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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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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 MEDICREA UNiD Spine Analyzer was developed to perform preoperative and postoperative patient image measurements and simulate preoperative planning steps for spine surgery. This web-based, Software as a Medical Device (SaMD) application aims to simulate a surgical strategy, make measurements on a patient image, and draw patient-specific rods or choose from a pre-selection of standard implants and ordering the patient-specific rods. 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

The purpose of this submission is to request clearance for the UNiD Spine Analyzer v4.0. The changes introduced are as follows:

• . Addition of the Degenerative Predictive Model, which corresponds to a type of adult spinal fusion degenerative construct, trained with a retrospective longitudinal patient dataset.
• . Update to the existing Adult Predictive Model consisting of three predictive model modules trained with retrospective longitudinal patient datasets, where one was included in Adult Deformity Model 1 (TKA-12) and two included in Adult Deformity Model 2 (PTA-12 and PTA-34).
• Update to the existing Pediatric Predictive Model consisting of two predictive model modules trained with retrospective longitudinal patient datasets (PediaLL and PediaPT),
• Addition of the display of a Predicted Value derived from a static machine-learning based model . when the user views simulated quantitative radiographic parameters of a planned surgery, generated when the Degenerative, Adult or Pediatric Predictive Models are used.
• . The subject device update also includes the addition of implant templates among a preselected database of Medtronic standard implants cleared in in the following 510(k)s: K073291, K083026, K091813, K110543, K113528, K120368, K150135, K152277, K172199, K172328, and K201267.

The provided text describes the UNiD™ Spine Analyzer, a medical image management and processing system, and its submission for FDA 510(k) clearance. Here's information extracted regarding acceptance criteria and the study that proves the device meets them:

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

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics in a quantitative manner for the "Predictive Models" (Degenerative Predictive Model, Adult Predictive Model, Pediatric Predictive Model). Instead, it states that these additions are "similar to the display of reference and normative data, and does not raise new questions of safety and effectiveness when considered with existing methods of managing spinal compensation."

For the software as a whole, the acceptance criteria are described indirectly through the validation activities:

• Comprehension of the Health Care professional with the UNiD Spine Analyzer,
• Appropriate human factors related to the UNiD Spine Analyzer, and
• Ease of use of the UNiD Spine Analyzer. |

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

• Predictive Models: The predictive models (Degenerative, Adult, and Pediatric) were "trained with retrospective longitudinal patient datasets." No specific sample size for these datasets or their provenance (country of origin) is provided.
• Software Validation/Verification: The document does not specify a separate "test set" sample size for the software validation activities beyond stating that "the software was tested against the established Software Design Specifications."
• Usability Study: No specific sample size (number of users) is mentioned for the usability study.

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

This information is not provided in the document. The document refers to the predictive models being "trained with retrospective longitudinal patient datasets" but does not detail how the ground truth for these training sets or any potential test sets was established, nor does it mention the involvement or qualifications of experts in this process for external validation. The clinical judgment of healthcare professionals is explicitly stated as required for proper software use, but not for ground truth establishment.

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

This information is not provided in the document.

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:

A multi-reader, multi-case (MRMC) comparative effectiveness study was not conducted or reported for this submission. The document explicitly states: "There was no human clinical testing required to support the medical device as the indications for use is identical to the predicate device." The new features (predictive models) are presented as an "additional tool" similar to "display of reference and normative data" and are not claimed to improve human reader performance with a measurable effect size.

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

The document mentions "Addition of the display of a Predicted Value derived from a static machine-learning based model" when the user views simulated quantitative radiographic parameters. This implies a standalone algorithmic prediction output. However, there are no specific performance metrics or a standalone study reported for the algorithm itself (e.g., accuracy of predictions against ground truth without human intervention).

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

For the predictive models, the ground truth for the training data was derived from "retrospective longitudinal patient datasets." The specific nature of this ground truth (e.g., direct surgical measurements, post-operative imaging, clinical outcomes) is not explicitly stated, beyond it being used to train models for "predicted spinal compensation."

8. The sample size for the training set:

The document states that the predictive models were "trained with retrospective longitudinal patient datasets" but does not specify the sample size for these training sets.

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

The document states the predictive models were trained using "retrospective longitudinal patient datasets." However, it does not detail the specific methodology for how the ground truth within these datasets was established (e.g., whether it was based on expert review of images, surgical records, or patient outcomes).

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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 purpose of this submission is to update the UNiD Spine Analyzer with the addition of a new software feature: "Data base of implants". This component will allow a user to draw implants (cages, screws and rods) taken from a range of MEDICREA INTERNATIONAL implants, previously cleared in K08009, K083810, K163595, in addition to the design of custom-made implants specific to a unique patient. A catalog of these implants is provided in this submission.

The provided text is a 510(k) summary for the UNiD Spine Analyzer. It states that the submission is to add a new software feature, "Data base of implants," to an already cleared device (UNiD Spine Analyzer, K170172). Therefore, the acceptance criteria and performance data described in this document relate to the new feature and its integration, rather than a full study of the entire device's performance from scratch.

However, the 510(k) summary does not contain specific acceptance criteria tables or detailed performance study results (like sensitivity, specificity, AUC, or other quantitative measures typically found in standalone AI/ML device studies). It primarily focuses on demonstrating substantial equivalence by comparing features and outlining the type of testing performed.

Based on the information provided, here's what can be extracted and what is NOT available:

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

• Acceptance Criteria: Not explicitly stated as quantitative metrics (e.g., "accuracy > X%"). The document implies acceptance based on successful "verification and validation activities" for the new software feature. For a medical device, this typically means:
  • The software correctly performs the functions it's designed for (e.g., implants are drawn accurately, catalog is accessible).
  • The new feature doesn't introduce new safety or effectiveness issues.
  • The software meets industry standards for medical device software development (e.g., IEC 62304).
• Reported Device Performance: No quantitative performance metrics (like accuracy, precision, etc.) are provided for the new "Database of implants" feature. The document only states that "Performance data for the modified UNiD Spine Analyzer consisted of verification and validation activities." and "The addition of the database of implants creates additional tools which were also tested, and documentation was provided."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size: Not specified. The document only mentions "verification and validation activities" for the software feature itself, not a clinical data set for performance evaluation.
• Data Provenance: Not specified. Since this is about adding a database of implants and related drawing tools, it's less about analyzing patient image data for diagnosis and more about the software's functional correctness.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of experts & Qualifications: Not applicable/not specified. The "ground truth" for this specific submission likely relates to the accuracy of implant representation and placement tools, which would be verified against design specifications, engineering standards, and potentially input from orthopedic surgeons during development, rather than a clinical ground truth established by diagnosing cases.

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

• Adjudication Method: Not applicable/not specified.

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

• MRMC Study: No, an MRMC study was not described. The device, UNiD Spine Analyzer, assists healthcare professionals in viewing, measuring, and planning orthopedic surgeries. The specific update in this submission is the addition of an implant database. This generally falls under medical image management/measurement software (PACS-like functionality) rather than an AI/ML diagnostic or prognostic tool that would typically undergo MRMC studies. The software is explicitly stated to require "Human Intervention for interpretation and manipulation of images."

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

• Standalone Study: Not explicitly described in terms of clinical performance. The "verification and validation activities" confirm the software's functionality, but these are not presented as a standalone clinical performance study typically seen for AI algorithms making diagnostic interpretations. The device is a tool for human use, not an autonomous diagnostic algorithm.

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

• Type of Ground Truth: Not specified in the provided text. For a feature involving an implant database and drawing tools, ground truth would likely be based on design specifications, physical accuracy of implant models, and functional correctness according to orthopedic surgical planning principles, rather than clinical outcomes or pathology.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This document does not describe the development of a machine learning algorithm that learns from a training set of data. It describes the addition of a database and associated software tools.

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

• Training Set Ground Truth Establishment: Not applicable.

Summary of what the document focuses on regarding device acceptance:

The document leverages the concept of "substantial equivalence" to a previously cleared version of the same device (K170172). The acceptance criteria for the new feature (database of implants) are implicit in the statement that "The addition of this new component (i.e., data base of cleared implants) to the UNiD Spine Analyzer does not raise new issues of safety or effectiveness compared to the previously cleared version of the UNiD Spine Analyzer." This implies that the testing (verification and validation) confirmed:

• The implant database functions as intended.
• The drawing tools work correctly.
• The new feature does not adversely affect the safety or performance of the existing cleared functionalities of the UNiD Spine Analyzer.
• The software development followed appropriate guidelines for medical device software ("Guidance for Industry and FDA Staff, 'Guidance for the Content of Premarket Submissions for Software Contained on Medical Devices'").

Essentially, for this 510(k) (which is an update to an existing device), the "proof" for acceptance is the demonstration that the change does not negatively impact safety or effectiveness, and the new feature itself is functionally sound, rather than a de novo clinical performance study against specific acceptance criteria.

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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 or 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 judgement and experience are required to properly use the software.

UNiD Spine Analyzer is a software solution developed for the medical community. It is intended to be used to view images and perform spine related measurements and plan surgical procedures. The planning of surgical procedures can be done either by MEDICREA as part of the service of designing patient specific implant (surgeons will have to validate the planning submitted by MEDICREA before the manufacturing of any implants) or by the surgeon himself. The image formats supported encompass the standard image formats (jpeg, png, gif). Measurements (generic, measuring and surgical tools) can be overlaid to each image. UNiD Spine Analyzer offers the ability to plan certain surgical procedures, such as osteotomies of the spine, and templating implants (screws, cages and rods). Patient specific rods can be ordered to be manufactured by MEDICREA. UNiD Spine Analyzer is a web-based software.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text doesn't explicitly state formal "acceptance criteria" with upper or lower bounds. Instead, it presents performance metrics of the subject device (UNiD Spine Analyzer) and compares them to its predicate (Surgimap 2.0), aiming to demonstrate substantial equivalence. The implication is that if the UNiD Spine Analyzer's performance is sufficiently comparable to the predicate, it meets the unstated acceptance criteria for safety and effectiveness.

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

The text states:

• "For basic measurement testing (angles and distances), random lines and angles have been drawn and measured by two different tools..."
• "For surgical tools (wedge and cage), sets of images were created with the wedge(s) or cage(s) to apply."

This indicates that the test set consisted of artificially generated lines, angles, and images with applied surgical tools, rather than a clinical dataset of patient images. Therefore:

• Sample Size: Not explicitly stated as a number of "cases" or "patients." It refers to "several configurations and values were tested" for basic measurements and "sets of images were created" for surgical tools. The exact numerical count of these configurations/images is not provided.
• Data Provenance: The data was synthetically generated or created for the purpose of testing, not derived from real-world patient data. There is no country of origin of data or indication of retrospective/prospective study, as it's not a clinical data set.

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

Based on the description, the "ground truth" for the test set was not established by human experts in the traditional sense of clinical assessment. Instead, for basic measurements, it appears the "true" values for the drawn lines and angles were known due to their synthetic generation. For surgical tools, the "true" placement or effect of the wedge/cage was inherently known from their application to the created images.

• No human experts were used to establish the "ground truth" for the test set; the ground truth was inherent in the synthetic generation of the test data.

4. Adjudication Method for the Test Set

Since the ground truth was inherent in the synthetic generation of the test data and not based on expert interpretation, no adjudication method was used. The comparison was between the measurements/applications of the UNiD Spine Analyzer and the predicate device (Surgimap), and the known synthetic values.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly stated or described. The study focused on the performance of the algorithm itself (standalone) and its comparison to the predicate software, not on how human readers perform with or without AI assistance.

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

Yes, a standalone performance study was done. The description focuses on the measurements and applications performed by the UNiD Spine Analyzer (and Surgimap) on the test data, independently of human interaction for interpretation post-measurement. The human intervention required is for "interpretation and manipulation of images" which is a general function of such software, not part of the performance evaluation method described.

7. The Type of Ground Truth Used

The ground truth used was synthetic/known values. For basic measurements (angles and distances), the values were known because "random lines and angles have been drawn". For surgical tools (wedge and cage), the effect was known because "sets of images were created with the wedge(s) or cage(s) to apply." This is not expert consensus, pathology, or outcomes data.

8. The Sample Size for the Training Set

The provided text does not mention a training set or its sample size. The description of the performance data focuses solely on verification and validation activities and testing against the predicate device using artificially generated data. This suggests that if the device uses machine learning, the details of its training were not disclosed in this section.

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

Since no training set is mentioned, no information is provided on how its ground truth was established.

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