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AutoDensity is a post-processing software intended to estimate spine Bone Mineral Density (BMD) from EOSedge dual energy images for orthopedic pre-surgical assessment applications. It is an opportunistic tool that enables immediate assessment of bone density from EOSedge images acquired for other purposes.

AutoDensity is not intended to replace DXA screening. Suspected low BMD should be confirmed by a DXA exam.

Clinical judgment and experience are required to properly use the software.

Based on EOSedge™ system's images acquired with the dual energy protocols cleared in K233920, AutoDensity software provides an estimate of the Bone Mineral Density (BMD) for L1-L4 in EOSedge AP radiographs of the spine. These values are used to aid in BMD estimation in orthopedic surgical planning workflows to help inform patient assessment and surgical decisions. AutoDensity is opportunistic in nature and provides BMD information with equivalent radiation dose compared to the EOSedge images concurrently acquired and used for general radiographic exams. AutoDensity is not intended to replace DXA screening.

Here's a breakdown of the acceptance criteria and the study details for the AutoDensity device, based on the provided FDA 510(k) clearance letter:

1. Acceptance Criteria and Reported Device Performance

Device Name: AutoDensity
Intended Use: Post-processing software to estimate spine Bone Mineral Density (BMD) from EOSedge dual energy images for orthopedic pre-surgical assessment applications.

2. Sample Sizes and Data Provenance

Test Set (for ROI Performance Evaluation):

• Sample Size: 129 patients.
• Data Provenance: All cases obtained from EOSedge systems (K233920). The document does not specify the country of origin but mentions a clinical study with 65% US subjects and 35% French subjects for clinical performance testing, which might suggest a similar distribution for the test set, though it's not explicitly stated for the ROI test set. The data was retrospective as it was "obtained from EOSedge systems."

3. Number of Experts and Qualifications for Ground Truth

For ROI Performance Evaluation Test Set:

• Number of Experts: At least 3 (implied by "3 truther majority voting principle") plus one senior US board certified expert radiologist who acted as the gold standard adjudicator.
• Qualifications:
  • Two trained technologists (for initial ROI and level identification).
  • One senior US board-certified expert radiologist (for supervision, review, selection of most accurate set, and final adjustments).

4. Adjudication Method for the Test Set

For ROI Performance Evaluation Test Set:

• Adjudication Method: A "3 truther majority voting principle" was used, with input from a senior US board-certified expert radiologist (acting as the "gold standard"). The radiologist reviewed results, selected the more accurate set, and made necessary adjustments. This combines elements of majority voting with expert adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was an MRMC study done? No, the provided document does not mention an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The performance data presented focuses on the standalone performance of the AI algorithm and its agreement/precision with a reference device or clinical measurements.

6. Standalone Performance Study (Algorithm Only)

• Was a standalone study done? Yes. The "Region of Interest (ROI) Performance Evaluation" section explicitly states: "To assess the standalone performance of the AI algorithm of AutoDensity, the test was performed with..." This section details the evaluation of the algorithm's predictions against ground truth for vertebral level identification and spine ROI accuracy.

7. Type of Ground Truth Used

For ROI Performance Evaluation Test Set:

• Type of Ground Truth: Expert consensus with adjudication. Ground truths for ROIs and level identification were established by two trained technologists under the supervision of a senior US board-certified radiologist. The radiologist made the final informed decision, often described as a "gold standard."

8. Sample Size for the Training Set

• Training Set Sample Size: The AI algorithm was trained using 4,679 3D reconstructions and 9,358 corresponding EOS (K152788) or EOSedge (K233920) biplanar 2D X-ray images.

9. How Ground Truth for the Training Set was Established

• The document implies that the training data was "selected to only keep relevant images with the fields of view of interest." However, it does not explicitly detail how the ground truth for the training set was established (e.g., whether it used expert annotations, a similar adjudication process, or other methods). It primarily focuses on the test set ground truth establishment.

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Standalone software for medical image analysis intended for advanced visualization and quantitative analysis for diagnostics in the field of cardiology or radiology by means of enabling visualization and measurement of structures of the heart and vessels for:

• Pre-operational planning and sizing for cardiovascular interventions and surgery
• Postoperative evaluation
• Support of clinical diagnosis by quantifying dimensions in coronary arteries
• Support of clinical diagnosis by quantifying calcifications (calcium scoring)

To facilitate the above, the 3mensio Workstation provides general functionality such as:

• Segmentation of cardiovascular structures
• Automatic and manual centerline detection
• Visualization and image reconstruction techniques: 2D review, Volume Rendering, MPR, Curved MPR, Stretched CMRP, Slabbing, MIP, AIP, MinIP
• Measurement and annotation tools
• Reporting tools

3mensio Workstation is an image post-processing software package for advanced visualization and analysis for diagnostics in the field of cardiology and radiology and offers functionality to view CT/X-Ray angiographic and ultrasound images, to segment cardiovascular structures in these images and to analyze and quantify these cardiovascular structures and to present the results in different formats.

3mensio Workstation can be deployed as a web-based solution intended for usage in a network or cloud environment or as a standalone package and runs on a PC with a Windows operating system. It can read DICOM images from an accessible file system, hard disk (local directory), or (indirectly) received from the CT or PACS system. 3mensio Workstation provides the functionality to import the DICOM images and to organize the loaded DICOM images into patients, studies, and series.

3mensio Workstation contains two analysis modules: 3mensio Vascular and 3mensio Structural Heart:

• 3mensio Vascular enables assessment of vessels and can help to measure calcifications, aneurysms and other anomalies to quickly and reliably prepare for various types of vascular procedures.

• 3mensio Structural Heart enables assessment and measurement of different structures of the heart, e.g., the heart valves, coronary arteries and the ventricles. It provides analysis of different approach routes to cardiovascular structures for replacement or repair procedures. In addition, it can help in the quantification of calcifications.

Results can be displayed on the screen, printed, or saved in a variety of formats to hard disk, network or PACS system. Results and clinical images with overlay can also be printed as a hardcopy and exported in various electronic formats.

This document describes the FDA 510(k) clearance letter for the 3mensio Workstation. The following information is extracted and organized as requested.

Acceptance Criteria and Device Performance Study for 3mensio Workstation (K250330)

The 3mensio Workstation is a medical image analysis software intended for advanced visualization and quantitative analysis for diagnostics in cardiology or radiology. It provides functionalities for pre-operational planning, postoperative evaluation, and support of clinical diagnosis through quantifying dimensions and calcifications in cardiovascular

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The AiORTA - Plan tool is an image analysis software tool for volumetric assessment. It provides volumetric visualization and measurements based on 3D reconstruction computed from cardiovascular CTA scans. The software device is intended to provide adjunct information to a licensed healthcare practitioner (HCP) in addition to clinical data and other inputs, as a measurement tool used in assessment of aortic aneurysm, pre-operative evaluation, planning and sizing for cardiovascular intervention and surgery, and for post-operative evaluation in patients 22 years old and older.

The device is not intended to provide stand-alone diagnosis or suggest an immediate course of action in treatment or patient management.

AiORTA - Plan is a cloud-based software tool used to make and review geometric measurements of cardiovascular structures, specifically abdominal aortic aneurysms. The software uses CT scan data as input to make measurements from 2D and 3D mesh based images. Software outputs are intended to be used as a measurement tool used in assessment of aortic aneurysm, pre-operative evaluation, planning and sizing for cardiovascular intervention and surgery, and for post-operative evaluation.

The AiORTA - Plan software consists of two components, the Analysis Pipeline and Web Application.

The Analysis Pipeline is the data processing engine that produces measurements of the abdominal aorta based on the input DICOM images. It consists of multiple modules that are operated by a trained Analyst to preprocess the DICOM images, compute geometric parameters (e.g., centerlines, diameters, lengths, volumes), and upload the results to the Web App for clinician review. The Analyst plays a role in ensuring the quality of the outputs. However, the end user (licensed healthcare practitioner) is ultimately responsible for the accuracy of the segmentations, the resulting measurements, and any clinical decisions based on these outputs.

The workflow of the Analysis Pipeline can be described in the following steps:

• Input: the Analysis Pipeline receives a CTA scan as input.
• Segmentation: an AI-powered auto-masking algorithm performs segmentation of the aortic lumen, wall, and key anatomical landmarks, including the superior mesenteric, celiac, and renal arteries. A trained Analyst performs quality control of the segmentations, making any necessary revisions to ensure accurate outputs.
• 3D conversion: the segmentations are converted into 3D mesh representations.
• Measurement computation: from the 3D representations, the aortic centerline and geometric measurements, such as diameters, lengths, and volumes, are computed.
• Follow-up study analysis: for patients with multiple studies, the system can detect and display changes in aortic geometry between studies.
• Report generation: a report is generated containing key measurements and a 3D Anatomy Map providing multiple views of the abdominal aorta and its landmarks.
• Web application integration: the outputs, including the segmented CT masks, 3D visualizations, and reports, are uploaded to the Web App for interactive review and analysis.

The Web Application (Web App) is the front end and user facing component of the system. It is a cloud-based user interface offered to the qualified clinician to first upload de-identified cardiovascular CTA scans in DICOM format, along with relevant demographic and medical information about the patient and current study. The uploaded data is processed asynchronously by the Analysis Pipeline. Once processing is complete, the Web App then enables clinicians to interactively review and analyze the resulting outputs.

Main features of the Web App include:

• Segmentation review and correction: Clinicians can review the resulting segmentations from the Analysis Pipeline segmentations by viewing the CT slices alongside the segmentation masks. Segmentations can be revised using tools such as a brush or pixel eraser, with adjustable brush size, to select or remove pixels as needed. When clinicians revise segmentations, they can request asynchronous re-analysis by the Analysis Pipeline, which generates updated measurements and a 3D Anatomy Map of the aorta based on the revised segmentations.
• 3D visualization: The aorta and key anatomical landmarks can be examined in full rotational views using the 3D Anatomy Map.
• Measurement tools: Clinicians can perform measurements directly on the 3D Anatomy Map of the abdominal aorta and have access to a variety of measurement tools, including:
  • Centerline distance, which measures the distance (in mm) between two user-selected planes along the aortic centerline.
  • Diameter range, which measures the minimum and maximum diameters (in mm) within the region of interest between two user-selected planes along the aortic centerline.
  • Local diameter, which measures the diameter (in mm) at the user-selected plane along the aortic centerline.
  • Volume, which measures the volume (in mL) between two user-selected planes along the aortic centerline.
  • Calipers, which allow additional linear measurements (in mm) at user-selected points.
• Screenshots: Clinicians can capture images of the 3D visualizations of the aorta or the segmentations displayed on the CT slices.
• Longitudinal analysis: For patients with multiple studies, the Web App allows side-by-side review of studies. Clinicians have access to the same measurement and visualization tools available in single-study review, enabling comparison between studies.
• Reporting: Clinicians can generate and download reports containing either the default key measurements computed by the Analysis Pipeline or custom measurements and screenshots captured during review.

Here's a breakdown of the acceptance criteria and the study details for the AiORTA - Plan device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Explanation for Lengths and Diameters that did not meet initial criteria:
For the following measurements which did not meet the initial acceptance criteria:

• Length: renal to left iliac bifurcation (7.0mm vs ≤ 6.0mm)
• Length: renal to right iliac bifurcation (6.6mm vs ≤ 6.0mm)
• Diameter: wall right iliac (2.5mm vs ≤ 2.3mm)

A Mean Pairwise Absolute Difference (MPAD) comparison was performed. The device-expert MPAD was smaller than the expert-expert MPAD in all three cases, indicating that the device's measurements were more consistent with experts than the experts were with each other.

Study Details for Device Performance Evaluation:

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

   • Auto-segmentation masks and Landmark Identification: The document does not explicitly state the sample size for this specific test, but it mentions using "clinical data, including aortic aneurysm cases from both US and Canadian clinical centers."
   • Diameters and Lengths: The document does not explicitly state the sample size for this specific test, but it mentions using "clinical data, including aortic aneurysm cases from both US and Canadian clinical centers."
   • Volumes: 40 CT scans. The data provenance is "clinical data, including aortic aneurysm cases from both US and Canadian clinical centers." The studies were retrospective, as they involved existing clinical data.

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

   • Auto-segmentation masks and Landmark Identification: 3 US-based board-certified Radiologists.
   • Diameters and Lengths: 3 US-based board-certified Radiologists.
   • Volumes: The ground truth for volumes was established using a reference device (Simpleware ScanIP Medical), not directly by human experts, although the input segmentations for both the device and the reference device were analyst-revised.

3. Adjudication method for the test set:

   • Auto-segmentation masks and Landmark Identification: Ground truth was "annotations approved by 3 US-based board-certified Radiologists." This implies consensus or a primary reader with adjudication, but the exact method (e.g., 2+1, 3+1) is not specified.
   • Diameters and Lengths: Ground truth was "annotations from 3 US-based board-certified Radiologists." Similar to above, the specific consensus method is not detailed.
   • Volumes: Ground truth was established by a reference device, Simpleware ScanIP Medical.

4. 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 explicitly mentioned in the provided text. The testing focused on the standalone performance of the AI-powered components and the consistency of the device's measurements with expert annotations, not on human reader improvement with AI assistance.

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

   • Yes, a standalone performance evaluation of the auto-masking algorithm (prior to analyst correction) was performed for auto-segmentation masks and landmark identification. The results demonstrated the performance of the auto-masking algorithm "independently of human intervention."
   • However, for diameters and lengths, the measurements were "based on segmentations that underwent Analyst review and correction, ensuring that the verification reflects real-world use conditions." This suggests a semi-automatic, human-in-the-loop performance evaluation for these specific metrics.

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

   • Expert Consensus: Used for auto-segmentation masks, landmark identification, diameters, and lengths. The consensus involved 3 US-based board-certified Radiologists.
   • Reference Device: Used for volumes, comparing against results from Simpleware ScanIP Medical.

7. The sample size for the training set:

   • The document does not explicitly state the sample size for the training set. It mentions "critical algorithms were verified by comparing their outputs to ground truth data to ensure accuracy and reliability. Algorithms were first verified using synthetic data...Subsequent verification was performed using clinical data, including aortic aneurysm cases from both US and Canadian clinical centers." This refers to verification data, not necessarily the training data size.

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

   • The document does not provide details on how the ground truth for the training set was established. It only describes the ground truth for the verification/test sets. It can be inferred that similar expert review or other validated methods would have been used for training data, but this is not explicitly stated.

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