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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

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.

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