of experts to establish a ground truth for any test set or their qualifications. |
  | 4. Adjudication method for the test set | Not specified. There is no mention of any adjudication method (e.g., 2+1, 3+1, none) for a test set. |
  | 5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size. | No. The document does not mention an MRMC comparative effectiveness study or any effect size related to human readers improving with AI assistance. The device is a "Medical Image Processing Software" intended for clinicians to "process, analyze, review," and in the case of "CT Coronary Artery Analysis," it can "semiautomatically determine contrasted lumen boundaries, stenosis measurements," but there is no study described that compares human performance with and without this assistance. |
  | 6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done | Not explicitly stated as a standalone performance evaluation with metrics. The description of "CT Coronary Artery Analysis" suggests semi-automatic functions (e.g., "semiautomatically determine contrasted lumen boundaries"), implying an algorithm-only component. However, the document does not report specific standalone performance metrics (like sensitivity, specificity, or accuracy) for these semi-automatic functions. The overall system is described as assisting clinicians in their analysis and measurements, not as a fully autonomous diagnostic tool. |
  | 7. The type of ground truth used | Not specified. Since no specific performance studies or test sets with ground truth are detailed, the type of ground truth (e.g., expert consensus, pathology, outcomes data) is not mentioned. |
  | 8. The sample size for the training set | Not specified. The document refers to the general software development process ("designed, developed, tested, and validated according to written procedures") but does not provide any details about a training set or its size, which would typically be associated with machine learning development. Given the 2007 submission date, the device likely predates widespread deep learning applications with distinct "training sets" as understood today. |
  | 9. How the ground truth for the training set was established | Not applicable/Not specified. As no training set is mentioned, naturally, how its ground truth was established is not discussed. |

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