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spineEOS software is indicated for assisting healthcare professionals with preoperative planning of spine surgeries. spineEOS provides access to EOS images with associated 3D datasets and measurements. spineEOS includes surgical planning tools that enable users to define a patient specific surgical strategy.

spineEOS is a software indicated for assisting healthcare professionals with preoperative planning of spine surgeries. EOS images (generated from EOS imaging's acquisition system) and associated 3D datasets are used as inputs of the software. The product manages clinical measurements and allows user to access surgical planning tools to define a patient specific surgical strategy. The product is indicated for adolescent and adult patients.

The provided document is a 510(k) premarket notification for the device spineEOS. It primarily focuses on demonstrating substantial equivalence to a predicate device (K160407 spineEOS) rather than detailing a clinical study with acceptance criteria and performance data for a novel AI/ML-based device.

Therefore, the document does not contain the detailed information required to fully answer the request, specifically relating to a study proving the device meets acceptance criteria for new or modified AI/ML features. The information regarding performance data (Section 7) states that "Nonclinical performance testing performed on the subject device, spineEOS, supports substantial equivalence to the predicate device" and explicitly, "Determination of substantial equivalence is not based on an assessment of clinical performance data."

However, based on the provided text, I can extract information relevant to the comparison for substantial equivalence as presented, and highlight what is missing for a comprehensive answer on acceptance criteria and a study proving performance for a new AI/ML feature.

Here's an attempt to answer the questions based on the available information, noting where data is absent:

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

The document does not provide a table of acceptance criteria for specific performance metrics of a new AI/ML model. Instead, it presents a comparison for substantial equivalence between the modified spineEOS and its predicate (K160407 spineEOS). This comparison focuses on demonstrating that technological differences do not affect safety or effectiveness, not on proving a specific quantitative performance metric against a set acceptance criterion for a novel feature.

Here's a re-interpretation of "acceptance criteria" based on the "Substantially Equivalent?" column in Table 5-1, which represents the justification for equivalence rather than quantitative performance.

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

The document states "Nonclinical performance testing performed on the subject device, spineEOS, supports substantial equivalence to the predicate device." It also mentions "Validation activities" including "Validation of the multifunctional requirements in terms of design" and "Usability testing".

• Sample Size for Test Set: This information is not provided. The document makes no mention of a specific dataset size for evaluating the performance of the claimed features, especially for any new AI/ML components. Given the "nonclinical performance" and "not based on clinical performance data" statements, it's highly likely that a traditional "test set" in the context of an AI/ML study (i.e., a set of patient cases to evaluate algorithm performance) was not part of this 510(k) submission.
• Data Provenance: This information is not provided. (e.g., country of origin, retrospective or prospective).

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. Since the submission states "Determination of substantial equivalence is not based on an assessment of clinical performance data," it's unlikely that such an expert-adjudicated ground truth dataset, typical for AI/ML device evaluations, was used. The focus was on demonstrating that the modified features (like the new Spondylolisthesis tool) function as intended and do not introduce new safety concerns compared to the predicate, rather than on proving diagnostic or measurement accuracy against a gold standard.

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

This information is not provided.

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

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "Determination of substantial equivalence is not based on an assessment of clinical performance data." The core of this 510(k) is demonstrating substantial equivalence based on technological comparisons and nonclinical performance (e.g., software verification and validation activities), not an MRMC study.

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

This information is not explicitly provided. The document mentions "Validation of the multifunctional requirements in terms of design" and "Usability testing." The product, spineEOS, is presented as a tool "assisting healthcare professionals," implying a human-in-the-loop model for its primary use ("clinical judgment and experience are required to properly use the software"). There's no separate mention of a "standalone" algorithm-only performance evaluation, especially one related to diagnostic accuracy without human input.

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

Since the submission states it's not based on clinical performance data and focuses on substantial equivalence via nonclinical V&V, the concept of "ground truth" in the context of clinical accuracy (e.g., expert consensus, pathology) for performance claims is not applicable or provided. The "ground truth" for the V&V activities would be the design specifications and requirements.

8. The sample size for the training set

This information is not provided. The document does not describe the specific AI/ML algorithms used or their training. The changes highlighted (like the new Spondylolisthesis tool) are additions to existing functionality, and the filing focuses on their technological equivalence and safety, not on a new AI/ML model requiring a training dataset.

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

This information is not provided, as there is no mention of a training set for an AI/ML model for this submission.

Summary of what the document does provide regarding performance and validation:

• Nonclinical performance testing: This includes design input review, unit testing, software integration, system integration, validation of multifunctional requirements in terms of design, and usability testing.
• Safety & Effectiveness Argument: The core argument is that the modified device, including new tools like "Spondylolisthesis" and "Rod Curvature Management," operates on principles similar to existing cleared functionalities (e.g., segmental alignment tool), and therefore does not raise new questions of safety or effectiveness.
• No Clinical Performance Data: The submission explicitly states that "Determination of substantial equivalence is not based on an assessment of clinical performance data." This significantly limits the type of "acceptance criteria" and "study" information available, as it implies a focus on V&V of software functionality and comparison to predicate rather than a clinical trial proving new performance claims.

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Using 3D data and models obtained with sterEOS workstation, spineEOS software is indicated for assisting healthcare professionals in viewing, measuring images as well as in preoperative planning of spine surgeries. The device includes tools for measuring spine anatomical components for placement of surgical implants. Clinical judgment and experience are required to properly use the software online.

spineEOS 1.0 allows surgeons to perform preoperative surgical planning of spine surgeries in case of Adolescent Idiopathic Scoliosis (AIS) or deformative spine. The software provides surgical tools for the correction of the curvature, for the placement of cages and for the achievement of osteotomies. The images displayed are x-rays from EOS System (K152788) and 3D model of the spine from sterEOS Workstation (K141137). spineEOS also displays preoperative parameters compared with reference values and updated values of parameters after planning. spineEOS is accessible on any computer via ONEFIT Management System (Class I device - Product code LMD - 510(k) Exempt) that provides a secure interface and storage through authentication mechanisms.

The FDA 510(k) summary for spineEOS provides some information regarding its performance data, but it does not contain a detailed study with acceptance criteria, specific reported device performance metrics, sample sizes, or information about experts and ground truth as requested.

The document primarily focuses on establishing substantial equivalence to a predicate device (Surgimap 2.0) by comparing intended use, indications, and technological characteristics.

Here's an analysis of what is available and what is missing from the provided text, structured according to your request:

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

• Missing from the document. The document states: "Software verification and validation testing were conducted and documentation was provided as recommended by FDA's Guidance for Industry and FDA Staff, 'Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices'." However, specific acceptance criteria or detailed results of these tests (e.g., accuracy of measurements, success rate of planning tools) are not provided in this 510(k) summary.

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

• Missing from the document. The summary mentions "Software verification and validation testing," but does not specify the sample size of any test set or the provenance of the data used for such testing.

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)

• Missing from the document. There is no mention of experts, ground truth establishment, or their qualifications for any validation testing.

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

• Missing from the document. No information about adjudication methods for a test set is provided.

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

• Missing from the document. The document makes no mention of a multi-reader multi-case (MRMC) comparative effectiveness study. The focus is on demonstrating equivalence to the predicate device's existing functionality rather than quantifying human performance improvements.

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

• Implied, but not detailed. The "Software verification and validation testing" would typically involve standalone performance testing of the algorithms and software functionalities. However, the specifics of these tests and their results are not detailed. The spineEOS is described as "assisting healthcare professionals," implying it's a human-in-the-loop device, but standalone testing of its components would be part of standard V&V. Again, no specific results are provided.

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

• Missing from the document. As no specific performance study is detailed, the type of ground truth used is not mentioned.

8. The sample size for the training set

• N/A (or not explicitly stated as a "training set"). The spineEOS is a software for viewing, measuring, and planning based on existing 3D data and models (from sterEOS workstation). It's not described as a machine learning device that requires a distinct "training set" in the sense of a deep learning model. Its validation would focus on the accuracy of its measurements and the functionality of its planning tools against known standards or expert opinion, not on learning from a dataset.

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

• N/A. Since a classical machine learning "training set" is not explicitly mentioned or implied for this type of device, the method for establishing its ground truth is not applicable in that context.

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