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AI-CVD® is an opportunistic AI-powered quantitative imaging tool that provides automated CT-derived anatomical and density-based measurements for clinician review. The device does not provide diagnostic interpretation or risk prediction. It is solely intended to aid physicians and other healthcare providers in determining whether additional diagnostic tests are appropriate for implementing preventive healthcare plans. AI-CVD® has a modular structure where each module is intended to report quantitative imaging measurements for each specific component of the CT scan. AI-CVD® quantitative imaging measurement modules include coronary artery calcium (CAC) score, aortic wall calcium score, aortic valve calcium score, mitral valve calcium score, cardiac chambers volumetry, epicardial fat volumetry, aorta and pulmonary artery sizing, lung density, liver density, bone mineral density, and muscle & fat composition.

Using AI-CVD® quantitative imaging measurements and their clinical evaluation, healthcare providers can investigate patients who are unaware of their risk of coronary heart disease, heart failure, atrial fibrillation, stroke, osteoporosis, liver steatosis, diabetes, and other adverse health conditions that may warrant additional risk assessment, monitoring or follow-up. AI-CVD® quantitative imaging measurements are to be reviewed by radiologists or other medical professionals and should only be used by healthcare providers in conjunction with clinical evaluation.

AI-CVD® is not intended to rule out the risk of cardiovascular diseases. AI-CVD® opportunistic screening software can be applied to non-contrast thoracic CT scans such as those obtained for CAC scans, lung cancer screening scans, and other chest diagnostic CT scans. Similarly, AI-CVD® opportunistic screening software can be applied to contrast-enhanced CT scans such as coronary CT angiography (CCTA) and CT pulmonary angiography (CTPA) scans. AI-CVD® opportunistic bone density module and liver density module can be applied to CT scans of the abdomen and pelvis. All volumetric quantitative imaging measurements from the AI-CVD® opportunistic screening software are adjusted by body surface area (BSA) and reported both in cubic centimeter volume (cc) and percentiles by gender reference data from people who participated in the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham Heart Study (FHS). Except for coronary artery calcium scoring, other AI-CVD® modules should not be ordered as a standalone CT scan but instead should be used as an opportunistic add-on to existing and new CT scans.

AI-CVD® is an opportunistic AI-powered modular tool that provides automated quantitative imaging reports on CT scans and outputs the following measurements:

• Coronary Artery Calcium Score
• Aortic Wall and Valves Calcium Scores
• Mitral Valve Calcium Score
• Cardiac Chambers Volume
• Epicardial Fat Volume
• Aorta and Main Pulmonary Artery Volume and Diameters
• Liver Attenuation Index
• Lung Attenuation Index
• Muscle and Visceral Fat
• Bone Mineral Density

The above quantitative imaging measurements enable care providers to take necessary actions to prevent adverse health outcomes.

AI-CVD® modules are installed by trained personnel only. AI-CVD® is executed via parent software which provides the necessary inputs and receives the outputs. The software itself does not offer user controls or access.

AI-CVD® reads a CT scan (in DICOM format) and extracts scan specific information like acquisition time, pixel size, scanner type, etc. AI-CVD® uses trained AI models that automatically segment and report quantitative imaging measurements specific to each AI-CVD® module. The output of each AI-CVD® module is inputted into the parent software which exports the results for review and confirmation by a human expert.

AI-CVD® is a post-processing tool that works on existing and new CT scans.

AI-CVD® passes if the human expert approves the segmentation highlighted by the AI-CVD® module is correctly placed on the target anatomical region. For example, Software passes if the human expert sees the AI-CVD® cardiac chamber volumetry module highlighted the heart anatomy.

AI-CVD® fails if the human expert sees the segmentation highlighted by the AI-CVD® module is not correctly placed on the target anatomical region. For example, Software fails if the human expert sees the AI-CVD® cardiac chamber volumetry module highlighted the lungs anatomy or a portion of the sternum or any adjacent organs. Furthermore, Software fails if the human expert sees that the quality of the CT scan is compromised by image artifacts, severe motion, or excessive noise.

The user cannot change or edit the segmentation or results of the device. The user must accept or reject the segmentation where the AI-CVD® quantitative imaging measurements are performed.

AI-CVD® is an AI-powered post-processing tool that works on non-contrast and contrast-enhanced CT scans of chest and abdomen.

AI-CVD® is a multi-module deep learning-based software platform developed to automatically segment and quantify a broad range of cardiovascular, pulmonary, musculoskeletal, and metabolic biomarkers from standard chest or whole-body CT scans. AI-CVD® system builds upon the open-source TotalSegmentator as its foundational segmentation framework, incorporating additional supervised learning and model training layers specific to each module's clinical task.

The provided FDA 510(k) Clearance Letter for AI-CVD® outlines several modules, each with its own evaluation. However, the document does not provide a single, comprehensive table of acceptance criteria with reported device performance for all modules. Instead, it describes clinical validation studies and agreement analyses, generally stating "acceptable bias and reproducibility" or "acceptable agreement and reproducibility" without specific numerical thresholds or metrics. Similarly, detailed information on sample sizes, ground truth establishment methods (beyond general "manual reference standards" or "human expert knowledge"), and expert qualifications is quite limited for most modules.

Here's an attempt to extract and synthesize the information based on the provided text, recognizing the gaps:

Acceptance Criteria and Study Details for AI-CVD®

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria for each module. Instead, it describes performance in terms of agreement with manual measurements or gold standard references, generally stating "acceptable bias and reproducibility" or "comparable performance." The table below summarizes what is reported.

2. Sample Size and Data Provenance for the Test Set

• Coronary Artery Calcium (CAC) Score:
  • Sample Size: 913 consecutive coronary calcium screening CT scans.
  • Data Provenance: "Real-world" data acquired across three community imaging centers. This suggests a retrospective collection from a U.S. or similar healthcare system, though the specific country of origin is not explicitly stated. The term "consecutive" implies that selection bias was minimized.
• Other Modules (Aortic Wall/Valve, Mitral Valve, Epicardial Fat, Aorta/Pulmonary Artery, Liver, Lung, Muscle/Visceral Fat):
  • The document refers to "agreement analyses" and "agreement studies" but does not specify the sample size for the test sets used for these individual modules.
  • Data Provenance: The document generally states that "clinical validation studies were performed based upon retrospective analyses of AI-CVD® measurements performed on large population cohorts such as the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham Heart Study (FHS)." It is unclear if these cohorts were solely used for retrospective analysis, or if the "real-world" data mentioned for CAC was also included for other modules. MESA and FHS are prospective, longitudinal studies conducted primarily in the U.S.

3. Number of Experts and Qualifications for Ground Truth

• Coronary Artery Calcium (CAC) Score:
  • Number of Experts: Unspecified, referred to as "expert manual measurements."
  • Qualifications: Unspecified, but implied to be human experts capable of performing manual Agatston scoring.
• Other Modules:
  • Number of Experts: Unspecified, generally referred to as "manual reference standards" or "manual measurements."
  • Qualifications: Unspecified.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1) for establishing ground truth on the test set. It mentions "expert manual measurements" or "manual reference standards," suggesting that the ground truth was established by human experts, but the process of resolving discrepancies among multiple experts (if any were used) is not detailed.

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

• Was an MRMC study done? No, the document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The performance data presented focuses on the standalone AI performance compared to human expert measurements.

• Effect Size of Human Reader Improvement: Not applicable, as an MRMC study was not described.

6. Standalone (Algorithm Only) Performance Study

• Was a standalone study done? Yes, the described performance evaluations for all modules (where new performance data was presented) are standalone performance studies. The studies compare the AI-CVD® algorithm's output directly against manual measurements or established reference standards.

7. Type of Ground Truth Used

• Coronary Artery Calcium Score: Expert manual measurements (Agatston scores).
• Aortic Wall and Aortic Valve Calcium Scores: Manual reference standards.
• Mitral Valve Calcium Score: Manual measurements.
• Epicardial Fat Volume: Manual measurements.
• Aorta and Main Pulmonary Artery Volume and Diameters: Manual reference measurements.
• Liver Attenuation Index: (Implicitly) Manual reference measurements or established methods for hepatic attenuation.
• Lung Attenuation Index: (Implicitly) Manual reference measurements or established methods for lung density.
• Muscle and Visceral Fat: (Implicitly) Manual reference measurements.
• Cardiac Chambers Volume & Bone Mineral Density: Leveraged previously cleared predicate devices, suggesting the ground truth for their original clearance would apply.

8. Sample Size for the Training Set

The document provides information on the foundational segmentation framework (TotalSegmentator) and hints at customization for AI-CVD® modules:

• TotalSegmentator (Foundational Framework):
  • General anatomical segmentation: 1,139 total body CT cases.
  • High-resolution cardiac structure segmentation: 447 coronary CT angiography (CCTA) scans.
• AI-CVD® Custom Datasets: The document states that "Custom datasets were constructed for coronary artery calcium scoring, aortic and valvular calcifications, cardiac chamber volumetry, epicardial and visceral fat quantification, bone mineral density assessment, liver fat estimation, muscle mass and quality, and lung attenuation analysis." However, it does not provide the specific sample sizes for these custom training datasets for each AI-CVD® module.

9. How Ground Truth for the Training Set Was Established

• TotalSegmentator (Foundational Framework): The architecture utilizes nnU-Net, which was trained on the described CT cases. Implicitly, these cases would have had expert-derived ground truth segmentations for training the neural network.
• AI-CVD® Custom Datasets: "For each module, iterative model enhancement was applied: human reviewers evaluated model-generated segmentations and corrected any inaccuracies, and these corrections were looped back into the training process to improve performance and generalizability." This indicates that human experts established and refined the ground truth by reviewing and correcting model-generated segmentations, which were then used for retraining. The qualifications of these "human reviewers" are not specified.

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BriefCase-Quantification is a radiological image management and processing system software indicated for use in the analysis of CT exams with contrast, that include the abdominal aorta, in adults or transitional adolescents aged 18 and older.

The device is intended to assist appropriately trained medical specialists by providing the user with the maximum abdominal aortic axial diameter measurement of cases that include the abdominal aorta (M-AbdAo), BriefCase-Quantification is indicated to evaluate normal and aneurysmal abdominal aortas and is not intended to evaluate postoperative aortas.

The BriefCase-Quantification results are not intended to be used on a stand-alone basis for clinical decision-making or otherwise preclude clinical assessment of cases. These measurements are unofficial, are not final, and are subject to change after review by a radiologist. For final clinically approved measurements, please refer to the official radiology report. Clinicians are responsible for viewing full images per the standard of care.

BriefCase-Quantification is a radiological medical image management and processing device. The software consists of a single module based on an algorithm programmed component and is intended to run on a linux-based server in a cloud environment.

The BriefCase-Quantification receives filtered DICOM Images, and processes them chronologically by running the algorithm on relevant series to measure the maximum abdominal aortic diameter. Following the Al processing, the output of the algorithm analysis is transferred to an image review software (desktop application), and forwarded to user review in the PACS.

The BriefCase-Quantification produces a preview image annotated with the maximum axial diameter measurement. The diameter marking is not intended to be a final output, but serves the purpose of visualization and measurement. The original, unmarked series remains available in the PACS as well. The preview image presents an unofficial and not final measurement, and the user is instructed to review the full image and any other clinical information before making a clinical decision. The image includes a disclaimer: "Not for diagnostic use. The measurement is unofficial, not final, and must be reviewed by a radiologist."

BriefCase-Quantification is not intended to evaluate post-operative aortas.

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: 160 cases
• Data provenance: Retrospective, from 6 US-based clinical sites (both academic and community centers). The cases were distinct in time and/or center from the training data.

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

• Number of experts: 3
• Qualifications of experts: US board-certified radiologists.

4. Adjudication method for the test set

The document does not explicitly state the adjudication method (e.g., 2+1, 3+1). It only mentions that the ground truth was "determined by three US board-certified radiologists." This implies a consensus-based approach, but the specific decision rule is not detailed.

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, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader improvement with AI assistance versus without AI assistance was not done. This study focused on the standalone performance of the algorithm against a ground truth established by experts.

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

Yes, a standalone performance study of the algorithm was done. The "Primary Endpoint" section details the algorithm's performance (mean absolute error) compared to the ground truth.

7. The type of ground truth used

Expert consensus was used as the ground truth. The ground truth measurements were "determined by three US board-certified radiologists."

8. The sample size for the training set

The document does not specify the sample size for the training set. It only states that the cases collected for the pivotal dataset (test set) were "distinct in time and/or center from the cases used to train the algorithm."

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 mentions that the cases were "distinct in time and/or center from the cases used to train the algorithm," implying that training data also had a ground truth, likely established by similar expert review, but this is not detailed.

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CoLumbo is an image post-processing and measurement software tool that provides quantitative spine measurements from previously-acquired DICOM lumbar spine Magnetic Resonance (MR) images for users' review, analysis, and interpretation. It provides the following functionality to assist users in visualizing, measuring and documenting out-of-range measurements:

• . Feature segmentation;
• . Feature measurement;
• . Threshold-based labeling of out-of-range measurement; and
• . Export of measurement results to a written report for user's revise and approval.

CoLumbo does not produce or recommend any type of medical diagnosis or treatment. Instead, it simply helps users to more easily identify and classify features in lumbar MR images and compile a report. The user is responsible for confirming/modifying settings. reviewing and verifying the software-generated measurements, inspecting out-of-range measurements, and approving draft report content using their medical judgment and discretion.

The device is intended to be used only by hospitals and other medical institutions.

Only DICOM images of MRI acquired from lumbar spine exams of patients aged 18 and above are considered to be valid input. CoLumbo does not support DICOM images of patients that are prognant, undergo MRI scan with contrast media, or have post-operational complications, scoliosis, tumors, infections, fractures.

CoLumbo is a medical device (software) for viewing and interpreting magnetic resonance imaging (MRI) of the lumbar spine. The software is a quantitative imaging tool that assists radiologists and neuro- and spine surgeons ("users") to identify and measure lumbar spine features in medical images and record their observations in a report. The users then confirm whether the out-of-range measurements represent any true abnormality versus a spurious finding, such as an artifact or normal variation of the anatomy. The segmentation and measurements are classified using "modifiers" based on rule-based algorithms and thresholds set by each software user and stored in the user's individualized software settings. The user also identifies and classifies any other observations that the software may not annotate.

The purpose of CoLumbo is to provides information regarding common spine measurements confirmed by the user and the pre-determined thresholds confirmed or defined by the user. Every feature annotated by the software, based on the user-defined settings, must be reviewed and affirmed by the radiologist before the measurements of these features can be stored and reported. The software initiates adjustable measurements resulting from semi-automatic segmentation. If the user rejects a measurement the corresponding segmentation is rejected too. Segmentations are not intended to be a final output but serve the purpose of visualization and calculating measurements. The device outputs are intended to be a starting point for a clinical workflow and should not be interpreted or used as a diagnosis. The user is responsible for confirming segmentation and all measurement outputs. The output is an aid to the clinical workflow of measuring patient anatomy and should not be misused as a diagnosis tool.

User-confirmed defined settings control the sensitivity of the software for labelling measurements in an image. The user (not the software) controls the threshold for identifying out-of-range measurements, and, in every case once an out-of-range measurement is identified, the user must confirm or reject its presence. The software facilitates this process by annotating or drawing contours (segmentations) around features of the relevant anatomy and displaying measurements based on these contours. The user maintains control of the process by inspecting the segmentation, measurements and annotations upon which the measurements are based. The user may also examine other features of the imaging not annotated by the software to form a complete impression and diagnostic judgment of the overall state of disease, disorder, or trauma.

Here's a breakdown of the acceptance criteria and the study that proves CoLumbo meets them, based on the provided FDA submission:

1. Acceptance Criteria and Reported Device Performance

Primary Endpoint (Measurement Accuracy):

• Acceptance Criteria: The maximum Mean Absolute Error (MAE), defined as the upper limit of the 95% confidence interval for MAE, is below a predetermined allowable error limit (MAE_Limit) for each measurement listed.
• Reported Performance: All primary endpoints were met.

Secondary Endpoints (Measurement and Segmentation Accuracy):

• Acceptance Criteria (Measurement): The maximum Mean Absolute Error, defined as the upper limit of the 95% confidence interval for MAE, is below a predetermined allowable error limit (MAE_Limit) for each measurement listed.
• Acceptance Criteria (Segmentation): The minimum Mean Dice Coefficient (MDC), defined as the lower limit of the 95% confidence interval for MDC, is above a predetermined allowable limit (MDC_Limit) for each segmentation listed.
• Reported Performance: All secondary endpoints on measurement and segmentation were met.

Secondary Measurement Performance:

Secondary Segmentation Performance:

2. Sample Size and Data Provenance

• Test Set Sample Size: 101 MR image studies from 101 patients.
• Data Provenance:
  • Country of Origin: Collected from seven (7) sites across the U.S.
  • Retrospective/Prospective: The document does not explicitly state whether the data was retrospective or prospective, but the phrasing "collected from seven (7) sites across the U.S." typically implies retrospective collection for this type of validation.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Three (3) U.S. radiologists.
• Qualifications: The document states they were "U.S. radiologists" but does not provide details on their years of experience, subspecialty, or specific certifications.

4. Adjudication Method for the Test Set

• Ground Truth Method: For segmentations, the per-pixel majority opinion of the three radiologists established the ground truth. For measurements, the median of the three radiologists' measurements established the ground truth. This is a form of multi-reader consensus.

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

• Was it done? No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly reported. The study conducted was a "standalone software performance assessment study," meaning it evaluated the algorithm's performance against ground truth without human readers in the loop.
• Effect Size: N/A, as an MRMC study comparing human readers with and without AI assistance was not performed.

6. Standalone (Algorithm Only) Performance Study

• Was it done? Yes. A standalone software performance assessment study was conducted.
• Details: The study "compared the CoLumbo software outputs without any editing by a radiologist to the ground truth defined by 3 radiologists on segmentations and measurements."

7. Type of Ground Truth Used

• Ground Truth Type: Expert consensus.
  • For segmentations: Per-pixel majority opinion of three radiologists using a specialized pixel labeling tool.
  • For measurements: Median of three radiologists' measurements using a commercial software tool.

8. Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated in the provided text. The document only mentions that the "training and testing data used during the algorithm development, as well as validation data used in the U.S. standalone software performance assessment study were all independent data sets." It does not specify the size of the training set.

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

• Ground Truth Establishment for Training Set: Not explicitly stated. The document only mentions that the training data and validation data were independent. It does not detail the method by which ground truth was established for the training data used in algorithm development.

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AVIEW provides CT values for pulmonary tissue from CT thoracic and cardiac datasets. This software could be used to support the physician quantitatively in the diagnosis, follow up evaluation of CT lung tissue images by providing image segmentation of sub-structures in lung, lobe, airways and cardiac, registration and expiration which could analyze quantitative information such as air trapped index, and inspiration/ expiration ratio. And also, volumetric and structure analysis, density evaluation and reporting tools. AVIEW is also used to store, transfer, inquire and display CT data set on premise and as cloud environment as well to allow users to connect by various environment such as mobile devices and chrome browser. Characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the nodule, volume of the nodule, Mean HU(the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass(mass calculated from the CT pixel value), and volumetric measures(Solid major; length of the longest diameter measured in 3D for solid portion of the nodule, Solid 2nd Major: The longest diameter of the solid part, measured in sections perpendicular to the Major axis of the solid portion of the nodule), VDT (Volume doubling time), and Lung-RADS (classification proposed to aid with findings). The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, integrate with FDA certified Mevis CAD (Computer aided detection) (K043617). It also provides CAC analysis by segmentation of four main artery (right coronary artery, left main coronary, left anterior descending and left circumflex artery then extracts calcium on coronary artery to provide Agatston score, volume score and mass score by whole and each segmented artery type. Based on the score, provides CAC risk based on age and gender.

The AVIEW is a software product which can be installed on a PC. It shows images taken with the interface from various storage devices using DICOM 3.0 which is the digital image and communication standard in medicine. It also offers functions such as reading, manipulation, analyzing, post-processing, saving and sending images by using the software tools. And is intended for use as diagnostic patient imaging which is intended for the review and analysis of CT scanning. Provides following features as semi-automatic nodule management, maximal plane measure, 3D measures and columetric measures, automatic nodule detection by integration with 3rd party CAD. Also provides Brocks model which calculated the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported. It also automatically analyzes coronary artery calcification which support user to detect cardiovascular disease in early stage and reduce the burden of medical.

The provided FDA 510(k) summary for the AVIEW 2.0 device (K200714) primarily focuses on establishing substantial equivalence to a predicate device (AVIEW K171199, among others) rather than presenting a detailed clinical study demonstrating its performance against specific acceptance criteria.

However, based on the nonclinical performance testing section and the overall description, we can infer some aspects and present the available information regarding the device's capabilities and how it was tested. It is important to note that explicit acceptance criteria and detailed clinical study results are not fully elaborated in the provided text. The document states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly state "acceptance criteria" with numerical or performance targets. Instead, it describes general validation methods and "performance tests" that were conducted to ensure functionality and reliability. The "Reported Device Performance" here refers to the successful completion or validation of these functions.

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

The 510(k) summary does not specify the sample size used for the test set(s) used in the performance evaluation, nor does it detail the data provenance (e.g., country of origin, retrospective or prospective). It simply mentions "software verification and validation" and "nonclinical performance testing."

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not provide information on the number of experts used to establish ground truth or their specific qualifications for any of the nonclinical or performance tests mentioned. Given that no clinical study was performed, it is unlikely that medical experts were involved in establishing ground truth for a test set in the conventional sense for clinical performance.

4. Adjudication Method

No information is provided regarding an adjudication method. Since the document states no clinical study was conducted, adjudication by multiple experts would not have been applicable for a clinical performance evaluation.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not reported. The document explicitly states: "This Medical device is not new; therefore, a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device." Therefore, there is no mention of an effect size for human readers with or without AI assistance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop) performance evaluation was conducted, implied by the "Nonclinical Performance Testing" and "Software Verification and Validation" sections. The "Performance Test" section specifically lists several automatic and semi-automatic functions (e.g., "Auto Lung & Lobe Segmentation," "Airway Segmentation," "CAC Performance") that were tested for the device's inherent capability.

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for each specific performance test. For software components involving segmentation, it is common to use expert-annotated images (manual segmentation by experts) as ground truth for a quantitative comparison. For calculations like Agatston score, or VDT, the ground truth would likely be mathematical computations based on established formulas or reference standards applied to the segmented regions. However, this is inferred, not explicitly stated.

8. Sample Size for the Training Set

The document does not specify the sample size for any training set. It mentions the use of "deep-learning algorithms" for segmentation, which implies a training phase, but details about the training data are absent.

9. How Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for any training set was established. While deep learning is mentioned for certain segmentation tasks, the methodology for creating the labeled training data is not detailed.

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AI-Rad Companion (Musculoskeletal) is an image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of musculoskeletal disease. It provides the following functionality:

• Segmentation of vertebras
• Labelling of vertebras
• Measurements of heights in each vertebra and indication if they are critically different
• Measurement of mean Hounsfield value in volume of interest within vertebra.
  Only DICOM images of adult patients are considered to be valid input.

Al-Rad Companion (Musculoskeletal) is software-only image post-processing application that uses deep learning algorithms to post-process CT data of the thorax. Al-Rad Companion (Musculoskeletal) supports workflows for visualization and various measurements of musculoskeletal disease, including:

• Segmentation of vertebras ●
• Labelling of vertebras
• Measurements of heights in each vertebra and indication if they are critically ● different
• . Measurement of mean Hounsfield value in volume of interest within vertebra

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Note: The document explicitly states that the device's performance was "consistent for all critical subgroups, such as vendors or reconstruction parameters and patient age."

Study Details

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

• Sample Size: N=140
• Data Provenance: Retrospective performance study on chest CT data from multiple clinical sites across the United States and Europe.

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

• Number of Experts: Four radiologists.
• Qualifications: Not explicitly stated beyond "radiologists."

4. Adjudication method for the test set:

• Adjudication Method: Two readers per case, plus a third reader for adjudications (effectively a 2+1 method for cases with disagreement).

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:

• MRMC Study: No, a comparative effectiveness study with human readers (MRMC) was not explicitly described. The study focused on the device's standalone performance compared to human ground truth.

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

• Standalone Performance: Yes, the described study evaluated the "performance of the Al-Rad Companion (Musculoskeletal) device," which is an algorithm-only image processing software. The reported performance metrics (ratio of measurements within LoA) are for the device itself against the human-established ground truth.

7. The type of ground truth used:

• Type of Ground Truth: Expert consensus, established using manual vertebra height and density measurements performed by four radiologists with adjudication.

8. The sample size for the training set:

• Sample Size for Training Set: Not specified in the provided text. The document mentions that the device "uses the same deep learning technology as in the previously cleared reference device Siemens Al-Rad Companion (Cardiovascular) (K183268)," implying a pre-existing training process, but details of that training set are not in this document.

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

• Ground Truth for Training Set: Not specified in the provided text. Since the device leverages deep learning, it would have required a large, annotated dataset for training, but the specifics of how that training ground truth was established are not detailed in this document.

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