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

Al-Rad Companion Brain MR provides the following functionalities:

• Automated segmentation and quantitative analysis of includual brain structures and white matter hyperintensities
• Quantitative comparison of brain structure with normative data from a healthy population
• Presentation of results of reporting that includes all numerical values as well as visualization of these results

AI-Rad Companion Brain MR VA40 is an enhancement to the predicate. AI-Rad Companion Brain MR VA20 (K193290). Just as in the predicate, 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. In AI-Rad Companion Brain MR VA40, the quantification and visual assessment extends to white matter hyperintensities on the basis of T1 MPRAGE and T2 weighted FLAIR datasets. These datasets are acquired as part of a typical head MR acquisition. The results are directly archived in PACS as this is the standard location for reading by radiologist. From a predefined list of 30 structures (e.g. Hippocampus, Left Frontal Grey Matter, etc.), volumetric properties are calculated as absolute and normalized volumes with respect to the total intercranial volume. The normalized values for a given patient are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects.

The white matter hyperintensities can be visualized as a 3D overlay map and the quantification in count and volume as per 4 brain regions in the report.

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

• 1. Modified Intended Use Statement
• 2. Addition of white matter hyperintensities overlay map, count and volume as per 4 brain regions
• 3. Enhanced DICOM Structured Report (DICOM SR)
• 4. Updated deployment structure

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

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

All reported device performance metrics meet or exceed the specified acceptance criteria, as their 95% Confidence Intervals either include the criterion or are entirely above it (for metrics requiring a minimum value).

Study Details

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

• Test Set Sample Size: 64 subjects for the main testing cohort, and 25 subjects for the reproducibility cohort.
  • Total Subjects: 89 subjects (64 + 25)
  • Total Studies: 164 studies (64 for testing cohort, and 100 for reproducibility cohort)
• Data Provenance (Country of Origin): United States (Cleveland, Baltimore, New York, ADNI), Switzerland (Lausanne, CLEMENS), France (Montpellier).
• Retrospective or Prospective: The text does not explicitly state whether the data was retrospective or prospective, but the description of "test data" and "training data" suggests retrospective data collection from existing datasets.

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

• Number of Experts: Three distinct groups involved in establishing ground truth for each dataset: an annotator, a reviewer, and a clinical expert. The text implies a total of three individuals per case, forming a disjoint group (i.e., three different people for annotation, review, and expert correction).
• Qualifications of Experts: The text refers to them as "in-house annotators," "in-house reviewer," and "referred clinical expert." Specific qualifications (e.g., years of experience, board certification) are not explicitly detailed beyond the "clinical expert" designation.

4. Adjudication method for the test set:

• Adjudication Method: A multi-step process: "For each dataset, three sets of white matter hyperintensity ground truth are annotated manually. Each set is annotated by a disjoint group of annotator, reviewer, and clinical expert with the expert randomly assigned per case to minimize annotation bias. For each test dataset, the three initial annotations are annotated by three different in-house annotators. Then, each initial annotation is reviewed by the in-house reviewer. Afterwards, each initial annotation is reviewed by the referred clinical expert. The clinical expert reviews and corrects the initial annotation of the WMH according to the annotation protocol."
  • This is a form of cascading/sequential review and consensus, rather than a direct voting or "X+Y" adjudication, with the clinical expert performing the final review and correction.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done:

• No, an MRMC comparative effectiveness study was not done. The study focuses on the standalone performance of the AI algorithm against expert-established ground truth, not on how human readers' performance improves 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 testing validated the "AI-Rad Companion Brain MR WMH" algorithm's performance by comparing its outputs directly to manually annotated ground truth. The results table explicitly presents the "Volumetric Segmentation Accuracy," "Voxel-wise Segmentation Accuracy," "WMH Lesion-wise Segmentation Accuracy," and "Reproducibility" of the device.

7. The type of ground truth used:

• Expert Consensus / Expert-Annotated Ground Truth. The ground truth was established through a multi-step manual annotation, review, and correction process by "in-house annotators," "in-house reviewer," and a "clinical expert."

8. The sample size for the training set:

• The sample size for the training set is not explicitly stated. The text only mentions: "The training data used for the training of the White matter hyperintensity algorithm is independent of the data used to test the white matter hyperintensity algorithm."

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

• The text does not provide details on how the ground truth for the training set was established. It only ensures that the training data and testing data are independent.

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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 provides the following functionalities:

• Automatic segmentation and quantitative analysis of individual brain structures
• Quantitative comparison of each brain structure with normative data from a healthy population
• Presentation of results for reporting that includes all numerical values as well as visualization of these results

AI-Rad Companion Brain MR is an enhancement to the predicate, syngo.MR Application (K182904). Just as in the predicate, 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. These datasets are acquired as part of a typical head MR acquisition. The results are directly archived in PACS as this is the standard location for reading by radiologist. From a predefined list of 30 structures (e.g. Hippocampus, Caudate, Left Frontal Grey Matter, etc.), volumetric properties are calculated as absolute and normalized volumes with respect to the total intercranial volume. The normalized values for a given patient are compared against age-matched mean and standard deviations obtained from a population of healthy reference subjects.

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

• 1. Modified Intended Use Statement
• 2. Addition of a customizable deviation map
• 3. Architectural enhancement for the clinical extension to be deployed with the AI-Rad Companion Engine platform in a cloud-based environment

The provided text is a Siemens 510(k) summary for AI-Rad Companion Brain MR, focusing on its substantial equivalence to a predicate device. It explicitly states, "No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion Brain MR." Therefore, the document does not contain the information requested in your prompt regarding acceptance criteria and a study proving the device meets those criteria.

The prompt asks for details about a study that proves the device meets acceptance criteria, including a detailed table of acceptance criteria and reported performance, sample sizes, ground truth establishment, expert qualifications, and MRMC study details. None of this information is present in the provided document.

The document primarily focuses on:

• The device's intended use (post-processing image analysis for MR brain images, including segmentation, quantitative analysis, and comparison with normative data).
• Its classification as a Class II device.
• Its substantial equivalence to an existing predicate device (syngo.MR Applications, K182904).
• The fact that it is an "enhancement" to the predicate, with modifications including a modified intended use statement, a customizable deviation map, and architectural enhancements for cloud-based deployment.
• Non-clinical tests (software validation, bench testing, conformance to industry standards like ISO 14971, IEC 62304, DICOM, and cybersecurity).
• A clear statement that no clinical tests were conducted.

To answer your prompt, clinical study data, which are not present here, would be required.

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