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Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data derived from DICOM 3.0 compliant CT scans. Hepatic VCAR is designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver lobe, liver segments and liver lesion segmentation and measurement. It is intended for use by clinicians to process, review, archive, print and distribute liver CT studies.

This software will assist the user by providing initial 3D segmentation, visualization, and quantitative analysis of liver anatomy. The user has the ability to adjust the contour and confirm the final segmentation.

Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data derived from DICOM 3.0 compliant CT scans. Hepatic VCAR was designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver, liver lobe, liver segments and liver lesion segmentation and measurement.

Hepatic VCAR is a post processing software medical device built on the Volume Viewer (K041521) platform, and can be deployed on the Advantage Workstation (AW) (K110834) and AW Server (K081985) platforms, CT Scanners, and PACS stations or cloud in the future.
This software will assist the user by providing initial 3D segmentation, vessel analysis, visualization, and quantitative analysis of liver anatomy. The user has the ability to adjust the contour and confirm the final segmentation.
In the proposed device, two new algorithms utilizing deep learning technology were introduced. One such algorithm segments the liver producing a liver contour editable by the user; another algorithm segments the hepatic artery based on an initial user input point. The hepatic artery segmentation is also editable by the user.

The provided text describes the 510(k) summary for Hepatic VCAR, a CT image analysis software package. The submission outlines the device's intended use and the validation performed, particularly highlighting the introduction of two new deep learning algorithms for liver and hepatic artery segmentation.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly define "acceptance criteria" through numerical thresholds for performance metrics. Instead, it states that "Verification and validation including risk mitigations have been executed with results demonstrating Hepatic VCAR met the design inputs and user needs with no unexpected results or risks."

For the new deep learning algorithms, the performance is described qualitatively:

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

• Test Set Sample Size: "A representative set of clinical sample images" was used for the clinical assessment. The exact number of cases/images is not specified in the provided text.
• Data Provenance: The provenance of the data (e.g., country of origin, retrospective or prospective) is not specified.

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

• Number of Experts: For the clinical assessment of the deep learning algorithms, 3 board certified radiologists were used.
• Qualifications of Experts: They are described as "board certified radiologists." The number of years of experience is not specified.
• For the "ground truth" used in "bench tests," the text states "ground truth annotated by qualified experts," but the number and specific qualifications of these experts are not explicitly detailed beyond "qualified experts."

4. Adjudication Method for the Test Set

The text states that the "representative set of clinical sample images was assessed by 3 board certified radiologists using 5-point Likert scale." It does not specify an explicit adjudication method (e.g., 2+1, 3+1 consensus) for establishing the "ground truth" or assessing the device's performance based on the radiologists' Likert scale ratings. The Likert scale assessment sounds more like an evaluation of clinical acceptability/usability rather than establishing ground truth for quantitative segmentation accuracy.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

A formal MRMC comparative effectiveness study comparing human readers with AI assistance vs. without AI assistance is not explicitly described in the provided text. The clinical assessment mentioned ("assessed by 3 board certified radiologists using 5-point Likert scale") appears to be an evaluation of the device's capability rather than a direct comparison of human performance with and without AI assistance. Therefore, no effect size for human reader improvement is provided.

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

Yes, standalone performance was assessed for the algorithms. The text states:

• "Bench tests that compare the output of the two new algorithms with ground truth annotated by qualified experts show that the algorithms performed as expected."
  This indicates an evaluation of the algorithm's direct output against an established ground truth before human interaction/adjustment.

7. The Type of Ground Truth Used

Based on the document:

• For the "bench tests" of the new deep learning algorithms, the ground truth was "ground truth annotated by qualified experts." This suggests expert consensus or expert annotation was used.
• For the clinical assessment by 3 radiologists using a Likert scale, it's more of a qualitative assessment of the device's capability rather than establishing a definitive ground truth for each case.

8. The Sample Size for the Training Set

The sample size for the training set (used to train the deep learning algorithms) is not specified in the provided text.

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

The text states that the deep learning algorithms were trained, but it does not explicitly describe how the ground truth for the training set was established. It only mentions that the ground truth for bench tests was "annotated by qualified experts."

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Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data derived from DICOM 3.0 compliant CT scans. Hepatic VCAR is designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver, liver lobe, liver segments and liver lesion segmentation and measurement. It is intended for use by clinicians to process, review, archive, print and distribute liver CT studies.

This software will assist the user by providing initial 3D segmentation, vessel analysis, visualization, and quantitative analysis of liver anatomy. The user has the ability to adjust the contour and confirm the final segmentation.

Hepatic VCAR is a CT image analysis software package that allows the analysis and visualization of Liver CT data derived from DICOM 3.0 compliant CT scans. Hepatic VCAR is designed for the purpose of assessing liver morphology, including liver lesion, provided the lesion has different CT appearance from surrounding liver tissue; and its change over time through automated tools for liver, liver lobe, liver segments and liver lesion segmentation and measurement.

This software will assist the user by providing initial 3D segmentation, vessel analysis, visualization, and quantitative analysis of liver anatomy. The user has the ability to adjust the contour and confirm the final segmentation.
Key functionalities of the Hepatic VCAR include:
a. Lesion segmentation
b. Liver segmentation
c. Portal vein segmentation
d. Segment Separation by Portal Vein Branches
e. Virtual Scalpel feature
Hepatic VCAR is also made available as a standalone post processing application on the AW VolumeShare 5 workstation and the AW Server image processing platforms that host advanced image processing applications.

Here's an analysis of the provided text regarding the Hepatic VCAR device, focusing on its acceptance criteria and the study proving its performance.

Note: The provided document is a 510(k) summary and FDA letter, which primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study with specific performance metrics and acceptance criteria as might be found in a full scientific publication or clinical trial report. As such, some information (especially quantitative acceptance criteria and detailed study results) is not explicitly present.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided 510(k) summary, the device's acceptance criteria are framed in terms of substantial equivalence to its predicate device, Volume Viewer Plus (K041521). The summary asserts that Hepatic VCAR is "as safe, as effective, and performance is substantially equivalent to the predicate device."

Specific, quantitative acceptance criteria for performance metrics (such as accuracy, sensitivity, or specificity for lesion detection or segmentation) are not explicitly stated in this document. The "reported device performance" is primarily qualitative, asserting equivalence to the predicate and highlighting optimized algorithms.

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

The document does not explicitly state a sample size for a test set in terms of number of cases or patients examined for clinical performance. The focus is on technical equivalence and functionality rather than a specific clinical validation study with a defined cohort.

The data provenance (e.g., country of origin, retrospective/prospective) for any internal testing is also not specified in this 510(k) summary.

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

This information is not provided in the given 510(k) summary. Since detailed clinical performance metrics for a test set are not discussed, the establishment of ground truth by experts is not described.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for a test set, as a specific clinical performance test set with expert ground truth validation is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not described or presented in this 510(k) summary. The document does not discuss human reader performance with or without AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The device is described as "assisting the user by providing initial 3D segmentation" and explicitly states "The user has the ability to adjust the contour and confirm the final segmentation." This indicates that the device is intended for human-in-the-loop use.

While "Performance testing (Bench testing, verification)" is mentioned, the summary does not detail a standalone algorithm-only performance study where the algorithm's output is evaluated without human interaction or adjustment in a clinical context. The claim of "optimized segmentation algorithms" implies some level of internal evaluation, but specifics are absent.

7. The Type of Ground Truth Used

Given the nature of the 510(k) summary and its focus on substantial equivalence based on technical aspects and functionality, a formal "ground truth" (such as pathology or long-term outcomes data) for clinical performance validation is not explicitly mentioned or described as being used in a reported study.

The closest to "ground truth" implied would be expert assessment of the "thorough testing of these capabilities" which "has not raised any safety or effectiveness issues," but the details of this assessment are not provided.

8. The Sample Size for the Training Set

The 510(k) summary does not disclose any information regarding the sample size used for training the algorithms within Hepatic VCAR.

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

Similarly, the document does not provide details on how ground truth was established for any training data used in the development of Hepatic VCAR's algorithms.

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