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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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The NuVasive® NuvaLine™ Mobile App is a medical device software mobile application intended to assist healthcare professionals in capturing, measuring, and storing spinal alignment assessment images at various time points in patient care. The device allows the healthcare professional to conveniently perform and review spinal alignment assessments of images by featuring measurement tools on their mobile device.

The NuVasive NuvaLine Mobile App is a medical device software used to measure spinal pelvic and cervical parameters from an image of patient's x-rays taken with the device's camera. 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 NuVasive® NuvaLine™ Mobile App is a medical device software mobile application intended to assist healthcare professionals in capturing, measuring, and storing spinal alignment assessment images. The device allows healthcare professionals to perform and review spinal alignment assessments of images using measurement tools on their mobile device.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set (number of images or cases). It mentions that "Verification testing was performed to confirm that NuvaLine accurately measures and offsets based off of x-ray images."

The data provenance (e.g., country of origin, retrospective or prospective) is not specified.

3. Number of Experts and their Qualifications for Ground Truth

The document does not mention the number of experts used to establish the ground truth for the test set, nor does it specify their qualifications.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance is mentioned. The study performed focuses on the device's accuracy against defined criteria, not against human performance or improvement with AI assistance.

6. Standalone Performance

Yes, a standalone performance study was done. The "Verification testing" described focused solely on the accuracy and functionality of the NuvaLine Mobile App without human-in-the-loop performance. Its ability to accurately measure angles and offsets, display values, and provide alerts was tested directly.

7. Type of Ground Truth Used

The type of ground truth used appears to be based on pre-defined "Software Requirements Specifications." The document states, "Verification testing was performed to confirm that NuvaLine accurately measures and offsets based off of x-ray images, and that these values are displayed properly with correct color-coded alert indications. Angle measurements were verified within ± 3° while offset measurement were verified within ± 2 cm accuracy." This implies that the ground truth for measurements was established against these specified accuracy thresholds, likely using known or precisely measured values on test images, rather than expert consensus, pathology, or outcomes data from actual patients.

8. Sample Size for the Training Set

The document does not specify the sample size used for the training set.

9. How the Ground Truth for the Training Set was Established

The document does not provide information on how the ground truth for the training set was established. The focus of the provided text is on nonclinical verification and validation testing of the pre-developed software.

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