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VEA Align:
This cloud-based software is intended for orthopedic applications in both pediatric and adult populations.
2D X-ray images acquired in EOS imaging's imaging systems is the foundation and resource to display the interactive landmarks overlayed on the frontal and lateral mages. These landmarks are available for users to assess patient-specific global alignment.
For additional assessment, alignment parameters compared to published normative values may be available.
This product serves as a tool to aid in the analysis of spinal deformities and degenerative diseases, and lower limb alignment disorders and deformities through precise and length measurements. It is suitable for use with adult and pediatric patients aged 7 years and older.
Clinical judgment and experience are required to properly use the software.

spineEOS:
spineEOS is indicated for assisting healthcare professionals with preoperative planning of spine surgeries. The product 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.

VEA Align is a software indicated for assisting healthcare professionals with global alignment assessment through clinical parameters computation. The product uses biplanar 2D X-ray images, exclusively generated by EOS imaging's EOS (K152788) and EOSedge (K202394) systems and generates an initial placement of the patient anatomic landmarks on the images using a machine learning-based algorithm. The user may adjust the landmarks to align with the patient's anatomy. Landmark locations require user validation. The clinical parameters communicated to the user are inferred from the landmarks and are recalculated as the user adjusts the landmarks. 3D datasets may be exported for use in spineEOS for surgical planning. The product is hosted on a cloud infrastructure and relies on VEA Portal for support capabilities, such as user access control and data access. 2D X-ray image transmissions from healthcare institutions to the cloud are managed by VEA Portal is a Class | 510(k)-exempt device (LMD).

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 text describes the performance data for the VEA Align device, focusing on the standalone performance of its AI algorithm.

Here's the breakdown of the acceptance criteria and the study proving the device meets them:

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

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

• Test set sample size: 538 patients.
• Data provenance: Not explicitly stated as country of origin, but the images were collected from EOS (K152788) and EOSedge (K202394) systems at a variety of sites. The subgroup analysis includes "US vs. OUS" (Outside US), implying international data. The data collection period was from 2007-2023. The study seems to be retrospective as it uses previously collected images.

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

The document states that the ground truth for the test set was an "EOS 3DServices reconstruction (model) from sterEOS Workstation (K172346)". It does not explicitly state the number or qualifications of experts used to establish this ground truth for the test set. However, the nature of the sterEOS Workstation suggests that these 3D reconstructions are typically performed or validated by trained specialists.

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

The document does not specify an adjudication method for the test set ground truth. It relies on the "ground truth EOS 3DServices reconstruction (model) from sterEOS Workstation (K172346)."

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 MRMC comparative effectiveness study was described where human readers' improvement with AI vs. without AI assistance was evaluated. The performance testing focused on the standalone performance of the AI algorithm. The VEA Align device involves a machine learning-based algorithm for initial landmark placement, but then explicitly states, "The user may adjust the landmarks to align with the patient's anatomy. Landmark locations require user validation." This implies a human-in-the-loop system, but the described performance study is primarily on the algorithm's initial accuracy, not human improvement.

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

Yes, a standalone performance test of the AI algorithm was done. The document explicitly states: "To assess the standalone performance of the Al algorithm of the VEA Align, the test was performed with..."

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

The ground truth used for the standalone algorithm performance was "a ground truth EOS 3DServices reconstruction (model) from sterEOS Workstation (K172346)". This suggests a reconstructed anatomical model derived from clinically used software, likely validated by trained operators or experts who generated that model previously.

8. The sample size for the training set

The AI algorithm was trained using 10,376 X-ray images and a total of 5,188 corresponding 3D reconstructions.

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

The document states that the training data included "corresponding 3D reconstructions" presumably generated by sterEOS Workstation (K172346), similar to the test set ground truth. These 3D reconstructions would have been based on images from EOS systems and likely performed by trained personnel using the sterEOS Workstation. It's implied that these served as the ground truth for training the AI algorithm to generate its initial placements.

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This cloud-based software is intended for orthopedic applications in both pediatric and adult populations. 2D X-ray images acquired in EOS imaging's imaging systems is the foundation and resource to display the interactive landmarks overlayed on the frontal and lateral images. These landmarks are available for users to assess patient-specific global alignment. For additional assessment, alignment parameters compared to published normative values may be available. This product serves as a tool to aid in the analysis of spinal deformities, degenerative diseases, lower limb alignment disorders, and deformities through precise angle and length measurements. It is suitable for use with adult and pediatric patients aged 7 years and older. Clinical judgment and experience are required to properly use the software.

VEA Align is a software indicated for assisting healthcare professionals with global alignment assessment through clinical parameters computation. The product uses biplanar 2D X-ray images, exclusively generated by EOS imaging's EOS (K152788) and EOSedge (K202394) systems and generates an initial placement of the patient anatomic landmarks on the images using a machine learning-based algorithm. The user may adjust the landmarks to align with the patient's anatomy. Landmark locations require user validation. The clinical parameters communicated to the user are inferred from the landmarks and are recalculated as the user adjusts the landmarks. The product is hosted on a cloud infrastructure and relies on VEA Portal for support capabilities. such as user access control and data access. 2D X-ray image transmissions from healthcare institutions to the cloud are managed by VEA Portal is a Class I 510(k)-exempt device (LMD).

The provided text describes the VEA Align device and its performance testing to support its substantial equivalence to a predicate device. However, it does not contain a detailed table of acceptance criteria with reported device performance metrics that would typically be found in a comprehensive study report. It states that "Direct comparison between skeletal landmark locations between the subject VEA Align device and predicate sterEOS Workstation (K172346) met acceptance criteria for algorithm performance," but it does not quantify these criteria or the specific performance results.

Therefore, some of the requested information cannot be directly extracted from the provided text. I will provide what is available and note what is missing.

Here's the breakdown of the information:

1. Table of Acceptance Criteria and Reported Device Performance

The document states: "Direct comparison between skeletal landmark locations between the subject VEA Align device and predicate sterEOS Workstation (K172346) met acceptance criteria for algorithm performance." However, the specific quantitative acceptance criteria (e.g., maximum allowable error for landmark placement) and the actual numerical performance results (e.g., mean absolute error) are not provided in this text.

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

• Test Set Sample Size: 555 patients.
• Data Provenance: The images were acquired from "EOS (K152788) and EOSedge (K202394) systems." The country of origin and whether the data was retrospective or prospective are not explicitly stated.

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

This information is not provided in the text. The document refers to the predicate device manually deforming a 3D model through control points to match X-ray contours, which implies expert interaction in the past, but it does not describe how ground truth was established for the 555-patient test set for the VEA Align device.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

This information is not provided in the text.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs. without AI assistance.

A MRMC comparative effectiveness study involving human readers with and without AI assistance is not mentioned in the provided text. The performance testing focuses on the standalone algorithm's comparison to the predicate device.

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

Yes, a standalone performance assessment was done. The text states:
"Standalone performance assessment of the machine learning algorithm. The testing dataset consisted of 555 patients... Direct comparison between skeletal landmark locations between the subject VEA Align device and predicate sterEOS Workstation (K172346) met acceptance criteria for algorithm performance."

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The ground truth for the standalone performance assessment appears to be based on the "skeletal landmark locations" derived from the predicate sterEOS Workstation (K172346). This implies that the predicate's output, which involved manual deformation by users ("The 3D model is deformed manually by the user through control points up to matching accurately the X-ray contours. This deformation is performed by using the common linear least squares estimation algorithm."), served as the reference for the VEA Align's automated landmark placement. It is not explicitly stated that an independent expert consensus or pathology was used directly for the 555-patient test set for the standalone evaluation of VEA Align, but rather conformance to the predicate's output.

8. The Sample Size for the Training Set

The sample size for the training set is not explicitly stated in the provided text. It mentions that the machine learning algorithm was "trained from data generated by EOS Imaging's imaging systems", but it doesn't quantify the size of this training dataset.

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

The text states that the machine learning algorithm learns to generate "an initial placement of the patient anatomic landmarks on the images" and that "The user may adjust the landmarks to align with the patient's anatomy." For the predicate device, it mentions "identification of anatomical landmarks" or "a model of bone structures derived from an a priori image data set from 175 patients (91 normal patients, 47 patients with moderate idiopathic scoliosis and 37 patients with severe idiopathic scoliosis), and dry isolated vertebrae data for spine modeling."

While it implies that human interaction and potentially pre-existing models established the ground truth used for training, the specific methodology and who established the ground truth labels for the VEA Align training set are not detailed. It implies the machine learning was "trained from data generated by EOS Imaging's imaging systems," which suggests leveraging existing data from their systems and prior approaches (potentially like the predicate).

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