(114 days)
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:
| Feature/Algorithm | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Liver Segmentation | Produces a liver contour that is editable by the user and is capable of segmentation. | Bench tests show algorithms performed as expected. |
| Demonstrated capability of liver segmentation utilizing the deep learning algorithm. | ||
| Hepatic Artery Segmentation | Segments the hepatic artery based on initial user input, editable by the user, and capable of segmentation. | Bench tests show algorithms performed as expected. |
| Demonstrated capability of hepatic artery segmentation utilizing the deep learning algorithm. | ||
| Overall Software Performance | Meets design inputs and user needs, no unexpected results or risks. | Verification and validation met design inputs and user needs with no unexpected results or risks. |
| Usability/Clinical Acceptance | Functionality is clinically acceptable for assisting users in 3D segmentation, visualization, and quantitative analysis. | Assessed by 3 board-certified radiologists using a 5-point Likert scale, demonstrating capability. |
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."
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.