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The UNiD™ Spine Analyzer is intended for assisting healthcare professionals in viewing and measuring images as well as planning orthopedic surgeries. The device allows surgeons and service providers to perform generic, as well as spine related measurements on images, and to plan surgical procedures. The device also includes tools for measuring anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software.

The UNiD™ Spine Analyzer is a web-based application developed to perform preoperative and postoperative patient image measurements and simulate preoperative planning steps for spine surgery. It aims to make measurements on a patient image, simulate a surgical strategy, draw patient-specific rods or choose from a pre-selection of standard implants. The UNiD™ Spine Analyzer allows the user to:

1. Measure radiological images using generic tools and "specialty" tools
2. Plan and simulate aspects of surgical procedures
3. Estimate the compensatory effects of the simulated surgical procedure on the patient's spine

The planning of surgical procedures is done by Medtronic as part of the service of pre-operative planning. The surgical plan may then be used to assist in designing patient-specific implants. Surgeons will have to validate the surgical plan before Medtronic manufactures any implant.

The UNiD™ Spine Analyzer interface is accessible in either standalone mode or connected mode.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) clearance letter for the UNiD™ Spine Analyzer:

Overview of Device and Study Focus:

The UNiD™ Spine Analyzer is a web-based application designed to assist healthcare professionals in viewing, measuring, and planning orthopedic spine surgeries. This 510(k) submission primarily focuses on the update to the AI-enabled degenerative predictive model (Degenerative Predictive model). The study aims to demonstrate that this new version is non-inferior to the previous version (predicate device).

1. Table of Acceptance Criteria and Reported Device Performance

The core of the performance evaluation for this AI-enabled software function is focused on demonstrating non-inferiority of the updated "Degenerative Predictive model" to the predicate version.

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

• Test Set Sample Size: 274 patient surgery cases.
• Data Provenance:
  • Country of Origin: US only.
  • Retrospective/Prospective: The document states "Preoperative and post operative images from 1050 patient surgery cases were collected." This implies existing data, making it a retrospective collection.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Number of Experts: Not explicitly stated as "experts." Instead, the document mentions "highly trained Medtronic measurement technicians."
• Qualifications of Experts: "Highly trained Medtronic measurement technicians, operating within a quality-controlled environment." The specific professional background (e.g., radiologist, orthopedist) or years of experience are not provided. They were responsible for vetting image viability and performing measurements.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (like 2+1 or 3+1 for consensus). It states that "After the images were collected, they were then provided to and measured by highly trained Medtronic measurement technicians, operating within a quality-controlled environment." This suggests a single evaluation per case by these technicians, which then forms the basis for the ground truth. There's no mention of multiple technicians independently measuring and then adjudicating discrepancies.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• No, a formal MRMC comparative effectiveness study involving human readers assisting with AI vs. without AI assistance was not mentioned or described in this document. The study specifically focused on the AI model's performance (algorithm only) compared to its previous version, not the impact on human reader performance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a standalone (algorithm only) performance study was done. The entire "AI-enabled device software functions (AI-DSF)" section describes the evaluation of the new Degenerative Predictive model's output against the ground truth, comparing its performance (MAEs) directly to the predicate AI model. This evaluates the algorithm itself.

7. The Type of Ground Truth Used

• Derived from Measured Images by Technicians: "Ground truth was derived from the measured images." These measurements were performed by the "highly trained Medtronic measurement technicians." This is a form of expert consensus/review, albeit by technicians rather than clinicians, and described as measurements on images. It is not pathology or outcomes data.

8. The Sample Size for the Training Set

• Training Set Sample Size: 776 patient surgery cases.

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

• The document implies the ground truth for the training set was established in the same manner as the test set: through measurements performed by "highly trained Medtronic measurement technicians." The statement "Ground truth was derived from the measured images" applies to the overall data collection process before splitting into training and testing sets.

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IB3D™ PL Spinal System is indicated for use in lumbar spinal fusion procedures for patients diagnosed with Degenerative Disc Disease (DDD) at 1 or 2 contiguous levels from L2 to S1. DDD is defined as discogenic back pain with degeneration of the disc confirmed by history and radiographic studies. DDD patients may also have up to Grade 1 spondylolisthesis or retrolisthesis at the involved levels. When used for these indications, the IB3D™ PL Spinal System is intended for use with supplemental fixation systems cleared for use in the lumbar spine.

Additionally, the IB3D™ PL Spinal System can be used to provide anterior column support in patients diagnosed with degenerative scoliosis as an adjunct to pedicle screw fixation.

All patients should be skeletally mature and have had at least 6 months of nonoperative treatment. The IB3D™ PL Spinal System is intended to be used with autogenous bone and/or allograft bone graft comprised of cancellous and/or corticocancellous bone graft, and/or demineralized allograft bone marrow aspirate when the subject device is used as an adjunct to fusion. These implants may be implanted via an open or a minimally invasive posterior or transforaminal approach. When implanting via posterior approach (PLIF), a minimum of two implants is required per spinal level.

The IB3D™ PL Spinal System implants are inter-somatic spacers manufactured by additive manufacturing (Direct Laser Metal Sintering) from Titanium alloy Ti-6Al-4V ELI powder, according to ASTM F3001 and ASTM F136.

The IB3D™ PL Spinal System implants are intended for insertion between two adjacent vertebrae by a posterior or a transforaminal approach on the lumbar spine only.

The subject IB3D™ PL Spinal System interbody devices are available in a variety of heights and lordosis angles for treatment of lumbar interbody fusion procedure. The implant is designed with a large hollow region in the center to house autograft or allograft bone comprised of cancellous and/or corticocancellous bone and/or demineralized allograft bone marrow aspirate. The design incorporates hexalock macro-rough surface on the superior and inferior surfaces of the device along with angular teeth to prevent expulsion from the interbody space.

This is a medical device submission, not an AI/ML device submission. Therefore, it does not contain information about acceptance criteria, test sets, ground truth, or training sets typical for AI/ML performance evaluation.

The provided document describes the IB3D™ PL Spinal System, an intervertebral body fusion device. The acceptance criteria and supporting studies for this type of device focus on mechanical performance, biocompatibility, and manufacturing quality, not diagnostic accuracy or AI algorithm performance.

Here's a breakdown of the relevant information from the document:

1. A table of acceptance criteria and the reported device performance:

The document mentions several non-clinical tests performed to support substantial equivalence. These tests serve as the basis for demonstrating the device meets certain performance criteria. However, explicit "acceptance criteria" presented in a table format with corresponding "reported device performance" values are not detailed in this summary.

Non-clinical tests performed in support of substantial equivalence:

The summary states that these tests were performed on "worst-case constructs" of the IB3D™ PL Spinal System. The implication is that the device met the performance requirements of these standards, demonstrating substantial equivalence to its predicates.

2. Sample size used for the test set and the data provenance:

For mechanical and material tests of this nature, "sample size" typically refers to the number of test articles (implants) subjected to testing. This information is not specified in the provided 510(k) summary. The document does not describe patient data (e.g., country of origin, retrospective/prospective) because no clinical testing was used to support the submission.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

This information is not applicable as the submission is for a medical device (intervertebral body fusion device) and does not involve diagnostic interpretation or AI algorithm evaluation requiring human experts to establish ground truth from medical images or clinical data.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

This information is not applicable for the same reasons as point 3.

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:

This information is not applicable as this is not an AI-assisted device. The submission explicitly states: "No clinical testing was used in order to support this submission."

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

This information is not applicable as this is not an AI algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

For the non-clinical tests, the "ground truth" is defined by the specified test standards (ASTM, ISO). Meeting the criteria outlined in these standards for mechanical strength, fatigue, wear, and biocompatibility constitutes the "ground truth" for proving the device's performance characteristics and safety.

8. The sample size for the training set:

This information is not applicable as this is a medical device, not an AI/ML product that requires a training set.

9. How the ground truth for the training set was established:

This information is not applicable as this is not an AI/ML product.

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