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Neurophet AQUA AD Plus is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures and lesions, as well as SUVR quantification from a set of MR and PET images. Volumetric measurements may be compared to reference percentile data.

Neurophet AQUA AD Plus is a software device intended for the automatic labeling of brain structures, visualization, and volumetric quantification of segmented brain regions and lesions, as well as standardized uptake value ratio (SUVR) quantification using MR and PET images. The volumetric outcomes are compared to normative reference data to support the evaluation of neurodegeneration and cognitive impairment.

The device is designed to assist physicians in clinical evaluation by streamlining the clinical workflow from patient registration through image analysis, analysis result archiving, and report generation using software-based functionalities. The device provides percentile-based results by comparing an individual's imaging-derived quantitative analysis results to reference populations. Percentile-based results are provided for reference only and are not intended to serve as a standalone basis for diagnostic decision-making. Clinical interpretation must be performed by qualified healthcare professionals.

Here's a breakdown of the acceptance criteria and study details for the Neurophet AQUA AD Plus, based on the provided FDA 510(k) Clearance Letter:

Acceptance Criteria and Device Performance for Neurophet AQUA AD Plus

The Neurophet AQUA AD Plus employs multiple AI modules for automated segmentation and quantitative analysis of brain structures and lesions using MR and PET images. The device's performance was validated against predefined acceptance criteria for each module.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance for the Test Set

• T1-SegEngine (Accuracy): 60 independent T1-weighted MRI cases. Data provenance not explicitly stated, but implicitly from public repositories (e.g., ADNI, AIBL, PPMI) and institutional clinical sites as mentioned for training data, and distinct from training.
• T1-SegEngine (Reproducibility): 60 subjects with paired T1-weighted scans (120 scans total). Data provenance not explicitly stated.
• FLAIR-SegEngine (Accuracy): 136 independent T2-FLAIR cases. Data provenance not explicitly stated, but distinct from training data.
• FLAIR-SegEngine (Reproducibility): Paired T2-FLAIR scans (number not specified). Data provenance not explicitly stated.
• PET-Engine (SUVR accuracy): 30 paired MRI–PET datasets. Data provenance not explicitly stated, but implicitly from multi-center studies including varied tracers and sites.
• PET-Engine (Centiloid classification): 176 paired T1-weighted MRI and amyloid PET scans from ADNI and AIBL. These are public repositories, likely involving diverse geographical data (e.g., USA, Australia). Data is retrospective.
• ED-SegEngine (Accuracy): 100 T2-FLAIR scans collected from U.S. and U.K. clinical sites. Data is retrospective.
• HEM-SegEngine (Accuracy): 106 GRE/SWI scans from U.S. clinical sites. Data is retrospective.

For all modules, validation datasets were fully independent from training datasets at the subject level, drawn from distinct sites and/or repositories where applicable.
The validation cohorts covered adult subjects across a broad age range (approximately 40–80+ years), with both females and males represented.
Racial/ethnic composition included White, Asian, Black, and African American subjects, depending on the underlying public and institutional datasets.
Clinical subgroups included clinically normal, mild cognitive impairment, and Alzheimer's disease for structural, FLAIR, and PET modules, and cerebrovascular/amyloid‑related pathologies for ED‑ and HEM‑SegEngines.

3. Number of Experts and Qualifications for Ground Truth

For structural and lesion segmentation modules (T1-, FLAIR-, ED-, HEM-SegEngines):

• Number of Experts: Not explicitly stated as a specific number, but "subspecialty-trained neuroradiologists" were used.
• Qualifications: "Subspecialty-trained neuroradiologists." Specific years of experience are not mentioned.

For Centiloid classification in the PET-Engine:

• Number of Experts: "Consensus expert visual reads." The exact number isn't specified, but implies multiple experts.
• Qualifications: "Experts" trained in established amyloid PET reading criteria. Specific qualifications beyond "expert" and training in criteria are not detailed.

4. Adjudication Method for the Test Set

For structural and lesion segmentation modules (T1-, FLAIR-, ED-, HEM-SegEngines):

• "Consensus/adjudication procedures and internal quality control to ensure consistency" were used for establishing reference segmentations. The specific 2+1, 3+1, or other detailed method is not provided.

For Centiloid classification in the PET-Engine:

• "Consensus expert visual interpretation" was used. The specific method details are not provided.

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

The provided text does not indicate that an MRMC comparative effectiveness study was done to compare human readers with AI assistance versus without AI assistance. The performance studies primarily focus on the standalone (algorithm-only) performance of the device against expert-derived ground truth or a cleared reference product.

6. Standalone (Algorithm-Only) Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done for all AI modules. The text explicitly states: "Standalone performance tests were conducted for each module using validation datasets that were completely independent from those used for model development and training." The results presented in the table above reflect this standalone performance.

7. Type of Ground Truth Used

• Expert Consensus:
  • For structural and lesion segmentation modules (T1-, FLAIR-, ED-, HEM-SegEngines), reference segmentations were generated by "subspecialty-trained neuroradiologists using predefined anatomical and lesion‑labeling criteria, with consensus/adjudication procedures."
  • For Centiloid classification in the PET-Engine, reference labels were derived from "consensus expert visual interpretation using established amyloid PET reading criteria."
• Comparison to Cleared Reference Product:
  • For SUVR quantification in the PET-Engine, reference values were obtained from an "FDA‑cleared reference product (K221405)" (Neurophet SCALE PET).

8. Sample Size for the Training Set

The exact sample size for the training set is not explicitly stated as a single number. However, the document mentions:

• "The AI-based modules (T1‑SegEngine, FLAIR‑SegEngine, PET‑Engine, ED‑SegEngine, HEM‑SegEngine) were trained using multi-center MRI and PET datasets collected from public repositories (e.g., ADNI, AIBL, PPMI) and institutional clinical sites."
• "Training data covered:
  • Adult subjects across a broad age range (approximately 20–80+ years), with both sexes represented and including multiple racial/ethnic groups (e.g., White, Asian, Black).
  • A spectrum of clinical conditions relevant to the intended use, including clinically normal, mild cognitive impairment, and Alzheimer's disease, as well as patients with cerebrovascular and amyloid‑related pathologies for lesion-segmentation modules.
  • MRI acquired on major vendor platforms (GE, Siemens, Philips) at 1.5T and 3T... and amyloid PET acquired on multiple PET systems with commonly used tracers (Amyvid, Neuraceq, Vizamyl)."

This indicates a large and diverse training set, although a precise count of subjects or images isn't provided.

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

The document implies that the training data included "manual labels" as it states: "No images or manual labels from the training datasets were reused in the validation datasets." However, it does not explicitly detail the process by which these "manual labels" or ground truth for the training set were established (e.g., number of experts, qualifications, adjudication method for training data). It's reasonable to infer that similar expert-driven processes were likely used for training ground truth as for validation, but this is not explicitly confirmed in the provided text.

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