(21 days)
Nu Vasive NuvaLine is a medical device software application intended to assist healthcare professionals in capturing, viewing, measuring, and storage and distribution of spinal alignment assessment images at various time points in patient care. Online synchronization of the database allows healthcare professionals and service providers to conveniently perform and review spinal alignment assessments of images by featuring measurement tools on various platforms. Clinical judgment and experience are required to properly use the software.
NuVasive NuvaLine is a medical device software application used to calculate the spinal pelvic, lumbar, thoracic, and cervical parameters for pre-operative and post-operative assessment of spinal x-ray images. These measured parameters provide a quantifiable way to assess a patient's spinal deformity and correction correlated to health related quality of life (HRQOL) scores.
The purpose of this premarket notification is to gain clearance of the previously cleared NuvaLine app to communicate with cloud server for online synchronization of database to transfer and store assessment data to allow for use of the NuvaLine app on different platforms (e.g.: mobile, web interface, desktop) by healthcare professionals and service providers.
The provided text does not contain explicit acceptance criteria and corresponding performance data in a dedicated table format. However, it does mention performance characteristics in the comparison table and describes the testing performed. I will extract the relevant information and present it in the requested format, inferring acceptance criteria where it implies a match to the predicate device's performance.
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Implied) | Reported Device Performance (NuVaLine® NuvaLine®) |
|---|---|
| Spinal alignment assessments of images (Matching predicate functionality) | Spinal alignment assessments of images |
| Various spinal assessment algorithms (Matching predicate functionality) | Various spinal assessment algorithms |
| User Interface: PC or mobile device or web interface (Matching reference devices) | PC or mobile device or web interface |
| Obtaining an image: Transferred from PACS (Matching reference device functionality) | Transferred from PACS (DICOM images from PACS converted to jpeg for use in NuvaLine) |
| Online synchronization of database (Matching reference device functionality) | Yes |
| PACS connectivity (Matching reference device functionality) | Yes |
| DICOM compatibility (Matching reference device functionality) | Yes (DICOM images from PACS converted to jpeg for use in NuvaLine) |
| Supported Platforms: Mobile application on iOS 10.0+; Web client on Windows 10, 3GHz processor, 18GB RAM, modern browser, 1920x1200 display resolution (Matching predicate/reference devices and added web client support) | Mobile application supported on devices running iOS version 10.0 or later. |
| Web client is supported for the following minimum system specifications: Windows 10, 3GHz processor, 18GB RAM, Modern browser supporting HTML5.2 and JavaScript ES7 or better, 1920x1200 display resolution | |
| Measurement accuracy: Angles within ± 3°, offsets within ± 1 cm (Improved from predicate's ± 2 cm) | NuvaLine measures angles within ± 3° and offsets within ± 1 cm accuracy. |
| Cloud Connectivity Validation | NuvaLine Cloud Connectivity Validation performed and met |
| Web Client Cloud Connectivity Validation | NuvaLine Web Client Cloud Connectivity Validation performed and met |
| Cloud Connectivity Measurement Library Verification | NuvaLine Cloud Connectivity Measurement Library Verification performed and met |
2. Sample size used for the test set and the data provenance
The document mentions "Nonclinical testing was performed..." and lists types of validation tests. However, it does not specify the sample size for the test set used in these validations (e.g., number of images, number of measurements). It also does not specify the data provenance (e.g., country of origin, retrospective or prospective nature of the data).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not provide information regarding the number of experts, their qualifications, or their involvement in establishing ground truth for any test set.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
The document does not specify any adjudication method for a test set.
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
The document does not mention or describe a multi-reader multi-case (MRMC) comparative effectiveness study. It focuses on the device's standalone performance and its equivalence to predicate devices, not on human reader improvement with AI assistance.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Yes, the document implies that a standalone performance evaluation was conducted. The "Measurement accuracy" specification confirms the device's ability to measure angles and offsets with specific accuracy limits (angles within ± 3° and offsets within ± 1 cm). This indicates an evaluation of the algorithm's performance independent of human-in-the-loop assistance for measurement, as it's a characteristic directly attributed to NuvaLine.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The document does not explicitly state the type of ground truth used for the measurement accuracy evaluation or other validation tests. Given the nature of a "Picture archiving and communications system" and "spinal alignment assessments," it is highly probable that the ground truth for measurement accuracy would have been established through highly precise manual measurements by qualified experts on a reference standard or through established anatomical landmarks on images, but this is not explicitly stated.
8. The sample size for the training set
The document does not provide information regarding the sample size of a training set. This is consistent with the subject device being described as a "medical device software application" that provides measurement tools, rather than a machine learning or AI algorithm that requires a distinct training phase.
9. How the ground truth for the training set was established
Since no training set is mentioned or implied for a machine learning or AI component, the document does not provide information on how ground truth for a training set was established.
§ 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).