(256 days)
PRAEVAorta®2 is a software intended to be run on its own or as part of another medical device to automatically calculate maximum diameters of anatomical zones from a DICOM CT image containing blood vessels.
PRAEVAorta®2 is designed to measure the maximal transverse diameter of vessels and determine the maximal general diameter using a non-adaptative machine learning algorithm.
Intended users of the software are aimed to the clinical specialists, physicians or other licensed practitioners in healthcare institutions, such as clinics, hospitals, healthcare facilities, residential care facilities and long-term care services. Any results obtained from the software by an intended user other than a physician must be validated by the physician responsible of the patient.
The system is suitable for adults. Its results are not intended to be used on a standalone basis for clinical decision making or otherwise preclude clinical assessment of any disease.
PRAEVAorta®2 is a decision-making support software for diagnosis and follow-up of vascular diseases. It is intended for automatic segmentation and geometric analysis of vessels.
It is a companion software whose purpose is to accompany the doctor in the first assessment of several indicators from CT scan images
The software is able to reconstruct automatically the vascular structures from CT (Computerized Tomography) scans images and automatically segments aneurysms and associated thrombus.
With this reconstruction, the software is able to provide diameters, volumes, and angles. In addition, the software provides diameters, volumes, angles and distances between anatomic points.
This software is cloud based or can be installed on premises. PRAEVAorta®2 is a server software usable through APIs. However, it is hardly recommended to use it via a client software. The client aims to provide a user interface to send images and receive the analysis results. It can either be a web client, a getaway / PACS client, an integrating solution, or a marketplace
Based on the provided FDA 510(k) clearance letter for PRAEVAorta®2 (K243859), here's a description of the acceptance criteria and the study that proves the device meets those criteria:
Acceptance Criteria and Device Performance Study for PRAEVAorta®2
PRAEVAorta®2 is a software intended to automatically calculate maximum diameters of anatomical zones from DICOM CT images containing blood vessels, specifically focusing on the aorta and iliac arteries. The device utilizes a non-adaptive machine learning algorithm to measure maximal transverse diameters of vessels and determine the maximal general diameter.
1. Table of Acceptance Criteria and Reported Device Performance
The primary performance validation criterion for PRAEVAorta®2 was based on the measurement accuracy of the total maximum orthogonal aorta diameter compared to a ground truth established by expert manual measurements.
| Variable | Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Total Maximum Orthogonal Aorta Diameter | ||
| Mean Absolute Error (MAE) | Must be less than or equal to 5 mm | 2.04 mm (95% CI: [1.75 mm; 2.34 mm]) |
| Percentage of values within ≤ 5 mm limit | Must be in at least 96% of cases | 96.9% of values (within a ≤ 5 mm limit) |
| Pearson Correlation Coefficient | Must be at least greater than 0.90 (defined as a very strong correlation) | 0.97 |
| Bias (Mean Difference) | (Implied acceptance: close to zero, within reasonable limits) | -0.75 mm (95% CI: [-1.17 mm; -0.33 mm]) |
| Percentage of values within 95% Limit of Agreement (Bland-Altman) | (Implied acceptance: high percentage) | 96.9% (within the 95% limit of agreement, ranged from –6.01 mm to +4.51 mm) |
Conclusion: The device successfully met all defined acceptance criteria based on its reported performance.
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 159 unique cases (patients).
- Data Provenance: The dataset included both contrast-enhanced and non-contrast-enhanced CT scans from:
- United States (81 CT scans)
- France (40 CT Scans)
- Canada (38 CT Scans)
- Retrospective/Prospective: The document does not explicitly state whether the data was retrospective or prospective, but the description "The selected CT scans were not used for AI training" and "Information collected on the dataset included patient demographics... imaging characteristics... and clinical management details" suggests these were pre-existing, retrospectively collected CT scans.
- The dataset included images from numerous scanner manufacturers (e.g., GE Medical System, Siemens, Philips, Toshiba) and comprised 95 preoperative and 64 postoperative CT scans (62 with an aortic stent graft). The patients were aged over 18 years, including 130 males, 28 females, and one patient of unknown sex.
3. Number of Experts Used to Establish Ground Truth and Qualifications
- Number of Experts: Four (4)
- Qualifications of Experts: All experts were vascular surgeons with at least five years of clinical experience in vascular diseases following board certification. They had no financial conflicts of interest and received adequate training from NUREA.
4. Adjudication Method for the Test Set
The ground truth was established by manual measurements performed by the four vascular surgeons. The document states, "The measurements performed by these professionals showed no discrepancy greater than 5 mm at the end of the collected data process." This suggests that a form of consensus or high agreement among experts was achieved, though a specific adjudication method (e.g., 2+1 tie-breaker, majority rule) for resolving discrepancies if they exceeded 5mm is not explicitly detailed. The statement implies that no significant discrepancies requiring formal adjudication arose.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was explicitly described in the provided text. The study focused on the standalone performance of the PRAEVAorta®2 algorithm against expert-established ground truth manual measurements, rather than comparing human reader performance with and without AI assistance.
6. Standalone (Algorithm Only) Performance Study
Yes, a standalone performance study was conducted. The "Performance assessment" section details a technical performance assessment of PRAEVAorta®2 to validate its accuracy against measurements provided by the Ground Truth using manual measurement tools. This means the algorithm's measurements were directly compared to expert manual measurements to determine its accuracy and reliability.
7. Type of Ground Truth Used
The ground truth used was expert consensus (or high agreement) based on manual measurements performed by a panel of four qualified vascular surgeons. This is explicitly stated as the "reference standard" and "ground truth."
8. Sample Size for the Training Set
The document explicitly states, "The selected CT scans were not used for AI training." However, the exact sample size for the training set is not provided in this document. The focus of this section is on the validation/test dataset used for performance assessment.
9. How the Ground Truth for the Training Set Was Established
The document states that the testing dataset was explicitly not used for AI training, but it does not describe how the ground truth for the training set was established. This information would typically be detailed in the development methodology rather than the performance validation section of a submission summary.
§ 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).