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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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The sterEOS Workstation is intended for use in the fields of musculoskeletal radiology and orthopedics in both pediatric and adult populations as a general device for acceptance, transfer, display, storage, and digital processing of 2D X-ray images of the musculoskeletal system including interactive 2D measurement tools.

When using 2D X-ray images obtained with the EOS Imaging EOS System, the sterEOS Workstation provides interactive 3D measurement tools:

• · To aid in the analysis of scoliosis and related disorders and deformities of the spine in adult patients as well as pediatric patients. The 3D measurement tools interactive analysis based either on identification of anatomical landmarks for postural assessment, or on 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 model of bone structures is not intended for use to assess individual vertebral abnormalities and is indicated only for patients 7 years and older. For postural assessment, a set of comparative tools is provided allowing the comparison of performed measurements to reference values for patients over 18 years old.
• · To aid in the analysis of lower limbs alignment and related disorders and deformities based on angle and length measurements. The 3D measurement tools include interactive analysis based either on identification of lower limb alignment landmarks or as for the spine, on a model of bone structures derived from an a priori image data set. The model of bone structures is not intended for use to assess individual bone abnormalities. The 3D package including model-based measurements and torsion angles is indicated only for patients 15 years or older. Only the 2D/3D ruler is indicated for measurements in patients younger than 15 years old.

The sterEOS Workstation is a general system for acceptance, transfer, display, storage, and digital processing of 2D X-ray images of the musculoskeletal system, including interactive 2D measurement tools.

When used with 2D X-ray images obtained with the EOS imaging's EOS System (K152788), the sterEOS Workstation provides interactive 3D measurement tools to aid in the analysis of skeletal deformities in spine and lower limbs.

The provided text describes a 510(k) premarket notification for the sterEOS Workstation, a device for processing 2D X-ray images and providing 3D measurement tools for skeletal deformities. The submission focuses on revisions to contraindications for spine modeling and minor software/hardware modifications.

However, the document does not contain the detailed information required to fill out a table of acceptance criteria and reported device performance based on a rigorous study. The "Performance Data" section primarily discusses functional testing and verification of software modifications, rather than a clinical study with specific performance metrics against acceptance criteria.

Here's an analysis of what is available and what is missing based on your request:

1. Table of acceptance criteria and the reported device performance:

• Missing. The document mentions "compliance with specifications, performance and non-regression" for software modifications, and that "additional performance and functional testing has confirmed the equivalent performance of the modified sterEOS Workstation compared to the predicate sterEOS." However, it does not specify what those specifications, performance metrics, or acceptance criteria were, nor does it provide quantified results of the performance against said criteria.

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

• For the modification related to contraindications: "functional testing of the spine 3D modeling workflows for the no longer contraindicated types of spine with extra vertebra, spine with missing vertebra, and spine with spondylolisthesis." This implies a test set composed of such cases, but the exact sample size is not stated. The data provenance is not explicitly mentioned (e.g., country of origin, retrospective/prospective).
• For the training set of the original model: "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." This refers to the data used for the original 3D spine model, not necessarily a test set for the modifications presented in this 510(k). This data would likely be considered retrospective.

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

• For the functional evaluation study on revised contraindications: "a functional evaluation study conducted by internal experienced radiographers from EOS imaging."
  • Number of experts: Not specified, but plural ("radiographers") implies more than one.
  • Qualifications: "internal experienced radiographers from EOS imaging." Further specific qualifications (e.g., years of experience, board certification) are not provided.
• For the original model (when establishing its ground truth): Not stated in this document.

4. Adjudication method for the test set:

• Not specified. The document mentions a "functional evaluation study conducted by internal experienced radiographers," but does not detail how their findings were reconciled or adjudicated.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No. The document does not describe an MRMC study comparing human readers with and without AI assistance. The functional evaluation study was to verify "ease to perform a 3D modeling and to obtain an adjusted outline," not a comparative effectiveness study involving human readers' diagnostic accuracy or efficiency with AI assistance.

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

• The "functional testing of the spine 3D modeling workflows" evaluated the software's ability to model specific spine types. While this involves the algorithm's performance, it's not described as a formal standalone performance study with specific quantitative metrics like sensitivity, specificity, or comparable clinical endpoints. The "functional evaluation study" involved radiographers, indicating a human-in-the-loop component for verification.

7. The type of ground truth used:

• For the functional evaluation of revised contraindications: The ground truth appears to be the ability of the software to successfully perform 3D modeling and provide an adjusted outline that matches the vertebrae for the previously contraindicated spine types. This is based on expert visual assessment ("internal experienced radiographers"). It's more of a qualitative "functional" ground truth rather than a clinical diagnostic ground truth like pathology or long-term outcomes.
• For the original model: Ground truth for the "model of bone structures" was derived from "an a priori image data set from 175 patients" and "dry isolated vertebrae data for spine modeling." How the ground truth for these foundational datasets was established is not detailed in this document.

8. The sample size for the training set:

• "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." This refers to the data used to build the original 3D model, which serves as a training or development set for the model itself.

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

• Not detailed in this document. It states the model was "derived from an a priori image data set," but how the ground truth for those 175 patient cases or the dry isolated vertebrae was established (e.g., manual annotation by experts, specific measurements, pathology) is not described here.

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kneeEOS is a software indicated for assisting orthopedic surgeon in preoperative planning of knee orthopedic surgery. The software allows for overlaying of 3D/2D implants models on radiological images EOS based and 3D reconstruction of bones, and includes tools for performing measurements on the images or 3D model of bones, and for selecting and positioning the implant model. Clinical judgments and experience are required to properly use the software.

kneeEOS 1.0 allows surgeons to perform preoperative surgical planning of total knee arthroplasties. The software provides surgical tools to analyze preoperative data, to set the resection levels, to position and size the femoral and tibial components and to evaluate the final alignment of the leg. The images displayed are x-rays from EOS System (K152788) and 3D model of the knee from sterEOS Workstation (K160914). kneeEOS also displays preoperative parameters and updated values of these parameters after planning. kneeEOS is accessible on any computer via ONEFIT Management System (Class I device - Product code LMD - 510(k) Exempt) that provides a secure internet interface and storage through authentication mechanisms.

The provided text is a 510(k) summary for the ONEFIT medical kneeEOS device. It describes the device, its intended use, and compares it to a predicate device (TraumaCad Release 2.0). However, the document does not contain explicit acceptance criteria, performance metrics, or details of a specific study to prove the device meets said criteria.

Instead, the document states that "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."" This general statement indicates that testing was performed according to FDA guidelines for software in medical devices, but it does not provide the specific performance data requested.

Therefore, I cannot populate the table or answer the specific questions about the study from the provided text. The document concludes that the device is "as safe and effective as its predicate" based on "Performance data demonstrate that the kneeEOS is as safe and effective as its predicate," but these performance data are not detailed in this 510(k) summary.

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The sterEOS Workstation is intended for use in the fields of musculoskeletal radiology and orthopediativ and adult populations as a general device for acceptance, transfer, display, storage, and digital processing of 2D X-ray images of the musculoskeletal system including interactive 2D measurement tools.

When using 2D X-ray images obtained with the EOS imaging EOS System, the sterEOS Workstation provides interactive 3D measurement tools:

• To aid in the analysis of scoliosis and related disorders and deformities of the spine in adult patients. The 3D measurement tools interactive analysis based either on identification of anatomical landmarks for postural assessment or on a model of bone structures derived from an a priori image data set from 175 patients, 47 patients with moderate idiopathic scoliosis and 37 patients with scoliosis), and dry isolated vertebrae data for spine modeling. The model of bone structures is not intended for use to assess individual vertebral abnormalities and is indicated only for patients 7 years and older. For postural assessment, a set of comparative tools is provided allowing the comparison of performed measurements to reference values for patients over 18 years old.
• To aid in the analysis of lower limbs alignment and deformities based on angle and length measurements. The 3D measurement tools include interactive analysis based either on identification of lower limb alignment landmarks or as for the spine, on a model of bone structures derived from an a priori image data set. The model of bone structures is not intended for use to assess individual bone abnormalities. The 3D package including model-based measurements and torsion angles is indicated only for patients 15 years or older. Only the 2D/3D ruler is indicated for measurements in patients younger than 15 years old.

The sterEOS Workstation is a general system for acceptance, transfer, display, storage, and digital processing of 2D X-ray images of the musculoskeletal system, including interactive 2D measurement tools.

When used with 2D X-ray images obtained with the EOS imaging's EOS System (K152788), the sterEOS Workstation provides interactive 3D measurement tools to aid in the analysis of skeletal deformities in spine and lower limbs.

The provided text appears to be a 510(k) summary for the sterEOS Workstation, a medical device for processing X-ray images, and not a study with detailed acceptance criteria and performance data. Therefore, the information requested in the prompt, especially regarding specific acceptance criteria, study methodologies, and quantitative results like sample sizes for test and training sets, number and qualifications of experts, and MRMC study effect sizes, is not present in the provided document.

The document discusses that the device has undergone software verification and validation testing to confirm compliance with specifications, performance, and non-regression. It also states that additional performance and functional testing was performed, and this testing "confirmed the equivalent performance of the modified sterEOS Workstation compared to the predicate sterEOS." However, it does not provide the details of these tests, such as the specific acceptance criteria, the actual performance metrics, or the study methodologies used.

The document states that the new device is "substantially equivalent" to its predicate device (K14137 sterEOS Workstation) due to having the same intended use, similar technological characteristics, and minor differences that do not raise new questions of safety or effectiveness. This suggests that the approval is based on demonstrating equivalence rather than meeting new, specific acceptance criteria through a standalone clinical study detailed in this summary.

Therefore, I cannot populate the table or answer the specific questions because the detailed information about acceptance criteria and the comprehensive study results are not provided in this 510(k) summary.

Here's a summary of what is available related to the request, and what is missing:

The 510(k) summary indicates that the sterEOS Workstation is a software upgrade to an existing device (K14137). The basis for approval is "substantial equivalence" to the predicate.

Missing Information:

• Detailed Acceptance Criteria and Performance Data: The summary states "performance data demonstrate that the device is as safe and effective," but it does not provide a table with specific acceptance criteria (e.g., minimum accuracy, precision, sensitivity, specificity for 3D measurements) or the reported device performance against those criteria.
• Sample sizes for test set and data provenance: Not mentioned.
• Number of experts and qualifications for ground truth: Not mentioned.
• Adjudication method: Not mentioned.
• MRMC comparative effectiveness study: Not mentioned. The focus is on demonstrating equivalence to the predicate device.
• Standalone performance study: The document refers to "software verification and validation testing" and "additional performance and functional testing" which imply standalone testing, but no detailed results or methodology for such a study (beyond "equivalent performance") are provided.
• Type of ground truth used (for performance testing): Not specified.
• Sample size for the training set: The "model of bone structures" for spine analysis is derived from an a priori image data set from 175 patients (91 normal, 47 moderate idiopathic scoliosis, 37 severe idiopathic scoliosis) and dry isolated vertebrae data for spine modeling. This 175-patient dataset likely serves as the basis for the model, which could be considered training/development data for the model-based measurements. For lower limbs, it also mentions "a model of bone structures derived from an a priori image data set," but doesn't specify the size or nature of this dataset here.
• How ground truth for the training set was established: Not explicitly stated, though for the 175-patient spine data, it implies clinical diagnoses (normal, moderate/severe idiopathic scoliosis).

In conclusion, the provided document is a regulatory submission focused on demonstrating substantial equivalence rather than a detailed scientific study report. It states that performance testing confirmed equivalence but does not offer the granular data requested.

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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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