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

lt enables:

· Importing cardiac MR & CT Images in DICOM format.

• Supporting clinical diagnostics by qualitative analysis of cardiac MR & CT images using display functionality such as panning, windowing, zooming, navigation through series/slices and phases, 3D reconstruction of images including multiplanar reconstructions of the images.

• Supporting clinical diagnostics by quantitative measurement of the heart and adjacent vessels in cardiac MR & CT images, specifically signal intensity, distance, area, volume, and mass.

• Supporting clinical diagnostics by using area and volume for measuring cardiac function and derived parameters cardiac output and cardiac index in long axis and short axis cardiac MR & CT images.

• Flow quantifications based on velocity encoded cardiac MR images (including two and four dimensional flow analysis).

• Strain analysis of cardiac MR images by providing measurements of 2D LV myocardial function (displacement, velocity, strain, strain rate, time to peak, and torsion).

· Supporting clinical diagnostics of cardiac CT images including quantitative measurements of calcified plaques in the coronary arteries (calcium scoring), specifically Agatston and volume and mass calcium scores, visualization and quantitative measurement of heart structures including coronaries, femoral, aortic, and mitral valves.

· Evaluating CT and MR images of blood vessels. Combining digital image processing and visualization tools such as multiplanar reconstruction (MPR), thin/thick maximum intensity projection (MIP), inverted MIP thin/thick, volume rendering technique (VRT), curved planar reformation (CPR), processing tools such as bone removal (based on both single energy and dual energy) table removal and evaluation tools (vessel centerline calculation, lumen calculation, stenosis calculation) and reporting tools (lesion location, lesion characteristics) and key images. The software package is designed to support the physician in confirming the presence of physician identified lesion in blood vessels and evaluation, documentation and follow up of any such lesions.

cvi42 shall be used by qualified medical professionals, experienced in examining and evaluating cardiovascular MR or CT images, for the purpose of obtaining diagnostic information as part of a comprehensive diagnostic decision-making process. cvi42 is a software application that can be used as a stand-alone product or in a networked environment.

The target population for cvi42 and its manual workflows is not restricted; however, cvi42's semiautomated machine learning algorithms, included in the MR Function and CORE CT modules, are intended for an adult population. Further, image acquisition by a cardiac MR or CT scanner may limit the use of the software for certain sectors of the general public.

cvi42 shall not be used to view or analyze images of any part of the body except the cardiac images acquired from a cardiovascular magnetic resonance or computed tomography scanner.

cvi42 Software Application ("cvi42") is a software as a medical device (SaMD) that is intended for evaluating CT and MR images of the cardiovascular system. Combining digital image processing, visualization, quantification, and reporting tools, cvi42 is designed to support physicians in the evaluation and analysis of cardiovascular imaqing studies.

cvi42 uses machine learning techniques to aid in semi-automatic contouring of regions of interest in cardiac MR or CT images.

The data used to train these machine learning algorithms were sourced from multiple clinical sites from urban centers and from different countries. When selecting data for training, the importance of model generalization was considered and data was selected such that a good distribution of patient demographics, scanner, and image parameters were represented. The separation into training versus validation datasets is made on the study level to ensure no overlap between the two sets. As such, different scans from the same study were not split between the training and validation datasets. None of the cases used for model validation were used for training the machine learning models.

cvi42 has a graphical user interface which allows users to analyze cardiac MR & CT images qualitatively and quantitatively.

cvi42 accepts uploaded data files previously acquired by MR or CT scanners or other data collection equipment but does not interface directly with such equipment. Its functionality is independent of the type of vender acquisition equipment. The analysis results are available onscreen and can be saved with the software for future review.

Here's a breakdown of the acceptance criteria and study details for the cvi42 Software Application, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

For cvi42 Auto (MR-CMR Function, CORE CT Coronary, and CORE CT-Calcium):

For CORE CT (CT Function Module):

2. Sample Size for the Test Set and Data Provenance

For cvi42 Auto (MR-CMR Function, CORE CT Coronary, and CORE CT-Calcium):

• Sample Size: n = 235 anonymized patient images acquired from major vendors of MR and CT imaging devices.
  • 70 samples for Coronary Analysis
  • 102 samples for Calcium analysis
  • 63 samples for SAX Function contouring
  • 63 samples for each of 2-CV, 3-CV, and 4CV LAX function contouring
  • 252 samples for Function Classification
• Data Provenance: Images were acquired from major vendors of MR and CT imaging devices. At least 50% of the data came from a U.S. population. The document does not specify if the data was retrospective or prospective, but the phrasing "were used for the validation" implies retrospective use of existing data.

For CORE CT (CT Function Module):

• Sample Size: Not explicitly stated, but the validation data was sourced from 9 different sites.
• Data Provenance: Sourced from 9 different sites, with 90% of the data sampled from US sources. The document does not specify if the data was retrospective or prospective.

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

For CORE CT (CT Function Module):

• Number of Experts: Three expert readers.
• Qualifications: "Expert readers" – specific qualifications (e.g., years of experience, board certification) are not detailed in the provided text.

For cvi42 Auto (MR-CMR Function, CORE CT Coronary, and CORE CT-Calcium), the document does not explicitly state the number of experts used to establish ground truth for the test set. It does mention expert readers for the comparison in the CORE CT section.

4. Adjudication Method for the Test Set

For CORE CT (CT Function Module):

• The "reference standard" was "established from three expert readers." The specific adjudication method (e.g., majority vote, specific consensus process) is not detailed, but it implies a consensus or agreement among these three experts.

For cvi42 Auto (MR-CMR Function, CORE CT Coronary, and CORE CT-Calcium):

• The document does not explicitly state the adjudication method for establishing ground truth for these modules.

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

• No, an MRMC comparative effectiveness study was not explicitly stated to have been done to measure human reader improvement with AI vs. without AI assistance. The performance tests described are primarily focused on the standalone performance of the AI algorithms (Machine Learning Derived Outputs) compared to a ground truth or a reference standard established by experts.

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

• Yes, standalone performance was assessed. The sections titled "Validation of Machine Learning Derived Outputs" and "CORE CT: CT Function" describe the evaluation of the algorithms' performance (e.g., classification accuracy, MAE, Dice coefficient, HD, EF bias) against pre-defined acceptance criteria and a reference standard made by experts, without human-in-the-loop assistance for the AI's output generation. This is a standalone assessment of the algorithms.

7. The Type of Ground Truth Used

• Expert Consensus: For the CORE CT module, the ground truth (reference standard) used for evaluation was established by "three expert readers." This implies an expert consensus or expert-derived ground truth.
• For other modules (cvi42 Auto), the document states that performance was evaluated against "pre-defined acceptance criteria" but does not explicitly describe how the ground truth for those criteria was established, though it likely involved expert annotations or established clinical metrics.

8. The Sample Size for the Training Set

• The document states: "The data used to train these machine learning algorithms were sourced from multiple clinical sites from urban centers and from different countries." However, the specific sample size for the training set is not provided for any of the modules. It only mentions that the data was selected for good distribution of patient demographics, scanner, and image parameters.

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

• The document does not explicitly describe how the ground truth for the training set was established. It only states that the training data "were sourced from multiple clinical sites from urban centers and from different countries." It also notes that "the separation into training versus validation datasets is made on the study level to ensure no overlap between the two sets." This suggests that the training data would have had associated ground truth data (e.g., expert annotations, clinical measurements) to enable supervised learning, but the method of establishing that ground truth is not detailed.

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cvi42 Auto is intended to be used for viewing, post-processing, qualitative evaluation of cardiovasular magnetic resonance (MR) and 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 assessment of cardiac images and quantitative measurements of the heart and adjacent vessels: perform calcium scoring: and to confirm the presence of physician-identified lesion in blood vessels.

The target population for cvi42 Auto's manual workflows is not restricted; however, cvi42 Auto's semi-automated machine learning algorithms are intended for an adult population.

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

cvi42 Auto is a software as a medical device (SaMD) that is intended for evaluating CT and MR images of the cardiovascular system. Combining digital image processing, visualization, guantification, and reporting tools, cvi42 Auto device is designed to support the physician in confirming the presence or absence of physician-identified lesion in blood vessels and evaluation, documentation and follow up of any such lesions.

cvi42 Auto uses machine learning techniques to aid in semi-automatic contouring of regions of interest of cardiac magnetic resonance (MR) or computed tomography (CT) images as follows:

• 1. Cardiac Function: semi-automatic contouring of the four heart chambers (including left ventricle, left atrium, right ventricle, right atrium) in MR images.
• 2. Calcium Assessment: using pixel intensity technique, identify calcified plaque in major coronary arteries in non-contrast enhanced CT images.
• 3. Coronary Analysis: semi-automatic placement of centerline in coronary vessels to visualize the coronary arteries and assess stenosis in non-contrast enhanced CT images.

The data used to train these machine learning algorithms were sourced from multiple clinical sites from urban centers and from different countries. When selecting data for training, the importance of model generalization was considered and data was selected such that a good distribution of patient demographics, scanner, and image parameters were represented. The separation into training versus validation datasets is made on the study level to ensure no overlap between the two sets. As such, different scans from the same study were not split between the training and validation datasets. None of the cases used for model validation were used for training the machine learning models.

cvi42 Auto software has a graphical user interface which allows users to analyze cardiac images qualitatively and quantitatively for volume/mass, function and signal intensity changes including a reporting function.

The device can be integrated into a hospital, private practice environment, or medical research institution and provides clinical diagnosis decision support tools for the cardiovascular MR and CT technique.

Additionally, the software is designed to generate 3D view of the heart in CT images for qualitative assessment of the coronary artery. No quantitative assessment can be made from the 3D image.

The software does not interface directly with any data collection equipment; instead, the software uploads data files previously generated by such equipment. Its functionality is independent of the type of vendor acquisition equipment. The analysis results are available on-screen and can be saved within the software for future review.

The provided text describes the acceptance criteria and the study that proves the cvi42 Auto Imaging Software Application meets these criteria.

Here's an organized breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are described as pre-defined performance thresholds for the machine learning models. The reported performance is the achieved accuracy or error rate.

Note: The document states that "All performance testing results met Circle's pre-defined acceptance criteria." While specific numerical "acceptance criteria" are not given for all metrics, the reported performance ranges are implicitly within the accepted thresholds.

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

• Total anonymized patient images for validation: n = 235
• Breakdown by analysis type (note: total is >235 as some analyses might use overlapping sets or different views from the same patient):
  • Coronary Analysis: 70 samples
  • Calcium Analysis: 102 samples
  • SAX Function Contouring: 63 samples
  • 2-CV LAX Function Contouring: 63 samples
  • 3-CV LAX Function Contouring: 63 samples
  • 4-CV LAX Function Contouring: 63 samples
  • Function Classification: 252 samples
• Data Provenance: "Across all MR and CT machine manufacturers." "At least 50% of the data came from a U.S. population." The data for validation was explicitly stated to not have been used during the development of the training algorithms, indicating a distinct test set. The document implies a retrospective collection of anonymized patient images for validation.

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

The document does not specify the number or qualifications of experts used to establish the ground truth for the test set. It only mentions that the device is "intended to be used by qualified medical professionals, experienced in examining and evaluating cardiovascular MR or CT images, for the purpose of obtaining diagnostic information as part of a comprehensive decision-making process." This likely refers to the users of the device, not necessarily the ground truth adjudicators for the validation study.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1) for establishing the ground truth on the test set. The results are presented as direct performance metrics against an assumed ground truth, but how that ground truth was derived (e.g., single expert, consensus) is not detailed.

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

The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The performance data presented focuses on the algorithm's standalone performance or its semi-automated function.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone study was done. The performance data provided (e.g., classification accuracies, MAE, centerline performance, mask performance) describes the performance of the machine learning algorithms themselves (the "semi-automated machine learning algorithms"), rather than human-AI team performance. The mention of "semi-automatic contouring" and "semi-automatic placement of centerline" implies that the AI assists, but the reported metrics appear to be related to the accuracy of the algorithm's output.

7. Type of Ground Truth Used

The type of ground truth used is not explicitly stated in detail for the validation set. Given the context of "semi-automatic contouring" and "classification accuracy," it is highly probable that the ground truth for contouring (e.g., for heart chambers) would have been established by expert manual segmentation, and for classifications (e.g., calcium presence), it would be based on expert review or established clinical criteria. However, explicit details like "expert consensus" or "pathology" are not mentioned.

8. The Sample Size for the Training Set

The document states: "The data used to train these machine learning algorithms were sourced from multiple clinical sites from urban centers and from different countries." However, the specific sample size (number of images or patients) used for the training set is not provided in the given text.

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

The document mentions that training data was "sourced from multiple clinical sites" and that "the importance of model generalization was considered and data was selected such that a good distribution of patient demographics, scanner, and image parameters were represented." It also differentiates between training and validation datasets by ensuring "no overlap between the two sets."
While it broadly states that data was selected considering generalization, it does not explicitly detail how the ground truth for the training set was established (e.g., expert annotation, clinical reports, etc.).

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cvi42 vascular analysis add-on is an image analysis software package add-on for evaluating CT and MR images of blood vessels. Combining digital image processing and visualization tools such as multiplaner reconstruction (MPR), thin/think maximum intensity projection (MIP) thin and think, inverted MIP thin and think, volume rendering technique (VRT), curved planner reformation, processing tools such as bone removal (based on both single energy and dual energy) table removal and evaluation tools (vessel centerline calculation, lumen calculation, stenosis calculation) and reporting tools (lesion location, lesion characteristics) and key images), the software package is designed to support the physician in conforming the presence or absence of physician identified lesion in blood vessels and evaluation, documentation and follow up of any such lesions.

It shall be used by qualified medical professionals, experienced in examining and evaluating cardiovascular CT or MR images, for the purpose of obtaining diagnostic information as part of a comprehensive diagnostic decision-making process. cvi42 is a software application that can be used as a stand-alone product or in a networked environment.

The target population for the cvi42 is not restricted, however the image acquisition by a cardiac CT or MR scanner may limit the use of the device for certain sectors of the general public.

cvi42 vascular add-on is software application for evaluating cardiovascular images in a DICOM Standard format. The software can be used as a stand-alone product that can be integrated into a hospital or private practice environment. cvi42 has a graphical user interface which allows users to qualitatively and quantitatively analyze cardiac CT & MR images.

The provided text describes the cvi42 device, an image analysis software for CT and MR images of blood vessels, but it does not contain the detailed acceptance criteria or the study results that specifically prove the device meets those criteria.

The document states that "cvi42 have been tested according to the specifications that are documented in a Master Software Test Plan," and that "The successful non-clinical testing demonstrates the safety and effectiveness of the cvi42 when used for the defined indications for use and demonstrates that the device for which the 510(k) is submitted performs as well as or better than the legally marketed predicate device." However, it does not provide the specifics of these tests, acceptance criteria, or performance metrics.

Therefore, I cannot fill out the requested table or answer the specific questions about the study design, sample sizes, ground truth establishment, or expert involvement based on the provided text.

The information related to the predicate device "iNtuition (K121916)" is largely for functional comparison, showing both devices have similar capabilities, but it does not present performance data for either the predicate or the cvi42 device.

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