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AIRAscore is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. This software is intended to automate the current manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images.

AIRAscore is a software that offers automatic, fast and reliable segmentation of brain volumes into gray matter, white matter, cerebrospinal fluid and, if present, white matter lesions with an additional classification of tissue anatomy. The AIRAscore software comprises two functions, referred to as "AIRAscore structure" and "AIRAscore MS". The report created using the AIRAscore structure function contains the volume evaluation for each seqmented anatomical area with the raw value, the relative value with respect to the total intracranial volume, and the percentile for the patient compared to a reference set. It furthermore provides a quick overview of potential segment size differences based on the reference set comparison. If the AIRAscore MS report is requested, it is provided with additional information about the number and the volume of white matter lesions and their categorization (i.e., juxtacortical, periventricular or infratentorial). For analysis with AIRAscore, incoming MRI data need to comply with the DICOM standard and are checked to fulfill the technical requirements. After successful verification, segmentation is performed using specialized neuronal networks that remain static during the lifetime of a software version. The results are then corrected for head size and compared to an age- and sex adjusted reference collective including a statistical classification. A report is generated and transmitted via a DICOM storage SCU (sender) to a defined DICOM storage SCP (usually the picture archive of the referring physician) using the DICOM format.

The provided FDA 510(k) summary for AIRAscore does not contain a detailed description of the acceptance criteria and the study that rigorously proves the device meets those criteria, specifically regarding its clinical performance or accuracy for volumetric quantification. The document focuses on general software verification and validation, comparison to a predicate device, and compliance with standards, but it lacks specific performance testing results (e.g., accuracy, precision, sensitivity, specificity, Dice scores) against a defined ground truth.

The "Performance Testing" section states: "The validation confirmed that AIRAscore performs well across target patient population and scanner manufacturers." However, it does not provide what performance metrics were used, what the acceptance criteria for "performing well" were, or what the actual results were.

Therefore, I cannot populate all the requested information. Below is what can be inferred or stated as missing based solely on the provided text.

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The provided 510(k) summary for AIRAscore does not explicitly define specific quantitative acceptance criteria for its performance (e.g., accuracy of volumetric quantification) or present a detailed study proving these criteria were met. The document focuses on general software verification and validation, comparison to a predicate device, and compliance with general software/medical device standards.

The "Performance Testing" section broadly states that "The validation confirmed that AIRAscore performs well across target patient population and scanner manufacturers." However, it does not specify the metrics, thresholds for "performing well," or the results of this validation.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided document, specific quantitative acceptance criteria and corresponding reported device performance metrics (e.g., accuracy, precision, correlation coefficients, Dice scores for segmentation) are NOT detailed.

The document states:

• Performance Measurement Testing (for New Device - AIRAscore):
  • Accuracy: "Brain segmentable structure volumes / volume changes compared to manually labeled ground truth"
  • Reproducibility: "Brain segmentable structure volumes / volume changes compared on test-retest images"

However, the specific acceptance thresholds for these measurements (e.g., "accuracy > X%", "Dice coefficient > Y") and the actual numerical results that demonstrate the device met these criteria are not included in this summary.

2. Sample Size and Data Provenance for Test Set

• Sample Size for Test Set: Not specified in the provided document.
• Data Provenance: The document states "The validation confirmed that AIRAscore performs well across target patient population and scanner manufacturers." This broadly implies use of diverse data, but specific details on country of origin or whether the data was retrospective or prospective are not provided.

3. Number of Experts and Qualifications for Ground Truth Establishment

• Number of Experts: Not specified in the provided document.
• Qualifications of Experts: The type of study described (comparison to "manually labeled ground truth") implies expert involvement, but their qualifications (e.g., specific medical specialties, years of experience, board certification) are not detailed.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified in the provided document.

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

• MRMC Study: The document describes "performance measurement testing" including "Accuracy" and "Reproducibility" comparing to "manually labeled ground truth." However, it does not mention a multi-reader multi-case (MRMC) comparative effectiveness study evaluating how much human readers improve with AI vs. without AI assistance. The device's intended use is to "automate the current manual process," suggesting a focus on automation rather than AI-assisted human reading improvement.

6. Standalone (Algorithm Only) Performance

• Standalone Performance: The description of "Performance Measurement Testing" (Accuracy relative to manually labeled ground truth, Reproducibility) suggests that the device's standalone performance (algorithm only without human-in-the-loop) was assessed. However, the specific metrics and results of this standalone assessment are not provided.

7. Type of Ground Truth Used

• Type of Ground Truth: "Manually labeled ground truth" is explicitly mentioned for accuracy measurement. The specific methodology for this manual labeling (e.g., expert consensus, pathology, long-term outcomes data) is not further detailed. Given the context of "segmentable brain structures" and "volumetric quantification," it is highly probable that this refers to expert-driven manual segmentation or volumetric measurements on the MR images.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not specified in the provided document. The document mentions "specialized neuronal networks" and "machine learning (supervised voxel classification by a Convolutional Neuronal Network)" for segmentation, which implies a training set was used, but its size is not given.

9. How Ground Truth for Training Set Was Established

• Ground Truth for Training Set: The document mentions "supervised voxel classification by a Convolutional Neuronal Network." For supervised learning, the ground truth for the training set would typically be established through expert annotations (e.g., manual segmentation/labeling of brain structures on MR images). However, the specific methodology and expert involvement for establishing the training set ground truth are not described in the provided text.

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