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.

Device Description

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.

AI/ML Overview

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:

Acceptance Criteria	Reported Device Performance
Accuracy and Robustness of Brain Regions Segmentation (Dice Coefficient)
≥ 0.75 for major subcortical brain structures	Average Dice coefficients ranged from 0.89 to 0.96 for major subcortical brain structures.
≥ 0.8 for major cortical brain structures	Average Dice coefficients ranged from 0.79 to 0.93 for major cortical brain structures.
Reproducibility of Brain Region Segmentation (Maximum Absolute Volume Difference)
Maximum absolute volume difference below 7%	Mean absolute volume difference of 2.1% across major cortical and subcortical brain structures, with individual structures ranging from 1.2% to 3.9%.

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").

Summary

FDA 510(k) Clearance Letter - ANDI 2.0

Page 1

U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov

Doc ID # 04017.08.00

October 22, 2025

Imeka Solutions Inc.
Valerie Lacroix
COO, Executive Vice President
195, Belvédère Nord #201
Sherbrooke, QC J1H 4A7
Canada

Re: K252298
Trade/Device Name: ANDI 2.0
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical image management and processing system
Regulatory Class: Class II
Product Code: QIH, LLZ
Dated: July 23, 2025
Received: September 30, 2025

Dear Valerie Lacroix:

We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

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K252298 - Valerie Lacroix
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Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).

Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.

All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.

For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-

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K252298 - Valerie Lacroix
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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Daniel M. Krainak, Ph.D.
Assistant Director
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health

Enclosure

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Indications for Use

Please type in the marketing application/submission number, if it is known. This textbox will be left blank for original applications/submissions. K252298

Please provide the device trade name(s).
ANDI 2.0

Please provide your Indications for Use below.

Please select the types of uses (select one or both, as applicable).
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary

K252298

Sponsor Information:

IMEKA Solutions, Inc.
195, Belvédère Nord
Sherbrooke, QC, Canada
J1H 4A7

Contact Person: Valerie Lacroix
COO, Executive Vice President
(888) 311-0599
valerie.lacroix@imeka.ca

Date of Summary: September 26, 2025

Device Name and Classification

Common or Usual Name: Automated Radiological Image Processing Software
Proprietary Name: ANDI 2.0
Classification Name: System, Image Processing, Radiological Picture Archiving and Communications System (21 CFR § 892.2050)
Classification Product Code: QIH, LLZ
Predicate Device: ANDI (K230913)
Reference Device: Neurophet AQUA (K220437)

Description of Device

Indications for Use

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Comparative Data for Determining Substantial Equivalence of New Device to Predicate Device

The subject device is a modified version of the predicate device that introduces technological enhancements, including an optional AI-based method to enhance segmentation and additional quantification outputs. Despite these updates, the subject and predicate devices remain substantially equivalent in terms of intended use, general functionality, and technological characteristics.

Information provided in this 510(k) submission shows that ANDI is substantially equivalent to the predicate device, cleared under K230913, in terms of intended use, indications for use, physical characteristics, workflow, anatomical location, and technological characteristics.

While the subject device incorporates minor technical enhancements compared to the predicate device, these differences do not affect the intended use or indications and do not introduce new or increased clinical risks.

Volumetric Grey Matter Quantification: The subject device provides volumetric quantification of segmented grey matter as part of its report output. While the predicate device performs grey matter segmentation, it does not present grey matter volume as a reportable output. This enhancement is a minor technical variation that does not alter the clinical function or risk profile of the device.
Expanded Normative Database Comparison: Both devices compare outputs against a control population (normative reference). In addition to the white matter measures of the predicate device, the subject device's normative reference includes cortical and subcortical brain structures volumes. All measures included in the normative reference are derived from the same source. This represents an extension of an existing functionality that does not impact the device's intended use or safety when compared to the predicate device. Comparative studies demonstrate that the expanded normative reference maintains equivalent performance to the predicate device while providing enhanced clinical information through additional brain structure measurements.
Use of AI for Grey Matter Segmentation: While both devices utilize intensity-based segmentation for white matter, grey matter, and CSF, the subject device employs an AI algorithm for grey matter segmentation to support volumetric quantification. This is the most notable difference, yet it does not alter the device's intended use or raise new safety concerns. The potential risk remains dependent on segmentation performance, and performance testing of the proposed device achieves accurate and reproducible results. This feature has been evaluated by comparison to a reference device (Neurophet AQUA v2, K220437) to validate the performance of the output segmentation for the subject device using the same methodology as the reference device.

Comparative analyses between the subject and predicate devices, including detailed assessments of intended use, technological characteristics, and performance specifications, have been conducted as part

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of this 510(k) submission. These comparisons, together with the results from design verification and validation activities, including bench testing, software validation, and risk analysis, demonstrate that the subject device performs as intended and does not raise new questions of safety or effectiveness. Collectively, the evidence supports the substantial equivalence of the subject device to the predicate device.

Non-Clinical Test Summary

Non-clinical performance testing has been performed in compliance with the following International and FDA recognized consensus standards and FDA guidance document:

ISO 14971:2019, Medical Devices – Application of Risk Management to Medical Devices
ANSI/AAMI/IEC 62304:2006/A1:2016, Medical Device Software - Software Life Cycle Processes
Digital Imaging and Communications in Medicine (DICOM) Set (NEMA PS 3.1 - 3.20)
Guidance for Industry and FDA Staff – Guidance for the Content of Premarket Submissions for Device Software Functions, June 2023
Guidance for Industry and FDA Staff – Guidance for Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions, September 2023

Performance testing was conducted to evaluate key endpoints, including the accuracy of the AI-based brain extraction, the accuracy and robustness of the brain regions segmentation and the overall robustness of the processing pipeline. A summative evaluation was also performed to assess the completeness, clarity, and structure of the output report.

AI / ML module development

The device incorporates a pretrained third-party brain segmentation algorithm as detailed in the SBOM. The algorithm was subjected to training using 140 representative subjects from 3 MRI manufacturers. Validation data included 747 independent subjects from multiple sites and MRI manufacturers. All subjects were selected a priori and secluded in a separate dataset to avoid any contamination.

AI / ML performance data

Performance testing of the brain segmentation algorithm as integrated in the ANDI device was performed on images preprocessed by ANDI, ensuring data independence since ANDI-preprocessed images were not available for the training of the algorithm by the third-party algorithm.

The accuracy and robustness of the brain regions segmentation was evaluated using a total of 71 subjects representative of USA population (n=35 females; n=36 males; age range 18-86 yo; n=38 USA origin), including healthy (n=35) and diseased subjects (Multiple Sclerosis (n=11); Parkinson's disease (n=12); Alzheimer's disease (n=12); mild cognitive impairment (n=1)). Results stratified by age, gender, pathology, MRI manufacturer and field strength were also provided. The reproducibility of the brain regions segmentation was evaluated using 2 timepoints from 59 subjects (n=30 females; n=29 males; age range 23-86 yo; n=38 USA origin). Only healthy subjects were selected for the reproducibility assessment to avoid any bias from disease progression. The brain segmentation performance testing datasets included MPRAGE images acquired on a variety of MRI scanner models from the three main manufacturers (GE, Philips and Siemens).

To constitute a ground truth of brain segmentations, 71 preprocessed T1 images were pre-segmented using Freesurfer v7.4.1. The resulting segmentations were then manually corrected by an expert and approved by a panel of 3 board certified neuroradiologists. Dice coefficients were computed between

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ANDI 2.0's brain regions segmentation and the expert approved ground truth to assess the accuracy and robustness. Average Dice coefficients ranged from 0.89 to 0.96 for major subcortical brain structures and from 0.79 to 0.93 for major cortical brain structures, which are higher than their respective acceptance criteria of ≥ 0.75 and ≥ 0.8.

The reproducibility of brain region segmentation was evaluated by calculating absolute volume differences between two scanning timepoints for identical subjects. Analysis of major cortical and subcortical brain structures yielded a mean absolute volume difference of 2.1%, with individual structures ranging from 1.2% to 3.9%. These findings satisfy the predetermined acceptance criterion of maximum absolute volume difference below 7%, thereby confirming adequate reproducibility of the segmentation methodology for clinical application.

Software verification and validation activities were carried out in accordance with pre-defined test protocols, established prior to testing. All test results were reviewed and approved by qualified technical personnel to confirm that the software meets all specified system requirements and functional expectations before proceeding to finalization.

Through the performance test, it was confirmed that ANDI meets all performance test criteria and that all functions work as intended. Test results support the conclusion that device performance satisfies the design intent and is equivalent to its predicate device.

Clinical Test Summary

No clinical studies were considered necessary and performed.

Conclusion

In conclusion, the tests conducted, as well as all verification and validation activities, demonstrate that the design specifications and technological characteristics of ANDI meet applicable requirements and standards for the safety and effectiveness of the device for its intended use. There are differences in technological characteristics between the predicates and the proposed device, but the nature of those differences does not raise new or different questions of safety or effectiveness as compared to the predicate devices. Therefore, ANDI is substantially equivalent to the currently marketed predicate device.

Regulation Number and Section

§ 892.2050 Medical image management and processing system.

(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).