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The aprevo® TLIF-C Articulating System is interbody fusion in skeletally mature patients and is to be used with supplemental fixation instrumentation cleared for use in the lumbar spine. The aprevo® TLIF-C Articulating System is indicated for use as an adjunct to fusion at one or more levels of the lumbar spine in patients having an ODI >40 and diagnosed with severe symptomatic adult spinal deformity (ASD) conditions. These patients should have had six months of non-operative treatment. The device is intended to be used with autograft and/or allogenic bone graft comprised of cancellous and/or cortico-cancellous bone graft. These implants may be implanted via a variety of open or minimally invasive approaches.

The aprevo® TLIF-C Articulating System is indicated for use at one or more levels of the lumbosacral spine as an adjunct to fusion in patients with the following indications: degenerative disc disease (DD), disc herniation (with myelopathy and/or radiculopathy), spondylolisthesis, deformity (degenerative scoliosis or kyphosis), spinal stenosis, and failed previous fusion (pseudarthrosis). DDD is defined as discogenic back pain with degeneration of the disc as confirmed by history and radiographic studies. These patients should be skeletally mature and have had at least six (6) months of non-operative treatment. aprevo® TLIF-C Articulating System devices are to be filled with autograft bone and/or allogenic bone graft composed of cancellous and/or corticocancellous bone. These devices are intended to be used with supplemental fixation systems that have been cleared for use in the thoracolumbosacral spine (e.g., posterior pedicle screw and rod systems). These implants may be implanted via a variety of open or minimally invasive approaches.

The aprevo® TLIF-C Articulating System devices are designed to stabilize the lumbar spinal column and promote fusion. The personalized aprevo® interbodies incorporate patient specific features to allow the surgeon to tailor the deformity correction to the individual needs of the patient and include an aperture intended for the packing of bone graft.

The aprevo® TLIF-C Articulating interbodies are additively manufactured from Ti-6Al-4V ELI titanium alloy per ASTM F3001. The devices are accompanied by an inserter instrument which facilitates the placement of the interbodies. Both the interbody devices and instrument are provided as single use, sterile-packed product to the end user.

The provided text describes a medical device, the aprevo® TLIF-C Articulating System, and its substantial equivalence to predicate devices, supported by non-clinical testing. However, it does not contain the specific information required to answer the questions about acceptance criteria for a study demonstrating device performance for an AI/ML-enabled medical device. The information provided is primarily focused on the mechanical and physical performance of the interbody fusion device itself, not on the performance of any AI/ML component.

Therefore, I cannot provide a detailed answer to your request regarding acceptance criteria and study details for an AI/ML device based on the given input.

Here's a breakdown of why and what kind of information would be needed:

• No AI/ML Component Described: The text describes a physical interbody fusion device and its associated instruments. There is no mention of any AI or machine learning algorithms, software, or intelligent functions associated with the device. The "technological characteristics" mentioned refer to its mechanical properties and materials, not software performance.
• Non-Clinical Testing is for Physical Device: The "non-clinical testing" listed (Static and dynamic axial compression, Static and dynamic compression-shear, Subsidence, Cadaveric study) are standard tests for orthopedic implants to ensure their mechanical integrity and biocompatibility. These tests do not evaluate AI/ML performance.

To answer your questions, the input would need to describe an AI/ML-enabled medical device and its corresponding performance study, which would typically include metrics like sensitivity, specificity, accuracy, AUC, etc., along with details on the ground truth establishment, expert review, and dataset characteristics.

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aprevo® Digital Segmentation software is intended to be used by trained, medically knowledgeable design personnel to perform digital image segmentation of the spine, primarily lumbar anatomy. The device inputs DICOM images and outputs a 3-D model of the spine.

The device is a software medical device that will use DICOM images as input and provide 3D model of the spine structure. Pre-processing will be performed on the uploaded DICOM files to filter soft tissue and identifying spine. Upon removal of soft tissue and identification of spine structure, the software will utilize an AI-based algorithm to segment the structure and render a 3D model as an output.

The provided text describes the acceptance criteria and the study that proves the device meets those criteria for the "aprevo® Digital Segmentation" software.

Here's a breakdown of the requested information:

Acceptance Criteria and Device Performance

Study Details:

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Sample Size: Not explicitly stated in the provided text, but it mentions that "Independent training and validation datasets were selected to ensure model performance would reflect real clinical performance" and "Validation datasets represented diversity in populations and equipment."
   • Data Provenance: Not explicitly stated, however, the phrase "diversity in populations and equipment" suggests data from various sources but does not specify countries of origin. The study was a "Non-Clinical Testing" which implies retrospective data.

2. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:

   • Not specified. The document states that the ground truth for the algorithm was used for model performance evaluation, but does not detail how this ground truth was established, or the number/qualifications of experts involved.

3. Adjudication Method for the Test Set:

   • Not specified.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • If done: No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or indicated. The document states "CLINICAL TESTING: Not applicable." The study solely focuses on the standalone performance of the software.
   • Effect size of human readers improvement: Not applicable, as no MRMC study was conducted.

5. Standalone Performance (Algorithm only without human-in-the-loop):

   • If done: Yes, a standalone performance evaluation was done. The "NON-CLINICAL TESTING" section describes the evaluation of the "software performance" using IOU and accuracy metrics for segmentation and labeling, without human intervention in the reported performance metrics.

6. Type of Ground Truth Used:

   • The type of ground truth used is not explicitly stated as expert consensus, pathology, or outcomes data. However, for "segmentation" and "vertebral body labeling," the ground truth would typically be established by expert annotation or a similar gold standard, refined through a consensus process, but this is not detailed.

7. Sample Size for the Training Set:

   • Not explicitly stated. It only mentions that "Independent training and validation datasets were selected to ensure model performance would reflect real clinical performance."

8. How the Ground Truth for the Training Set Was Established:

   • Not explicitly stated. The document mentions that "Independent training and validation datasets were selected," but does not elaborate on the method used to establish the ground truth for the training data (e.g., expert annotations, manual segmentation).

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