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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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Intended Use

Viewing, post-processing, qualitative and quantitative evaluation of blood vessels and cardiovascular CT images in DICOM format.

Indications for Use

cvi42 Coronary Plaque Software Application is intended to be used for viewing, post-processing, qualitative and quantitative evaluation of cardiovascular computed tomography (CT) images in a Digital Imaging and Communications in Medicine (DICOM) Standard format.

It enables a set of tools to assist physicians in qualitative and quantitative assessment of cardiac CT images to determine the presence and extent of coronary plaques and stenoses, in patients who underwent Coronary Computed Tomography Angiography (CCTA) for evaluation of CAD or suspected CAD.

cvi42 Coronary Plaque's semi-automated machine learning algorithms are intended for an adult population.

cvi42 Coronary Plaque shall be used only for cardiac images acquired from a CT scanner. It shall be used by qualified medical professionals, experienced in examining cardiovascular CT images, for the purpose of obtaining diagnostic information as part of a comprehensive diagnostic decision-making process.

Circle's cvi42 Coronary Plaque Software Application ('cvi42 Coronary Plaque' or 'Coronary Plaque Module', for short) is a Software as a Medical Device (SaMD) that enables the analysis of CT Angiography scans of the coronary arteries of the heart. It is designed to support physicians in the visualization, evaluation, and analysis of coronary vessel plaques through manual or semi-automatic segmentation of vessel lumen and wall to determine the presence and extent of coronary plaques and luminal stenoses, in patients who underwent Coronary Computed Tomography Angiography (CCTA) for the evaluation of coronary artery disease (CAD) or suspected CAD. The device is intended to be used as an aid to the existing standard of care and does not replace existing software applications that physicians use. The Coronary Plaque Module can be integrated into an image viewing software intended for visualization of cardiac images, such as Circle's FDA-cleared cvi42 Software Application. The Coronary Plaque Module does not interface directly with any data collection equipment, and its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on-screen, can be sent to report or saved for future review.

The Coronary Plaque Module consists of multiplanar reconstruction (MPR) views, curved planar reformation (CPR) and straightened views, and 3D rendering of the original CT data. The Module displays three orthogonal MPR views that the user can freely adjust to any position and orientation. Lines and regions of interest (ROIs) can be manually drawn on these MPR images for quantitative measurements.

The Coronary Plaque Module implements an Artificial Intelligence/Machine Learning (AI/ML) algorithm to detect lumen and vessel wall structures. Further, the module implements post-processing methods to convert coronary artery lumen and vessel wall structures to editable surfaces and detect the presence and type of coronary plaque in the region between the lumen and vessel wall. All surfaces generated by the system are editable and users are advised to verify and correct any errors.

The device allows users to perform the measurements listed in Table 1.

Here's a summary of the acceptance criteria and study details based on the provided FDA 510(k) Clearance Letter for the cvi42 Coronary Plaque Software Application:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The acceptance criteria for each endpoint are not explicitly numerical in the provided text. They are inferred to be "met Circle's pre-defined acceptance criteria" and are presented here as the numeric value reported, implying that the reported value met or exceeded the internal acceptance threshold for a 'Pass'.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "All data used for validation were not used during the development of the ML algorithms" and "Image information for all samples was anonymized and limited to ePHI-free DICOM headers." However, the specific number of cases or images in the test set is not provided.
• Data Provenance: Sourced from multiple sites, with 100% of the data sampled from US sources. The data consisted of images acquired from major vendors of CT imaging devices.

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

• Number of Experts: Three expert annotators were used.
• Qualifications of Experts: Not explicitly stated beyond "expert annotators." The document implies they are experts in coronary vessel and lumen wall segmentation within cardiac CT images.

4. Adjudication Method for the Test Set

The ground truth was established "from three expert annotators." While it doesn't explicitly state "2+1" or "3+1", the use of three annotators suggests a consensus-based adjudication, likely majority vote (e.g., if two out of three agreed, that constituted the ground truth, or a more complex consensus process). The specific method is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No. The document states, "No clinical studies were necessary to support substantial equivalence." The evaluation was primarily based on the performance of the ML algorithms against a reference standard established by experts, not on how human readers improved their performance with AI assistance.

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

Yes. The performance evaluation focused on the "performance of the ML-based coronary vessel and lumen wall segmentation algorithm... evaluated against pre-defined acceptance criteria and compared to a reference standard established from three expert annotators." This indicates a standalone performance assessment of the algorithm's output. The device is also described as having "semi-automated machine learning algorithms", implying the user can verify and correct.

7. The Type of Ground Truth Used

Expert Consensus. The ground truth was established "from three expert annotators," indicating that human experts' annotations formed the reference standard against which the algorithm's performance was measured.

8. The Sample Size for the Training Set

Not explicitly stated. The document mentions the ML algorithms "have been trained and tested on images acquired from major vendors of CT imaging devices," but it does not provide the specific sample size for the training set. It only clarifies that the validation data was not used for training.

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

Not explicitly stated. The document describes how the ground truth for the validation/test set was established (three expert annotators). It does not provide details on how the ground truth for the training set was generated.

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Cocoon Solo (DX-7020s) is a portable x-ray system indicated for taking diagnostic dental x-rays for both pediatric and adult patients.

Cocoon Solo (DX-7020s) is a handheld, portable X-ray device designed for dental radiographic examination and diagnosis for pediatric and adult patients by exposing a X-ray image receptor to ionizing radiation.

The X-ray tube is located inside the device body to be used with conventional film (F-speed or greater film), PSP (Phosphor plates), or digital X-ray sensors.

The image detectors (an integral part of a fully-functional diagnostic x-ray system) are not part of the submission.

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