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ANDI is intended for the display of medical images and other healthcare data. It includes functions for processing MR images, atlas-assisted visualization, segmentation, and volumetric quantification of segmentable brain structures. The output is generated for use by a system capable of reading DICOM image sets.

The information presented by ANDI does not provide prediction, diagnosis, or interpretation of brain health. Clinical interpretation and decision-making are the responsibility of the physician, who must review all clinical information associated with a patient in order to make a diagnosis and to determine the next steps in the clinical care of the patient.

Typical users of ANDI are medical professionals, including but not limited to neurologists and radiologists. ANDI should be used only as adjunctive information. The decision made by trained medical professionals will be considered final.

ANDI is software as a medical device (SaMD) that can be deployed on a cloud-based system, or installed on-premises. It is delivered as software as a service (SaaS) and operates without a graphical user interface. The software can be used to perform DICOM image viewing, image processing, and analysis, specifically designed to analyze brain MRI data. It processes diffusion-weighted and T1-weighted images to quantify and visualize white matter microstructure, providing adjunctive information to aid clinical evaluation. An optional AI-based segmentation feature enables quantification of the volume of gray matter regions. The results are output in a report that presents reference information to assist trained medical professionals in clinical decision-making by enabling comparisons between a patient's data, a normative database, and the patient's longitudinal data.

The document is a 510(k) clearance letter for ANDI 2.0. The device is a "Medical image management and processing system" that processes MR images for atlas-assisted visualization, segmentation, and volumetric quantification of segmentable brain structures. It provides adjunctive information to aid clinical evaluation, with the final clinical interpretation and decision-making remaining the responsibility of the physician.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

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

• Accuracy and Robustness Test Set:

  • Sample Size: 71 subjects.
  • Demographics: 35 females, 36 males; age range 18-86 years.
  • Geographic Origin: 38 subjects were of USA origin.
  • Health Status: 35 healthy subjects; 36 diseased subjects (Multiple Sclerosis (n=11), Parkinson's disease (n=12), Alzheimer's disease (n=12), mild cognitive impairment (n=1)).
  • Data Provenance: The document does not explicitly state if this data was retrospective or prospective, but it implies pre-existing data with the phrase "images preprocessed by ANDI". The selection process (stratified by age, gender, pathology, MRI manufacturer, and field strength) suggests a retrospective collection to represent a diverse population.

• Reproducibility Test Set:

  • Sample Size: 59 subjects (with 2 timepoints each).
  • Demographics: 30 females, 29 males; age range 23-86 years.
  • Geographic Origin: 38 subjects were of USA origin.
  • Health Status: Only healthy subjects were selected to avoid bias from disease progression.
  • Data Provenance: The document does not explicitly state if this data was retrospective or prospective. The mention of "2 timepoints" indicates longitudinal data, which could be from either prospective follow-ups or retrospectively re-analyzed longitudinal datasets.

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

• Number of Experts: A panel of 3 board-certified neuroradiologists.
• Qualifications: Board-certified neuroradiologists.
• Process: 71 preprocessed T1 images were first pre-segmented using Freesurfer v7.4.1. The resulting segmentations were then manually corrected by "an expert" (singular, qualifications not specified, but likely a trained individual) and subsequently "approved" by the panel of 3 board-certified neuroradiologists.

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

The adjudication method for establishing ground truth was a consensus-based approval process by a panel of 3 board-certified neuroradiologists, following initial manual correction by a single expert. This can be seen as a form of expert consensus and approval, but not a specific "2+1" or "3+1" voting method as typically applied when multiple readers independently rate and then adjudicate. Here, the neuroradiologists approved an already corrected segmentation.

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 comparing human readers with AI assistance versus without AI assistance was not done. The document explicitly states: "No clinical studies were considered necessary and performed." The performance testing focused on the standalone algorithm's accuracy and reproducibility against an expert-approved ground truth.

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

Yes, a standalone algorithm-only performance study was done. The sections "AI / ML performance data" detail the evaluation of "ANDI 2.0's brain regions segmentation" against an "expert approved ground truth" using Dice coefficients and volume differences. This assesses the algorithm's performance independent of real-time human interaction.

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

The ground truth used for the test set was expert consensus, derived from Freesurfer pre-segmentations, manually corrected by an expert, and then approved by a panel of 3 board-certified neuroradiologists.

8. The sample size for the training set:

• AI/ML Module Training Set: 140 representative subjects.
• AI/ML Module Validation Set: 747 independent subjects.

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

The document states that the device incorporates a "pretrained third-party brain segmentation algorithm." It mentions that this algorithm was "subjected to training using 140 representative subjects" and "Validation data included 747 independent subjects." However, the document does not explicitly describe how the ground truth for this training or validation set was established by the third party. It only mentions that the data for evaluation of ANDI's integration of this algorithm was independent ("ensuring data independence since ANDI-preprocessed images were not available for the training of the algorithm by the third-party algorithm").

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GyriCalc is intended for automatic labeling, visualization, and quantification including volume, surface area and gyrification analysis (i.e., gyrification index) of segmentable brain structures from a set of MR images.

GyriCalc is intended to be used by qualified personnel and interpreted by a qualified clinician.

GyriCalc is not intended to be used for visualization or quantification of neurologic lesions.

GyriCalc is intended for children between 24 to 36 months of age.

GyriCalc is an automated imaging post-processing software medical device (SaMD) that provides automatic labeling, visualization, volumetric quantification, surface area, thickness and gyrification analysis of brain structures for children ages 24-36 months of age from a set of MR images and returns segmented images and morphometric reports. GyriCalc is a proprietary application which incorporates customized, state-of-the-art open-source software to perform image analysis and quantitative functionality.

The resulting output is provided as a PDF report with segmented color overlays and morphometric reports that can be displayed on commonly used Off The Shelf (OTS) PDF viewer. GyriCalc is not intended to be used for image review. The PDF report is the sole output of the device.

GyriCalc provides morphometric measurements based on 3D T1 MRI series. The output of the software includes volumes that have been annotated with color overlays, with each color representing a particular segmented region, and morphometric reports that provide measured volumes and other qualitative and quantitative data.

GyriCalc's processing architecture includes functionality that performs:

• Preprocessing

• Artifact correction (correct for various artifacts and distortions, such as motion, intensity inhomogeneity, and scanner-related differences),
• Skull-stripping to remove non-brain tissue,
• Bias field correction,
• Intensity normalization,

• Volumetric measurement,

• Surface area measurement,

• Morphological/morphometric analysis,

• Gyrification measurement and report generation.

Here's an analysis of the acceptance criteria and the study proving GyriCalc meets them, based on the provided FDA 510(k) Clearance Letter.

Acceptance Criteria and Device Performance for GyriCalc

1. Table of Acceptance Criteria and Reported Device Performance

Note: While some regions did not meet the stated pre-specified acceptance criteria for volume and surface area errors, the FDA still granted clearance, implying these deviations were considered acceptable in the overall context of safety and effectiveness, or that other factors mitigated the concern.

2. Sample Size and Data Provenance for the Test Set

• Sample Size: 82 T1 head MRIs from 82 patients.
• Data Provenance:
  • Country of Origin: U.S. (54 patients) and Brazil (28 patients).
  • Retrospective or Prospective: Retrospective. The imaging data was collected retrospectively from a population of anonymized patients with curated clinical records. The dataset represents a new, independent sampling of patients that were not involved in the development of the device.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: A total of 6 experts were used, divided into two groups:
  • Group 1 (32 cases): 2 U.S.-based neuroradiologists and 1 U.S.-based neuroimaging consultant PhD. (Specific years of experience are not mentioned, but their titles imply high qualification.)
  • Group 2 (50 cases): 3 U.S.-based neuroradiologists. (Specific years of experience are not mentioned, but their titles imply high qualification.)
• Qualifications of Experts: Neuroradiologists and a Neuroimaging Consultant PhD. These are highly specialized medical professionals with expertise in interpreting and analyzing brain MRI images and neuroanatomy.

4. Adjudication Method for the Test Set

• Method: The segmentations of the 3 experts for each MRI were combined using the STAPLE method (Simultaneous Truth and Performance Level Estimation) to produce a single, consolidated segmentation. This method statistically estimates a consensus segmentation from multiple expert annotations, weighting each expert's contribution based on their estimated accuracy.

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

• Was it done?: No, an MRMC comparative effectiveness study involving human readers assisting with or without AI was not reported. The study focused on the standalone performance of the AI algorithm against expert manual segmentations (ground truth). The document states, "The subject device (GyriCalc) and the predicate device (NeuroQuant) have the same automated quality control functions. Results must be reviewed by a qualified clinician." This indicates the device is intended as a support tool, implying a human-in-the-loop scenario, but the reported study does not compare human performance with and without the AI.
• Effect Size of Human Improvement with AI vs. Without AI Assistance: Not applicable, as this type of study was not conducted or reported.

6. Standalone (Algorithm Only) Performance Study

• Was it done?: Yes. The entire clinical performance assessment detailed in section 9.2 focuses on the standalone performance of the GyriCalc algorithm by comparing its output (segmentations and measurements) directly against expert-derived ground truth.

7. Type of Ground Truth Used

• Type: Expert Consensus Segmentation.
  • For each brain MRI, three experts independently annotated and edited pre-loaded segmentations.
  • These three expert segmentations were then combined using the STAPLE method to create a single, consolidated reference standard (ground truth).
  • Reference measurements (volume, surface area, gyrification index) were then derived from this combined segmentation.

8. Sample Size for the Training Set

• Sample Size: The document does not specify the sample size used for the training set. It only describes the test set used for performance validation.

9. How Ground Truth for Training Set Was Established

• Method: The document does not explicitly state how the ground truth for the training set was established. It describes the ground truth establishment for the test set (expert consensus via STAPLE). For AI/deep learning models, training data often uses similar or less rigorous ground truth methods, or sometimes pseudo-labeling, but this information is not provided in the supplied text.

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