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AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT and MR pre-defined structures using deep-learning-based algorithms.

Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT.

The outputs of AI-Rad Companion Organs RT are intended to be used by trained medical professionals.

The software is not intended to automatically detect or contour lesions.

AI-Rad Companion Organs RT provides automatic segmentation of pre-defined structures such as Organs-at-risk (OAR) from CT or MR medical series, prior to dosimetry planning in radiation therapy. AI-Rad Companion Organs RT is not intended to be used as a standalone diagnostic device and is not a clinical decision-making software.

CT or MR series of images serve as input for AI-Rad Companion Organs RT and are acquired as part of a typical scanner acquisition. Once processed by the AI algorithms, generated contours in DICOMRTSTRUCT format are reviewed in a confirmation window, allowing clinical user to confirm or reject the contours before sending to the target system. Optionally, the user may select to directly transfer the contours to a configurable DICOM node (e.g., the Treatment Planning System (TPS), which is the standard location for the planning of radiation therapy).

AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept the automatically generated contours. Then the output of AI-Rad Companion Organs RT must be reviewed and, where necessary, edited with appropriate software before accepting generated contours as input to treatment planning steps. The output of AI-Rad Companion Organs RT is intended to be used by qualified medical professionals, who can perform a complementary manual editing of the contours or add any new contours in the TPS (or any other interactive contouring application supporting DICOM-RT objects) as part of the routine clinical workflow.

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

Acceptance Criteria and Device Performance Study for AI-Rad Companion Organs RT

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the AI-Rad Companion Organs RT device, particularly for the enhanced CT contouring algorithm, are based on comparing its performance to the predicate device and relevant literature/cleared devices. The primary metrics used are Dice coefficient and Absolute Symmetric Surface Distance (ASSD).

Table 3: Acceptance Criteria of AIRC Organs RT VA50

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AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT and MR predefined structures using deep-leaming-based algorithms.

Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT.

The output of AI-Rad Companion Organs RT are intended to be used by trained medical professionals.

The software is not intended to automatically detect or contour lesions.

AI-Rad Companion Organs RT provides automatic segmentation of pre-defined structures such as Organs-at-risk (OAR) from CT or MR medical series, prior to dosimetry planning in radiation therapy. AI-Rad Companion Organs RT is not intended to be used as a standalone diagnostic device and is not a clinical decision-making software.

CT or MR series of images serve as input for AI-Rad Companion Organs RT and are acquired as part of a typical scanner acquisition. Once processed by the AI algorithms, generated contours in DICOM-RTSTRUCT format are reviewed in a confirmation window, allowing clinical user to confirm or reject the contours before sending to the target system. Optionally, the user may select to directly transfer the contours to a configurable DICOM node (e.g., the TPS, which is the standard location for the planning of radiation therapy).

The output of AI-Rad Companion Organs RT must be reviewed and, where necessary, edited with appropriate software before accepting generated contours as input to treatment planning steps. The output of AI-Rad Companion Organs RT is intended to be used by qualified medical professionals. The qualified medical professional can perform a complementary manual editing of the contours or add any new contours in the TPS (or any other interactive contouring application supporting DICOM-RT objects) as part of the routine clinical workflow.

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary for AI-Rad Companion Organs RT:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria and reported performance are detailed for both MR and CT contouring algorithms.

MR Contouring Algorithm Performance

CT Contouring Algorithm Performance

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

• MR Contouring Algorithm Test Set:
  • Sample Size: N = 66
  • Data Provenance: Retrospective study, data from multiple clinical sites across North America & Europe. The document further breaks this down for different sequences:
    • T1 Dixon W: 30 datasets (USA: 15, EU: 15)
    • T2 W TSE: 36 datasets (USA: 25, EU: 11)
    • Manufacturer: All Siemens Healthineers scanners.
• CT Contouring Algorithm Test Set:
  • Sample Size: N = 414
  • Data Provenance: Retrospective study, data from multiple clinical sites across North American, South American, Asia, Australia, and Europe. This dataset is distributed across three cohorts:
    • Cohort A: 73 datasets (Germany: 14, Brazil: 59) - Siemens scanners only
    • Cohort B: 40 datasets (Canada: 40) - GE: 18, Philips: 22 scanners
    • Cohort C: 301 datasets (NA: 165, EU: 44, Asia: 33, SA: 19, Australia: 28, Unknown: 12) - Siemens: 53, GE: 59, Philips: 119, Varian: 44, Others: 26 scanners

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

• The ground truth annotations were "drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists."
• "Additionally, a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist using validated medical image annotation tools."
• The exact number of individual annotators or experts is not specified beyond "a team" and "a board-certified radiation oncologist." Their specific experience level (e.g., "10 years of experience") is not given beyond "experienced" and "board-certified."

4. Adjudication Method for the Test Set

• The document implies a consensus/adjudication process: "a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist." This suggests that initial annotations by the "experienced annotators" were reviewed and potentially corrected by a higher-level expert. The specific number of reviewers for each case (e.g., 2+1, 3+1) is not explicitly stated, but it was at least a "team" providing initial annotations followed by a "board-certified radiation oncologist" for quality assessment/correction.

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

• No, the document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study evaluating how much human readers improve with AI vs. without AI assistance. The validation studies focused on the standalone performance of the algorithm against expert-defined ground truth.

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

• Yes, the performance validation described in section 10 ("Performance Software Validation") is a standalone (algorithm only) performance study. The metrics (Dice, ASSD, Fail Rate) compare the algorithm's output directly to the established ground truth. The device produces contours that must be reviewed and edited by trained medical professionals, but the validation tests the AI's direct output.

7. The Type of Ground Truth Used

• The ground truth used was expert consensus/manual annotation. It was established by "manual annotation" by "experienced annotators mentored by radiologists or radiation oncologists" and subsequently reviewed and corrected by a "board-certified radiation oncologist." Annotation protocols followed NRG/RTOG guidelines.

8. The Sample Size for the Training Set

• MR Contouring Algorithm Training Set:
  • T1 VIBE/Dixon W: 219 datasets
  • T2 W TSE: 225 datasets
  • Prostate (T2W): 960 datasets
• CT Contouring Algorithm Training Set: The training dataset sizes vary per organ group:
  • Cochlea: 215
  • Thyroid: 293
  • Constrictor Muscles: 335
  • Chest Wall: 48
  • LN Supraclavicular, Axilla Levels, Internal Mammaries: 228
  • Duodenum, Bowels, Sigmoid: 332
  • Stomach: 371
  • Pancreas: 369
  • Pulmonary Artery, Vena Cava, Trachea, Spinal Canal, Proximal Bronchus: 113
  • Ventricles & Atriums: 706
  • Descending Coronary Artery: 252
  • Penile Bulb: 854
  • Uterus: 381

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

• For both training and validation data, the ground truth annotations were established using the "Standard Annotation Process." This involved:
  • Annotation protocols defined following NRG/RTOG guidelines.
  • Manual annotations drawn by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool.
  • A quality assessment including review and correction of each annotation by a board-certified radiation oncologist using validated medical image annotation tools.
• The document explicitly states that the "training data used for the training of the algorithm is independent of the data used to test the algorithm."

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AI-Rad Companion (Pulmonary) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from specialty care and general practice in the evaluation and assessment of disease of the lungs.

It provides the following functionality:

• Segmentation and measurements of complete lung and lung lobes
• · Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
• · Providing an interface to external Medical Device syngo.CT Lung CAD
• · Segmentation and measurements of solid and sub-solid lung nodules
• Dedication of found lung nodules to corresponding lung lobe
• Correlation of segmented lung nodules of current scan with known priors and quantitative assessment of changes of the correlated data.
• Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinquished.

The software has been validated for data from Siemens Healthineers (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).

Only DICOM images of adult patients are considered to be valid input.

The subject device AI-Rad Companion (Pulmonary) is an image processing software that utilizes machine learning and deep learning algorithms to provide quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax. AI-Rad Companion (Pulmonary) builds on platform functionality provided by the AI-Rad Companion Engine and cloud/edge functionality provided by the Siemens Healthineers teamplay digital platform. AI-Rad Companion (Pulmonary) is an adjunct tool and does not replace the role of a qualified medical professional. AI-Rad Companion (Pulmonary) is also not designed to detect the presence of radiographic findings other than the prespecified list. Qualified medical professionals should review original images for all suspected pathologies.

AI-Rad Companion (Pulmonary) offers:

• Segmentation of lungs, ●
• Segmentation of lung lobes.
• Parenchyma evaluation, ●
• Parenchyma ranges,
• Pulmonary density,
• Visualization of segmentation and parenchyma results,
• Interface to LungCAD,
• Lesion segmentation, ●
• Visualization of lesion segmentation results, ●
• Lesion follow-up

AI-Rad Companion (Pulmonary) requires images of patients of 22 years and older.

AI-Rad Companion (Pulmonary) SW version VA40 is an enhancement to the previously cleared device AI-Rad Companion (Pulmonary) (K213713) that utilizes machine and deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax.

As an update to the previously cleared device, the following modifications have been made:

• Sub-solid Lung Nodule Segmentation ●

This feature provides the ability to segment and measure all subtypes of lesions including solid and sub-solid lesions.

• . Modified Indications for Use Statement The indications for use statement was updated to include descriptive text for sub-solid lung nodule addition.
• Updated Subject Device Claims List The claims list was updated to reflect the new device functionality
• . Updated Limitations for Use Additional limitations for use has been added to the subject device.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 273 subjects from the United States and 254 subjects from Germany, for a total of 527 subjects.
• Data Provenance: The data originated from the United States (69% of cases) and Germany (31% of cases). The data was retrospective, as it refers to "previously acquired Computed Tomography DICOM images."
• Imaging Vendors: The test data included images from Canon/Toshiba (18%), GE (35%), Philips (15%), and Siemens (32%).

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

• Number of Experts: Two board-certified radiologists, with a third radiologist for adjudication.
• Qualifications:
  • Radiologist 1: 10 years of experience (board-certified)
  • Radiologist 2: 7 years of experience (board-certified)
  • Adjudicating Radiologist 3: 9 years of experience

4. Adjudication Method for the Test Set

• Method: 2+1 (Two experts independently established ground truth, and in case of disagreement, a third expert served as an adjudicator.)

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

• Was it done?: No, a traditional MRMC comparative effectiveness study involving human readers was not performed in the context of this specific submission. The study focuses on the standalone performance of the AI algorithm in comparison to the predicate device's performance, particularly for the new sub-solid nodule segmentation feature. The device is described as an "adjunct tool," but the presented study validates the algorithm's performance against expert consensus, not against human readers with and without AI.

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

• Was it done?: Yes, the performance testing described directly evaluates the AI-Rad Companion (Pulmonary) lesion segmentation algorithm's accuracy (measured by DICE score, bias, and RMSE) against established ground truth. This is a standalone performance evaluation of the algorithm.

7. The Type of Ground Truth Used

• Type: Expert Consensus. The ground truth annotations for the test data were established independently by two board-certified radiologists, with a third radiologist serving as an adjudicator in cases of disagreement.

8. The Sample Size for the Training Set

• The sample size for the training set is not explicitly stated in the provided document. However, it is mentioned that "None of the clinical sites providing the test data provided data for training of any of the algorithms. Therefore there is a clear independence on site level between training and test data." This indicates that a distinct training set (or sets) was used.

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

• The document does not explicitly state how the ground truth for the training set was established. It only emphasizes the independence of the training and test data sites.

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The Insulia Bolus Companion is indicated for the management of diabetes by adults with diabetes, by calculating an insulin dose or suggesting carbohydrate intake based on user entered data.

Prior to use, a healthcare professional must provide a patient-specific target blood glucose, insulin doses based on fixed or variable meal sizes, and insulin sensitivity parameters to be programmed into the software. This can be done through a dedicated, secured web portal.

Insulia Bolus Companion is a prescription device indicated for the management of diabetes, for adults with diabetes, by calculating an insulin dose or suggesting carbohydrate intake based on user entered data; and for healthcare professionals (HCP) having experience in the management of people with diabetes treated with bolus insulin.

Insulia Bolus Companion is for prescription use and is not for over-the-counter sale.

Insulia Bolus Companion includes a Bolus Calculator intended to provide direction to the patient in response to blood glucose (BG) and health events, within the scope of a preplanned treatment program prescribed by their HCP for insulin suggestions. The guidance is similar to the directions provided to patients as a part of routine clinical practice: prior to use, a healthcare professional must provide a patient-specific target blood glucose, insulin doses based on fixed or variable meal sizes, and insulin sensitivity parameters to be programmed into the software.

An HCP can only start using Insulia Bolus Companion after having been registered by the Manufacturer or the delegate field-support team.

Insulia Bolus Companion includes three components:

• · A mobile application for use by people with diabetes on commercially available smartphones (iPhone or Android) and tablets, enabling patients to document BG measurements, meals, and generate dose suggestions for bolus insulin
• · A web-based application for use by HCPs in professional healthcare settings through a compatible web browser on a computer, allowing patient inclusion and patient monitoring in-person and by distance
• · A secure database hosted in a private cloud environment and used to securely store patient data

The provided text does not contain detailed information about the acceptance criteria and the study that proves the device meets those criteria, specifically regarding numerical performance metrics, sample sizes for test and training sets, expert qualifications, or adjudication methods.

The document states that "Design verification and validation testing on Insulia Bolus Companion demonstrated that the device meets the performance requirements for its intended use" and "performance testing has demonstrated that Insulia Bolus Companion performs as intended and is substantially equivalent to the predicate device." However, it does not provide the specifics of this performance testing.

Therefore, I cannot provide a detailed answer to your request that includes:

1. A table of acceptance criteria and reported device performance.
2. Sample size and data provenance for the test set.
3. Number of experts and their qualifications used for ground truth.
4. Adjudication method.
5. Effect size for MRMC study (as no mention of such a study is made).
6. Standalone performance details.
7. Type of ground truth used.
8. Sample size for the training set.
9. How ground truth for the training set was established.

The document primarily focuses on the regulatory submission process, demonstrating substantial equivalence to a predicate device, and outlining the device's indications for use and general technological characteristics.

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AI-Rad Companion Brain MR is a post-processing image analysis software that assists clinicians in viewing, analyzing, and evaluating MR brain images.

AI-Rad Companion Brain MR VA50 is an enhancement to the predicate, AI-Rad Companion Brain MR VA40 (K213706). Just as in the predicate, the brain morphometry feature of AI-Rad Companion Brain MR addresses the automatic quantification and visual assessment of the volumetric properties of various brain structures based on T1 MPRAGE datasets. From a predefined list of brain structures (e.g. Hippocampus, Caudate, Left Frontal Gray Matter, etc.) volumetric properties are calculated as absolute and normalized volumes with respect to the total intracranial volume. The normalized values are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects. The deviation from this reference population can be visualized as 3D overlay map or out-of-range flag next to the quantitative values.

Additionally, identical to the predicate, the white matter hyperintensities feature addresses the automatic quantification and visual assessment of white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. The detected WMH can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.

Here's a breakdown of the acceptance criteria and study details for the AI-Rad Companion Brain MR device, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

Study Details

1. Sample Size and Data Provenance:

   • Test Set Sample Size: 75 subjects / 150 studies (2 scans per subject).
   • Data Provenance: The data originate from a mix of retrospective and potentially prospective sources, from both the US and Europe:
     • UPenn (US): 15 subjects
     • ADNI (US): 15 subjects
     • Lausanne (EU): 22 subjects
     • Prague (EU): 23 subjects
     • Medical Indication: 60 Multiple Sclerosis (MS) patients, 15 Alzheimer's (AD) patients.
     • Age Range: 25-88 years.
     • Gender Distribution: 56 females, 19 males.
     • Scanner Info: Siemens 3.0T MR scanners, T1w MPRAGE and T2w FLAIR scan protocols.

2. Number of Experts and Qualifications for Ground Truth:

   • The document states that for each dataset, three sets of ground truth were manually annotated. Each set was annotated by a "disjoint group of annotator, reviewer, and clinical expert."
   • For the initial annotation and review, "in-house annotators" and "in-house reviewers" were used.
   • For final review and correction, a "clinical expert" was used, randomly assigned per case to minimize bias.
   • Specific qualifications (e.g., years of experience, board certification) for these experts are not explicitly stated in the provided text, beyond being "clinical experts."

3. Adjudication Method for Test Set:

   • The ground truth process involved a multi-step adjudication. For each test dataset:
     1. Three initial annotations by three different in-house annotators.
     2. Each initial annotation was reviewed by an in-house reviewer.
     3. Each initial annotation (after in-house review) was reviewed by a reference clinical expert.
     4. If corrections by the clinical expert were "significant and time-consuming," they were communicated back to the annotator for correction and then re-reviewed.
   • This resembles a form of iterative consensus building and expert adjudication, where multiple initial annotations are refined through reviewer and expert input, rather than a strict N+1 or N+M voting system for final ground truth, though the final decision appears to rest with the clinical expert.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC study was done. The document explicitly states: "The predicate (K213706) was not validated using clinical tests and therefore no clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion Brain MR."
   • The validation focused on standalone algorithmic performance compared to expert-established ground truth and comparison against a "reference device" (icobrain) using equivalent validation methodology for the WMH follow-up feature.
   • Therefore, there's no reported effect size of human readers improving with AI vs. without AI assistance.

5. Standalone (Algorithm Only) Performance Study:

   • Yes, a standalone performance study was conducted for the WMH follow-up feature. The acceptance criteria and reported performance metrics (PCC, Dice, F1-score) are for the algorithm's performance against the established ground truth.

6. Type of Ground Truth Used:

   • The ground truth for the White Matter Hyperintensities (WMH) Follow-Up Feature was established through expert consensus and manual annotation. It involved a "disjoint group of annotator, reviewer, and clinical expert" for each ground truth dataset. The clinical expert performed the final review and correction.

7. Training Set Sample Size:

   • The document states: "The training data used for the fine tuning the hyper parameters of WMH follow-up algorithm is independent of the data used to test the white matter hyperintensity algorithm follow up algorithm."
   • However, the specific sample size for the training set is not provided in the given text. It mentions independent training data but does not quantify it.

8. How Ground Truth for Training Set was Established:

   • The document mentions that training data was used for "fine tuning the hyper parameters." While it implies that the training data would also require ground truth, the method for establishing ground truth for the training set is not explicitly described in the provided text. It only states that the training data was "independent" of the test data. Given the "WMH follow-up algorithm does not include any machine learning component," the type of "training" might refer to calibration or rule optimization rather than machine learning model training in the conventional sense, and subsequently, how ground truth for that calibration was established is not detailed.

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AI-Rad Companion (Cardiovascular) is 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 cardiovascular diseases.

It provides the following functionality:

• Segmentation and volume measurement of the heart
• Quantification of the total calcium volume in the coronary arteries
• Segmentation of the aorta
• Measurement of maximum diameters of the aorta at typical landmarks
• Threshold-based highlighting of enlarged diameters

The software has been validated for non-cardiac chest CT data with filtered backprojection reconstruction from Siemens Healthineers, GE Healthcare, Philips, and Toshiba/Canon. Additionally, the calcium detection feature has been validated on non-cardiac chest CT data with iterative reconstruction from Siemens Healthineers.

Only DICOM images of adult patients are considered to be valid input.

AI-Rad Companion (Cardiovascular) SW version VA20 is an enhancement to the previously cleared device AI-Rad Companion (Cardiovascular) K183268 that utilizes machine and deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of cardiovascular diseases.

As an update to the previously cleared device, the following modifications have been made:

Segmentation of Aorta – Performance Improvement
Although the structure of the underlying neural network has not changed in the subject device of this submission, the performance was enhanced over the previously cleared device by adding training data (re-use of existing annotations + 267 additional annotations).

Aorta diameter measurements - Maximum Diameter Ascending, Descending Aorta
In the previously cleared device diameter measurements of the aorta were performed at nine predefined locations according to the AHA guidelines.

As an enhancement to the previously cleared device and subject of this submission are aorta diameter measurements at the locations of the maximum diameter of the ascending and the descending aorta.

Visualization of aorta's VRT and as cross-sectional MPRs - Maximum Diameter Ascending, Descending Aorta
In the previously cleared device visualization VRT and cross-sectional MPRs were provided at nine predefined locations according to the AHA guidelines.

As an enhancement to the previously cleared device, such visualization of the maximum diameter of the ascending and descending aorta were added to the subject of this submission.

Categorization of diameter measurements - Maximum Diameter Ascending, Descending Aorta
In the previously cleared device categorization of diameter measurements was performed at locations according to the AHA guidelines.

With the subject of this submission, the categorization of diameter measurements was extended to locations of the maximum diameter of the ascending and descending aorta.

Individual Confirmation of Aorta Findings
For the measurements of the aorta, only all the measurements could be accepted or declined in the predicate device.

Within the scope of this submission the concept of individual accept, decline-possibility was introduced to all aorta measurements.

Structured DICOM Report (DICOM TID 1500)
In the predicate device, the system would produce results in form of quantitative, structured and textual reports and would generate DICOM Secondary Capture images which would be forwarded to PACS reading and reporting systems.

Within the scope of this submission, the system supports an alternative, digital output format for the same results. For this purpose, a DICOM Structured Report is generated which is both human and machine readable and, therefore, will support, e.g., a transfer of the results into the clinical report more efficiently. The DICOM Structured Report is compliant to the TID1500 format for applicable content.

Cloud and Edge Deployment
Another enhancement provided within this submission is the existing cloud deployment in an on-premise deployment known as an edge deployment. The system remains hosted in the teamplay digital health platform and remains driven by the AI-Rad Companion Engine; however, with the edge deployment the processing of clinical data and the generation of results is performed within the customer environment. This system remains fully connected to the cloud for monitoring and maintenance of the system from a remote setup. At the time of this submission this feature has been cleared in submission K213706 (AI-Rad Companion Brain MR VA40) and is unchanged within this subject device.

Here's a breakdown of the acceptance criteria and study details for the AI-Rad Companion (Cardiovascular) from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Aorta Segmentation:
  • Sample Size: N=315
  • Data Provenance: Retrospective clinical cohort. Details regarding country of origin are not specified.
• Aorta Diameter Measurements:
  • Sample Size: N=193
  • Data Provenance: Representative retrospective clinical cohort. This included:
    • Consecutive patients undergoing Chest CT exams for varying indications.
    • A cohort at increased risk for incidental findings, particularly in the cardiovascular domain, due to the screening nature of the examination.
  • Specific percentages: 50% of cases with dilated aorta, 9% of cases with aortic aneurysm.
  • Details regarding country of origin are not specified.

3. Number and Qualifications of Experts for Test Set Ground Truth

The document does not explicitly state the number or qualifications of experts used to establish the ground truth for the test set. It mentions "inter-reader variability was assessed" for the diameter measurements, implying human expert involvement in establishing reference values, but provides no further details on their credentials or number.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly stated in the provided text.

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

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study (AI vs. human readers with AI assistance vs. without AI assistance) is mentioned. The study focuses on comparing the subject device's performance to the predicate device's performance, and algorithm accuracy against ground truth.

6. Standalone (Algorithm Only) Performance Study

Yes, standalone performance studies were done. The reported performance metrics (DICE coefficient, bias, MAE) are directly attributed to the device's algorithms in comparison to established (likely expert-defined) ground truth, without human interaction during the measurement process. The text explicitly states, "the performance of the aorta segmentation module has been validated... For the subject device, average DICE (± std. dev) coefficient was 0.924 (± 0.046)." Similarly for the diameter measurements, "91.9% of the measurements provided by the subject device were found to lie within the LoA," indicating standalone algorithmic measurement.

7. Type of Ground Truth Used for Test Set

The type of ground truth used is implied to be expert consensus or expert measurements, especially for the aorta diameter measurements where "inter-reader variability was assessed" and the device's measurements were compared against these established ranges. For segmentation, the DICE coefficient comparison against the predicate suggests a reference standard, likely also based on expert annotations or consensus.

8. Sample Size for the Training Set

The document notes that the performance of the aorta segmentation was "enhanced over the previously cleared device by adding training data (re-use of existing annotations + 267 additional annotations)." This indicates that the training set for the aorta segmentation was augmented with at least 267 new cases, in addition to previously used annotations from the predicate device's training. The total size of the training set is not explicitly given, only the additional annotations for the updated model.

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

The text states "re-use of existing annotations + 267 additional annotations" for the training data. While it doesn't explicitly detail how these annotations were created, "annotations" typically refer to expert-labeled segmentation masks or measurements. This implies that medical professionals (e.g., radiologists) meticulously outlined structures or took measurements on the training images to serve as the ground truth for training the deep learning model.

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The Merit Low Profile Companion Sheath is indicated to be used for the introduction of interventional and diagnostic devices into the peripheral (and coronary) vasculature.

The Low Profile Companion Sheath is a sterile, disposable device consisting of (a) a coil reinforced shaft with and the distal end; (b) a hemostasis valve with a side port and (c) a tapered tip dilator with snap-fit hub at the proximal end.

(a) Shaft. The coil reinforced, multi-layer polymer shaft contains a tapered tip at the distal end. A continuous inner PTFE tube forms the core of the shaft and provides a circular working lument through which devices can be passed. A hydrophilic coating is applied to the entire outer surface of the shaft. A radiopaque marker made of platinum iridium is embedded 5mm from the dista At the proximal end of the shaft, a female, winged luer hub is over-molded onto the shaft to support handling and to provide for the connection of the hemostasis valve.

(b) Hemostasis valve. A removable hemostasis valve is thread onto the proximal end of the shaft. Inside the valve housing, a lubricated, silicone slit disc provides a seal around devices passed through the sheath, thereby preventing blood leakage through the valve. Just distal of the valve housing is connected to a side port leading to a three-way stopcock valve. The sideport is used for flushing the introducer sheath.

(c) Dilator. The dilator made of a polypropylene blend contains a full-length round lumen to allow placement over a guidewire. The distal end of the dilator is configured as a tapered tip that extends about 2 cm beyond the end of the dilator is fully inserted through the sheath.

The Low Profile Companion Sheath is a prescription medical device that is used only in healthcare facilities and hospitals. The device is placed in patients for up to 24 hours.

This is a 510(k) summary for a medical device called the "Low Profile Companion Sheath." This document describes the device and claims substantial equivalence to a previously cleared device (predicate device). For such submissions, the acceptance criteria and study information provided generally focus on demonstrating that the new device performs comparably to the predicate or meets established performance specifications. The details for acceptance criteria and studies are typically more concise than for novel device approvals.

Here's an analysis of your requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

Based on the text, the acceptance criteria are implicitly tied to meeting "predetermined specifications" for the "changed dimensions of the device." The specific numerical acceptance criteria are not explicitly provided in this summary, but the general categories of tests and their successful outcome are stated.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not explicitly state the sample size used for the design verification tests. It mentions "design verification tests" were performed. The "data provenance" (country of origin, retrospective/prospective) is also not specified, as these are typically bench-top engineering tests rather than clinical studies.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This is a technical device submission, not an AI/diagnostic software submission where expert adjudication is common for ground truth. Therefore, this information is not applicable and not provided in the document. The "ground truth" here is defined by engineering specifications and physical measurements.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

As this is a technical device submission involving engineering measurements and performance testing, an "adjudication method" in the context of expert review for diagnostic accuracy is not applicable and not mentioned. The tests would likely be performed by qualified engineers/technicians according to established protocols.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study is relevant for diagnostic software (often AI-powered) where human interpretation is involved. This submission is for a physical medical device (catheter introducer sheath). Therefore, such a study was not performed and is not applicable to this device.

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

This refers to AI algorithm performance. Since this is a physical medical device, there is no AI algorithm involved, and thus no standalone performance was evaluated in this context.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

For the design verification tests, the "ground truth" would be the predetermined engineering specifications and physical measurements for the device's dimensions and characteristics. This is not "expert consensus," "pathology," or "outcomes data" in the clinical sense.

8. The sample size for the training set

This question refers to the training of an AI algorithm. Since this is a physical medical device and does not involve an AI algorithm, there is no "training set."

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

Again, this question is relevant for AI algorithm development. As there is no AI algorithm or training set for this physical medical device, this information is not applicable.

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AI-Rad Companion (Musculoskeletal) is an image processing software that provides quantitative andysis 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.

AI-Rad Companion (Musculoskeletal) SW version VA20 is an enhancement to the previously cleared device AI-Rad Companion (Musculoskeletal) K193267 that utilizes deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of the spine.

As an update to the previously cleared device, the following modifications have been made:

• Enhanced AI Algorithm The vertebrae segmentation accuracy has been improved through retraining the algorithm.
• DICOM Reports

The reports generated out of the system have been enhanced to support both human and machine-readable formats. Additionally, an update version of the system changed the DICOM structured report format to TID 1500 for applicable content.

• Architecture Enhancement for on premise Edge deployment The system supports the existing cloud deployment as well as an on premise "edge" deployment. The system remains hosted in the teamplay digital health platform and remains driven by the AI-Rad Companion Engine. Now the edge deployment implies that the processing of clinical data and the generation of results can be performed onpremises within the customer network. The edge system is fully connected to the cloud for monitoring and maintenance of the system from remote.

Here's a summary of the acceptance criteria and the study proving the device meets those criteria, based on the provided document:

Acceptance Criteria and Device Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 140 Chest CT scans, comprising 1,553 thoracic vertebrae.
• Data Provenance: The document lists two sources for the data:
  • KUM (N=80): Primary indications and various medical conditions are detailed (e.g., Lung/airways, infect focus, malignancy, cardiovascular, trauma).
  • NLST (N=60): Comorbidities are detailed (e.g., diabetes, heart disease, hypertension, cancer, smoking history).
  • The patient demographics (sex, age, manufacturer of CT scanner, slice thickness, dose, reconstruction method, kernel, contrast enhancement) are provided.
  • The document implies this is retrospective data collected from existing patient studies, as it describes the "testing data information" with pathologies and patient information already existing.

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

• Number of Experts: Four board-certified radiologists.
• Qualifications of Experts: Board-certified radiologists. (No specific years of experience are mentioned).

4. Adjudication Method for the Test Set

• Adjudication Method: Each case was read independently by two radiologists in randomized order.
  • For outliers (cases where the initial two radiologists' annotations potentially differed significantly or inconsistently), a third annotation was blindly provided by a radiologist who had not previously annotated that specific case.
  • The ground truth was then generated by the average of the two most concordant measurements.
  • For all other cases (non-outliers), the two initial annotations were used as ground truth. This suggests a form of 2+1 adjudication for outliers and 2-reader consensus for non-outliers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• The document describes a validation study comparing the device's performance against ground truth established by human readers. However, it does not describe a multi-reader multi-case (MRMC) comparative effectiveness study designed to measure the effect size of how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the AI algorithm.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Yes, a standalone study was performed. The "Summary Performance Data" directly reports the "Failure Rate" and "Inter-reader variability" (difference between AIRC and ground truth) of the AI-Rad Companion (Musculoskeletal) itself. The study's design of comparing device measurements to expert-established ground truth evaluates the algorithm's standalone accuracy.

7. The Type of Ground Truth Used

• Expert Consensus. The ground truth for the test set was established by the manual measurements and annotations of four board-certified radiologists, utilizing an adjudication process to determine the most concordant measurements for vertebra heights and average density (HU) values.

8. The Sample Size for the Training Set

• The document does not specify the exact sample size for the training set. It only states that the "training data used for the training of the post-processing algorithm is independent of the data used to test 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 "vertebrae segmentation accuracy has been improved through retraining the algorithm," implying that training data with associated ground truth was used for this process, but the method of establishing that ground truth is not detailed in this submission.

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AI-Rad Companion Organs RT is a post-processing software intended to automatically contour DICOM CT imaging data using deep-learning-based algorithms.

Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning. AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT.

The output of AI-Rad Companion Organs RT in the format of RTSTRUCT objects are intended to be used by trained medical professionals.

The software is not intended to automatically detect or contour lesions. Only DICOM images of adult patients are considered to be valid input.

AI-Rad Companion Organs RT is a post-processing software used to automatically contour DICOM CT imaging data using deep-learning-based algorithms. AI-Rad Companion Organs RT contouring workflow supports CT input data and produces RTSTRUCT outputs. The configuration of the organ database and organ templates defining the organs and structures to be contoured based on the input DICOM data is managed via a configuration interface. Contours that are generated by AI-Rad Companion Organs RT may be used as input for clinical workflows including external beam radiation therapy treatment planning.

The output of AI-Rad Companion Organs RT, in the form of RTSTRUCT objects, are intended to be used by trained medical professionals. The output of AI-Rad Companion Organs RT must be used in conjunction with appropriate software such as Treatment Planning Systems and Interactive Contouring applications, to review, edit, and accept contours generated by AI-Rad Companion Organs RT application.

At a high-level, AI-Rad Companion Organs RT includes the following functionality:

• 1. Automated contouring of Organs at Risk (OAR) workflow
  • a. Input -DICOM CT
  • b. Output DICOM RTSTRUCT
• 2. Organ Templates configuration (incl. Organ Database)
• 3. Web-based preview of contouring results to accept or reject the generated contours

Here's a breakdown of the acceptance criteria and study details for the AI-Rad Companion Organs RT device, based on the provided text:

1. Table of Acceptance Criteria & Reported Device Performance:

Note: The text did not explicitly state the "fail rate" for the Head & Neck Lymph Nodes, only that it should be "smaller than 15%". The conclusion implies all acceptance criteria were met. The confidence intervals for the predicate device's DICE and ASSD are missing in Table 4, but the statement "performance of the subject device and predicate device are comparable" suggests the criteria were acceptable.

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

• Sample Size: N = 113 retrospective performance study on CT data.
  • This N=113 is composed of:
    • Cohort A: 73 subjects (14 from Germany, 59 from Brazil)
    • Cohort B: 40 subjects (Canada: 40)
• Data Provenance: Multiple clinical sites across North America (Canada) and Europe (Germany, Brazil – often considered part of South America, but grouped with "Europe" in the text for data collection context). The study used previously acquired CT data (retrospective).

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

• Number of Experts: Not explicitly stated as a specific number. The text mentions "a team of experienced annotators" and "a board-certified radiation oncologist".
• Qualifications:
  • Annotators: "experienced annotators mentored by radiologists or radiation oncologists".
  • Review/Correction: "board-certified radiation oncologist".

4. Adjudication Method for the Test Set:

• The ground truth annotations were drawn manually by a team of experienced annotators and then underwent a "quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist". This suggests a method where initial annotations are created by multiple individuals and then reviewed/corrected by a single, highly qualified expert. This could be interpreted as a form of expert review/adjudication.

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

• No, a MRMC comparative effectiveness study was not explicitly stated as having been done. The performance evaluation focused on comparing the AI algorithm's output to expert-generated ground truth and comparing the device's performance to predicate/reference devices, not on how human readers improve with or without AI assistance.

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

• Yes, a standalone performance study was done. The study "validated the AI-Rad Companion Organs RT software from clinical perspective" by evaluating its auto-contouring algorithm, and calculating metrics like DICE coefficients and ASSD against ground truth annotations. The device's output "must be used in conjunction with appropriate software... to review, edit, and accept contours", indicating its standalone output is then reviewed by a human, but the validation of its generation of contours is standalone.

7. The Type of Ground Truth Used:

• Expert Consensus/Manual Annotation with Expert Review (following guidelines): "Ground truth annotations were established following RTOG and clinical guidelines using manual annotation. The ground truth annotations were drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool. Additionally, a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist..." This indicates a robust expert-derived ground truth.

8. The Sample Size for the Training Set:

• 160 datasets (for Head & Neck specifically, other organs might have different training data, but this is the only training set sample size provided).

9. How the Ground Truth for the Training Set was Established:

• "In both the annotation process for the training and validation testing data, the annotation protocols for the OAR were defined following the NRG/RTOG guidelines. The ground truth annotations were drawn manually by a team of experienced annotators mentored by radiologists or radiation oncologists using an internal annotation tool. Additionally, a quality assessment including review and correction of each annotation was done by a board-certified radiation oncologist using validated medical image annotation tools."
  • This is the same process as for the test set, ensuring consistency in ground truth establishment.

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