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Spine Auto Views is a non-invasive image analysis software package which may be used in conjunction with CT images to aid in the automatic generation of anatomically focused multi-planar reformats and automatically export results to predetermined DICOM destinations.

Spine Auto Views assists clinicians by providing anatomically focused reformats of the spine, with the ability to apply anatomical labels of the vertebral bodies and intervertebral disc spaces.

Spine Auto Views may be used for multiple care areas and is not specific to any disease state. It can be utilized for the review of various types of exams including trauma, oncology, and routine body.

Spine Auto Views is a software analysis package designed to generate batch reformats and apply labels to the spine. It is intended to streamline the process of generating clinically relevant batch reformat outputs that are requested for many CT exam types.

Spine Auto Views can generate, automatically, patient specific, anatomically focused spine reformats. Spine Auto Views brings a state-of-the-art deep learning algorithm that generates oblique axial reformats, appropriately angled through each disc space without the need for a user interface and human interaction. 3D curved coronal and curved sagittal views of the spine as well as traditional reformat planes can all be generated with Spine Auto Views, no manual interaction required. Vertebral bodies and disc spaces can be labeled, and all series networked to desired DICOM destination(s), ready to read. The automated reformats may help in providing a consistent output of anatomically orientated images, labeled, and presented to the interpreting physician ready to read.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance Study for Spine Auto Views

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: A database of retrospective CT exams for algorithm validation and a sample of clinical CT images for the reader study. The exact number of exams/images for each is not specified in the document.
• Data Provenance: The document states that the database of exams for algorithm validation was "representative of the clinical scenarios where Spine Auto Views is intended to be used, with consideration of acquisition parameters and patient characteristics." No specific country of origin is mentioned, but the mention of "retrospective CT exams" confirms the nature of the data.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts or their specific qualifications (e.g., "radiologist with 10 years of experience") used to establish the ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1). For the reader study, it indicates that readers assessed the reformats, but it doesn't detail how discrepancies or consensus were handled if multiple readers were involved in rating the same case.

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

• Was an MRMC study done? A reader study was performed to assess user acceptance, which is a form of MRMC study in that multiple readers evaluated cases. However, it was not explicitly a "comparative effectiveness study" with and without AI assistance as described in the prompt.
• Effect Size (AI vs. No AI): The study focused on the acceptance of the AI-generated reformats, implying a comparison against traditional manual generation (which would be "without AI assistance" for reformats). The text states that "Spine Auto Views oblique axial reformats generates user acceptable results greater than 95% of the time for all readers." This indicates a high level of acceptance for the AI-generated images, suggesting a positive effect compared to the traditional manual process, which the AI aims to streamline and automate. However, a quantitative effect size in terms of clinical improvement or efficiency gain compared to a "without AI" baseline is not provided. The device's primary benefit is automation ("no manual interaction required"), implying an improvement in workflow efficiency and consistency over manual methods.

6. Standalone (Algorithm Only) Performance

• Was standalone performance done? Yes, an engineering validation of the Spine Auto Views algorithm's capability to automatically detect the position and orientation of intervertebral discs was performed as a standalone assessment. This evaluated the algorithm's performance independent of human interaction or interpretation in a clinical setting.

7. Type of Ground Truth Used

• Algorithm Validation: The ground truth for the algorithm validation (disc detection) seems to have been established through a reference standard derived from the "database of retrospective CT exams." While the exact method (e.g., manual expert annotation, pathology correlation) is not explicitly stated, it implies a reliable, established truth against which the algorithm's detections were compared.
• Reader Study: For the reader study, the "user acceptable results" and comparison with "corresponding standard coronal and standard sagittal views" suggest a ground truth based on expert consensus/clinical acceptability by the evaluating readers.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set used for the deep learning algorithm. It only mentions a "database of retrospective CT exams" for validation.

9. How Ground Truth for Training Set Was Established

The document does not provide details on how the ground truth for the training set was established. It only refers to the validation set and its ground truth.

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