(17 days)
Kuvia3D software is intended for the display and analysis of DICOM image data to facilitate 3D visualization of joint anatomy for planning surgical and non-surgical therapies.
Kuvia3D is a software system used to display, analyze and generate three-dimensional visualizations of DICOM image data. The software is supported on an off-the-shelf personal computer system platform running the Microsoft Windows operating system. The user will transfer medical image data to the Kuvia3D system from a DICOM image system (such as a PACS) and proceed to use Kuvia3D's viewing and segmentation tools to segment the image data as desired. Once the user has completed segmenting the image data, they can convert the segmented region to a 3D rendered surface and adjust the view of the segmented anatomy as desired. Linear measurements may be taken in the 2D image as well as the 3D surface rendering. Screen captures of the 3D surface rendering window mav be saved as derivative images and appended to the imaging study, or transferred to other DICOM image systems.
Based on the provided text, the Kuvia3D device is a software system for displaying and analyzing DICOM image data to facilitate 3D visualization of joint anatomy. The document is a 510(k) premarket notification for substantial equivalence, not a detailed study report with specific performance metrics against pre-defined acceptance criteria. Therefore, the information typically found in a clinical study report (e.g., specific sensitivity/specificity, reader study details) is not present.
However, I can extract the information related to the approach to acceptance criteria and the type of testing conducted to demonstrate substantial equivalence, based on the provided text.
Here's a breakdown of the available information:
1. A table of acceptance criteria and the reported device performance
The document frames "acceptance criteria" in terms of compliance with standards and demonstration of substantial equivalence to predicate devices, rather than specific quantitative performance metrics. While specific performance numbers are not given, the device "meets" its acceptance criteria by demonstrating this equivalence and compliance.
| Acceptance Criteria Focus | Reported Device Performance |
|---|---|
| Compliance with voluntary DICOM and JPEG standards for device performance. | Kuvia3D complies with the voluntary DICOM and JPEG standards for device performance. |
| Design and manufacturing according to engineering quality processes and standards. | Kuvia3D is designed and manufactured according to engineering quality processes and standards as detailed in the 510(k) submission. |
| Validation that Kuvia3D conforms to defined product, user requirements, and intended uses. | Verification activities were conducted on system, unit, and software component levels to validate that Kuvia3D conforms to defined product and user requirements and intended uses. Predefined acceptance criteria were met. |
| Demonstration of safety and effectiveness as the predicate devices. | Nonclinical software testing was conducted under simulated use conditions. Predefined acceptance criteria were met and demonstrate that the device is as safe and effective as the predicate devices. Kuvia3D is substantially equivalent to the predicate device in terms of intended use, specific features/functionality, and design. The color-coded display of anatomical feature size, similar to a reference device, does not raise any new questions of safety or effectiveness. The 510(k) Pre-Market Notification contains adequate information and data to determine that Kuvia3D is as safe and effective as the legally marketed predicate device. |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
- Test Set Sample Size: Not specified. The document mentions "nonclinical software testing" and "simulated use conditions" but does not provide a number of cases or datasets used.
- Data Provenance: Not specified. The text focuses on the software itself and its compliance, not on a dataset of patient images. It refers to "DICOM image data" which is a generic format.
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)
Not applicable. The document describes software verification and validation, not a study involving human reader performance comparisons or ground truth established by experts on a specific image set. It states: "A physician, providing ample opportunity for competent human intervention, reviews images, generates/corrects segmentations as necessary and reviews information generated by Kuvia3D." This refers to the clinical use case, not the V&V testing.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. No expert adjudication method is described for generating ground truth for a test set, as this type of study was not performed or detailed in the document.
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
No MRMC study was done or reported. The document describes a "stand-alone software package" and its substantial equivalence to predicate devices based on its features and compliance with standards, not its comparative effectiveness with human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, the testing described appears to be primarily standalone software testing: "Nonclinical software testing was conducted under simulated use conditions." This implies testing the algorithm's functionality and compliance as a standalone system. The document states, "Kuvia3D is a stand-alone software package."
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The document does not explicitly describe "ground truth" in the context of a diagnostic performance study. The testing focused on validating that the software conforms to defined requirements and standards. For features like segmentation accuracy or measurement accuracy, the "ground truth" would likely be based on:
- Engineering specifications.
- Comparison against known correct outputs for simulated data or previously validated methods.
- "The basic information on which the coding is based does not change. Digital data displayed and thicknesses rendered via color coding do not raise new questions of safety and effectiveness since the underlying algorithms and libraries used to generate the segmentations are the same for each." This rather vaguely suggests that the validity of the underlying data and algorithms for segmentation is assumed.
8. The sample size for the training set
Not applicable. This is a 510(k) for a software system, not a machine learning algorithm that requires a training set in the conventional sense. The "development" and "training" for such software refer to its engineering and coding according to specified requirements and standards.
9. How the ground truth for the training set was established
Not applicable, as there is no mention of a training set for a machine learning model.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).