Neuro Insight V1.0 is an image processing solution. It is intended to assist appropriately trained medical professionals in their analysis workflow on neurological MRI images.

Neuro Insight V1.0 is composed of two subsets, including an image processing application package (NeuroPro) and an optional user interface (Neuro Synchronizer).

NeuroPro is an image processing application package that computes maps, extracts and communicates metrics which are to be used in the analysis of multiphase or monophase neurological MR images.

NeuroPro can be integrated and deployed through technical integration environment, responsible for transferring, storing, converting formats and displaying of DICOM imaging data.

Neuro Synchronizer is an optional dedicated interface allowing the viewing, manipulation, and comparison of neurological medical imaging and/or multiple time-points, including post-processing results provided by NeuroPro or any other results from compatible processing applications.

Neuro Synchronizer is a medical image management application intended to enable the user to edit and modify parameters that are optional inputs of aforementioned applications. These modified parameters are provided through the technical integration environment as inputs to the application to reprocess outputs. If necessary, Neuro Synchronizer provides the user with the option to validate the information.

Neuro Synchronizer can be integrated in compatible technical integration environments.

The device does not alter the original medical image. Neuro Insight V1.0 is not intended to be used as a standalone diagnostic device and should not be used as the sole basis for patient management decisions. The results of Neuro Insight V1.0 are intended to be used in conjunction with other patient information and based on professional judgment to assist with reading and interpretation of medical images. Users are responsible for viewing full images per the standard of care.

Device Description

Neuro Insight (NEU_INS_MM) V1.0 product is a neurological image analysis solution, composed of several image processing applications and optional visualization and manipulation features.

Neuro Insight V1.0 is composed of two subsets:

NeuroPro (NEU_PRO_MR) as an image application package, responsible for the processing of specific neurological MR Images.
Neuro Synchronizer (NEU_HMI_MM) as an optional image analysis environment, that provides the user interface which has visualization and manipulation tools and allows the user to edit the parameters of compatible applications.

Neuro Insight does not alter the original medical image and is not intended to be used as a diagnostic device.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study details for the Neuro Insight V1.0 device, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes a validation study comparing Neuro Insight V1.0 to the predicate device, Olea Sphere® V3.0, focusing on parametric maps computation and co-registration.

Feature Evaluated	Acceptance Criteria / Performance Goal	Reported Device Performance (Neuro Insight V1.0)
Parametric Maps Computation	Statistical and/or visual analysis supports substantial equivalence to Olea Sphere® V3.0 for ADC, CBF, CBV, CBV_Corr, K2, MTT, TTP, Tmax/Delay, tMIP.	Met: For each DWI and DSC parametric map, the statistical and/or visual analysis of results derived from comparison with Olea Sphere® V3.0 supported substantial equivalence.
Intra- and Inter-exam Co-registration (FLAIR-DWI, FLAIR-DSC, FLAIR-T1, FLAIR-T1g, FLAIR-T2, FLAIR-follow-up FLAIR)	All 6 co-registrations provided by Neuro Insight V1.0 are considered acceptable for reading and interpretation.	Met: Visual analysis reported that all 6 co-registrations provided by Neuro Insight V1.0 were considered acceptable for reading and interpretation by the experts.
Brain Extraction Tool (BET) - Deep Learning Algorithm (for spatial overlap)	Average DICE coefficient of 0.95	Met: Achieved an average DICE coefficient of 0.97 (ranging from 0.907 to 0.988), exceeding the predetermined acceptance threshold of 0.95.

2. Sample Size Used for the Test Set and Data Provenance

Parametric Maps & Co-registration Study:
- Sample Size:
  - Parametric maps: 30 anonymized brain MRI cases
  - Co-registration: 60 anonymized brain MRI cases
- Data Provenance: Not explicitly stated as retrospective or prospective, or country of origin for these specific comparison studies. However, the BET deep learning algorithm training and testing data mentions sourcing from multiple MRI system manufacturers (GE Healthcare, Siemens, Philips, Canon/Toshiba) implying a diverse, likely multi-center, dataset. Given the anonymization and comparison with a predicate, it's highly likely this was a retrospective study.
Brain Extraction Tool (BET) Validation (Deep Learning):
- Test Set Sample Size: 100 cases
- Data Provenance: Sourced to ensure broad representativeness depending on manufacturer, magnetic field, acquisition parameters, origin, patient age and sex. Cases collected from multiple MRI system manufacturers (GE Healthcare, Siemens, Philips, Canon/Toshiba) and varying magnetic fields (1.5T, 3T). Patients included 51% male, 43% female, with varied age (mean age 60 years, range 14 to 100 years for available data). This suggests diverse origin, likely global or at least multi-site.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

Parametric Maps & Co-registration Study:
- Number of Experts: Three (3)
- Qualifications: US board-certified neuroradiologists.
Brain Extraction Tool (BET) Validation (Deep Learning):
- Number of Experts: Expert clinicians performed manual segmentation, following criteria defined by a US board-certified neuroradiologist. Each segmentation was reviewed by a neuroradiologist and a research engineer.

4. Adjudication Method for the Test Set

Parametric Maps & Co-registration Study: The document states that the comparison was done "by three US board-certified neuroradiologists." For the parametric maps, it involved "statistical and/or visual analysis of the results." For co-registration, it was "visual analysis." This implies a consensus or agreement among the three experts, but a specific adjudication method (e.g., majority vote, independent review with arbitration) is not explicitly detailed.
Brain Extraction Tool (BET) Validation (Deep Learning): Manual segmentation was "reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy across the dataset." This suggests a review process, but not a specific adjudication method like 2+1.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No, a traditional MRMC comparative effectiveness study aiming to quantify the improvement of human readers with AI vs. without AI assistance was not explicitly described.
- The studies focused on the substantial equivalence of the device's output to a predicate (for parametric maps) and the acceptability of the device's output (for co-registration), as evaluated by human readers. It also validated the performance of the deep learning algorithm (BET) against expert-annotated ground truth. These are standalone evaluations of the device's output, not human-in-the-loop performance studies.

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

Yes, a standalone evaluation of the deep learning Brain Extraction Tool (BET) algorithm was performed.
- The algorithm's performance was assessed by comparing its automated segmentations to expert-annotated ground truth masks. The metric used was the DICE coefficient, which is a common measure of spatial overlap for segmentation tasks. This is a direct measure of the algorithm's performance without a human in the loop for the actual output generation being assessed.

7. The Type of Ground Truth Used

Parametric Maps & Co-registration Study:
- Predicate Device Output: For parametric maps, the ground truth was effectively the results generated by the predicate device (Olea Sphere® V3.0), against which Neuro Insight V1.0's outputs were compared.
- Expert Visual Assessment: For co-registration, the ground truth was based on the "acceptable for reading and interpretation" visual assessment by three US board-certified neuroradiologists.
Brain Extraction Tool (BET) Validation (Deep Learning):
- Expert Consensus/Manual Annotation: Ground truth brain masks were created by "experienced clinicians following a standardized annotation protocol defined by a U.S. board-certified neuroradiologist." Each segmentation was "reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy." This strongly indicates expert consensus / manual annotation.

8. The Sample Size for the Training Set

Brain Extraction Tool (BET) Validation (Deep Learning):
- Training Set Sample Size: 199 cases
- Validation Set Sample Size: 63 cases (used for model tuning during development)

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

Brain Extraction Tool (BET) Validation (Deep Learning):
- Ground truth brain masks were created specifically by "experienced clinicians following a standardized annotation protocol defined by a U.S. board-certified neuroradiologist."
- The protocol included all brain structures (hemispheres and lesions) while explicitly excluding non-brain anatomical elements (skull, eyeballs, optic nerves).
- Each segmentation was "reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy across the dataset." This method of establishing ground truth for the training set is consistent with the test set and involves expert consensus/manual annotation and review.

Summary

FDA 510(k) Clearance Letter - Neuro Insight V1.0

Page 1

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

Doc ID # 04017.08.00

July 23, 2025

Olea Medical S.A.S.
℅ John J. Smith
Partner
Hogan Lovells US LLP
Columbia Square 555 Thirteenth Street, NW
Washington, District of Columbia 20004

Re: K243681
Trade/Device Name: Neuro Insight V1.0
Regulation Number: 21 CFR 892.2050
Regulation Name: Medical Image Management And Processing System
Regulatory Class: Class II
Product Code: LLZ
Dated: July 7, 2025
Received: July 7, 2025

Dear John J. Smith:

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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K243681 - John J. Smith Page 2

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.

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K243681 - John J. Smith Page 3

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-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,

Ningzhi Li -S [Digitally signed by Ningzhi Li -S]

for
Daniel M. Krainak, Ph.D.
Assistant Director
Magnetic Resonance and Nuclear Medicine Team
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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FORM FDA 3881 (6/20) Page 1 of 1

DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration

Indications for Use

Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below

Submission Number (if known): K243681
Device Name: Neuro Insight V1.0

Indications for Use (Describe)

Neuro Insight V1.0 is an image processing solution. It is intended to assist appropriately trained medical professionals in their analysis workflow on neurological MRI images.

Neuro Insight V1.0 is composed of two subsets, including an image processing application package (NeuroPro) and an optional user interface (Neuro Synchronizer).

NeuroPro is an image processing application package that computes maps, extracts and communicates metrics which are to be used in the analysis of multiphase or monophase neurological MR images.

NeuroPro can be integrated and deployed through technical integration environment, responsible for transferring, storing, converting formats and displaying of DICOM imaging data.

Neuro Synchronizer can be integrated in compatible technical integration environments.

Type of Use (Select one or both, as applicable)

☒ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

This section applies only to requirements of the Paperwork Reduction Act of 1995.
DO NOT SEND YOUR COMPLETED FORM TO THE PRA STAFF EMAIL ADDRESS BELOW.

The burden time for this collection of information is estimated to average 79 hours per response, including the time to review instructions, search existing data sources, gather and maintain the data needed and complete and review the collection of information. Send comments regarding this burden estimate or any other aspect of this information collection, including suggestions for reducing this burden, to:

Department of Health and Human Services
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Office of Chief Information Officer
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PRAStaff@fda.hhs.gov

"An agency may not conduct or sponsor, and a person is not required to respond to, a collection of information unless it displays a currently valid OMB number"

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510(K) SUMMARY

Olea Medical's Neuro Insight V1.0

(K243681)

Submitter

Olea Medical
93 avenue des Sorbiers, ZI ATHELIA IV
13600, La Ciotat
France
Phone: +33 4 42 71 24 20
Facsimile: +33 4 42 71 24 27
Contact Person: Nathalie Palumbo

Date Prepared: July 22, 2025

Device Identification

Name of Device: Neuro Insight V1.0
Common Name: Medical image management and processing system
Classification Name: System, Image Processing, Radiological
Regulation No: 892.2050
Product Code: LLZ

Legally Marketed Predicate Device

510(k): K152602
Trade Name: Olea Sphere V3.0
Manufacturer: Olea Medical
Product Code: LLZ

Legally Marketed Reference Devices

510(k): K223502
Trade Name: MR Diffusion Perfusion Mismatch V1.0
Manufacturer: Olea Medical
Product Code: LLZ

510(k): K223532
Trade Name: Olea S.I.A Neurovascular V1.0
Manufacturer: Olea Medical
Product Code: LLZ

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Device Description Summary

Neuro Insight (NEU_INS_MM) V1.0 product is a neurological image analysis solution, composed of several image processing applications and optional visualization and manipulation features.

Neuro Insight V1.0 is composed of two subsets:

NeuroPro (NEU_PRO_MR) as an image application package, responsible for the processing of specific neurological MR Images.
Neuro Synchronizer (NEU_HMI_MM) as an optional image analysis environment, that provides the user interface which has visualization and manipulation tools and allows the user to edit the parameters of compatible applications.

Neuro Insight does not alter the original medical image and is not intended to be used as a diagnostic device.

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Intended Use / Indications for Use

Neuro Insight V1.0 is an image processing solution. It is intended to assist appropriately trained medical professionals in their analysis workflow on neurological MRI images.

Neuro Insight V1.0 is composed of two subsets, including an image processing application package (NeuroPro) and an optional user interface (Neuro Synchronizer).

NeuroPro is an image processing application package that computes maps, extracts and communicates metrics which are to be used in the analysis of multiphase or monophase neurological MR images.

NeuroPro can be integrated and deployed through technical integration environment, responsible for transferring, storing, converting formats and displaying of DICOM imaging data.

Neuro Synchronizer can be integrated in compatible technical integration environments.

Indications for Use Comparison

Both Neuro Insight V1.0 and Olea Sphere® V3.0 are user-defined software analysis tools used for the analysis of magnetic resonance imaging (MRI) studies. Both devices are intended for use in Clinical/Hospital Environment, by any trained professional. Importantly, neither software product is used for diagnosis. Patient management decisions should not be based solely on the results of either software.

The minor difference in the indications for use between the two devices is that Neuro Insight V1.0 subject device provides processing capabilities for MR only whereas Olea Sphere® V3.0 predicate device provides processing capabilities for both MR and CT.

Neuro Insight V1.0 subject device represents a subset of Olea Sphere® V3.0 predicate device. Therefore, the indication for use of Neuro Insight V1.0 is considered substantially equivalent to Diffusion, Perfusion, Olea Vision, Analysis and Longitudinal Analysis Mono modules of Olea Sphere® V3.0.

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Technological Comparison

Both Neuro Insight V1.0 and Olea Sphere V3.0 have similar technological characteristics as they both:

provide processing capabilities for the analysis of FLAIR, T1, T2, T1g, diffusion and DSC perfusion series;
are designed to be able to process FLAIR, T1, T2, T1g, diffusion and DSC perfusion series;
are able to provide the same outputs for FLAIR, T1, T2, T1g, diffusion and DSC perfusion metrics (CBV, CBF, MTT, TTP, Tmax/Delay, CBV_corr, K2, tMIP);
provide the same standard viewing tools and edition of parameters that are optional inputs of compatible docker applications. Currently, with NeuroPro being the only compatible application, the only editable parameter is arterial input function (AIF) selection. As with the FDA-cleared Olea S.I.A Neurovascular V1.0 (K223532), the user can adjust the AIF automatically calculated by the application (by NeuroPro) as needed and request a recalculation of the results using the desired value. The implementation and management of this feature are exactly the same as in Olea S.I.A Neurovascular V1.0.

The minor differences in the technological characteristics between the two devices are:

Neuro Insight V1.0 uses a motion correction algorithm based on a 3D rigid method before DSC perfusion maps computation, while Olea Sphere® V3.0 uses a 2D rigid motion correction algorithm. This difference does not impact the calculation method of the outputs. This motion correction algorithm based on a 3D rigid method has already been cleared as a component of MR Diffusion Perfusion Mismatch V1.0 (Olea Medical, France), FDA-cleared (K223502).
Neuro Insight V1.0 uses a brain extraction tool (BET) based on a 3D deep learning algorithm before DSC perfusion maps computation, while Olea Sphere® V3.0 uses thresholds based on a histogram analysis of the MR images. For more information about this deep learning testing and validation process please refer to "About Deep learning algorithm for BET" below. This difference does not impact the calculation method of the outputs.
Neuro Insight V1.0 uses co-registration algorithms based on 3D rigid 6-dof transformation, while Olea Sphere® V3.0 uses IPP IOP DICOM tags to locate and align volumes.

However, these minor differences in the technological characteristics between both devices do not raise different questions of safety and effectiveness.

About Deep learning algorithm for BET

To filter out the brain and the cerebrospinal fluid (CSF), surrounding noise and background pixels on raw images, MR DSC Perfusion includes a brain extraction algorithm for MR PWI series. This brain extraction algorithm uses a 3D U-Net architecture, consisting of 6 layers (16, 32, 64, 128, 256, 512), and was optimized using both DICE and Tversky metrics. It was trained with 362 manually annotated MR volumes split in train (199 cases), validation (63 cases) and test (100 cases) dataset.

The database was sourced to ensure broad representativeness depending on the manufacturer, magnetic field, acquisition parameters, origin, patient age and sex. The manual segmentation was performed by expert clinicians following criteria defined by a US board certified neuroradiologist. Two labels were used: one for the brain tissue and one for the voxels who do not belong to brain tissue (background).

Cases were collected from multiple MRI system manufacturers: 114 cases from GE Healthcare (48% at 1.5T, 52% at 3T), 127 from Siemens (64% at 1.5T, 36% at 3T), 95 from Philips (70% at 1.5T, 30% at 3T), 25 from Canon formerly Toshiba (60% at 1.5T, 40% at 3T), and one case with unknown manufacturer information due to severe DICOM anonymization.

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Patient demographics comprised 51% male subjects, 43% female subjects, with 6% lacking gender information due to DICOM anonymization protocols. Age information was limited due to anonymization processes, with over half of the dataset missing age data. Among available cases, the mean age was 60 years with a range spanning from 14 to 100 years, representing a diverse patient population across different age groups.

Ground truth brain masks were created by experienced clinicians following a standardized annotation protocol defined by a U.S. board-certified neuroradiologist. The protocol included all brain structures—hemispheres and lesions—while explicitly excluding non-brain anatomical elements such as the skull, eyeballs, and optic nerves. Each segmentation was reviewed by a neuroradiologist and a research engineer to ensure consistency and accuracy across the dataset.

A 5-fold cross-validation procedure was applied to assess performance stability and generalizability across different scanner types and patient profiles.

The achieved average DICE coefficient of 0.97, ranging from 0.907 to 0.988, exceeds the predetermined acceptance threshold of 0.95. These results demonstrate excellent spatial overlap between automated segmentations and expert-annotated ground truth masks, as well as high segmentation accuracy and consistent reliability across diverse clinical scenarios.

These conclusions confirm the algorithm's robustness and reliability, enabling accurate and reproducible brain extraction as part of automated neuroimaging workflows.

Non-Clinical and/or Clinical Tests Summary & Conclusions

Olea Medical has conducted validation testing of the Neuro Insight V1.0. Internal verification, usability and validation testing confirms that the product specifications are met, and support of the substantial equivalence of the intended use and technological characteristics to the predicate device.

Neuro Insight V1.0 has been validated to ensure that the system, as a whole, provides all the capabilities necessary to operate according to its intended use and in a manner substantially equivalent to the predicate device.

The following performance evaluations were conducted:

Product risk assessment;
Software modules verification tests;
Usability assessment;
Software validation test.

All software features of Neuro Insight V1.0 have undergone verification and usability testing to ensure proper performance and safe integration into clinical workflows. Specific attention was given to features identified through risk analysis, which were evaluated with consideration of human factors. Testing was performed in alignment with the intended use and user instructions. No issues were reported by operators during testing, and no clinically significant failures or incidents occurred.

Based on the performance testing, the Neuro Insight V1.0 has a safety and effectiveness profile that is similar to the predicate device.

Neuro Insight V1.0 was quantitatively and qualitatively compared to Olea Sphere® V3.0 software (Olea Medical®, FDA-cleared - K152602), identified as the predicate device.

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Two main features were evaluated:

Parametric maps computation: ADC, CBF, CBV, CBV_Corr, K2, MTT, TTP, Tmax/Delay, tMIP;

Neuro Insight V1.0 and Olea Sphere® V3.0 were quantitatively and qualitatively compared by three US board-certified neuroradiologists using 30 anonymized brain MRI cases. For each DWI and DSC parametric maps, the statistical and/or visual analysis of the results derived from this comparison supported the substantial equivalence of performance between Neuro Insight V1.0 subject device and Olea Sphere® V3.0 predicate device.

Intra- and inter-exam co-registration: FLAIR-DWI, FLAIR-DSC, FLAIR-T1, FLAIR-T1g, FLAIR-T2, and FLAIR-follow-up FLAIR.

Neuro Insight V1.0 was qualitatively assessed by the three US board-certified neuroradiologists using 60 anonymized brain MRI cases. The visual analysis reported that all 6 co-registrations (FLAIR-DWI, FLAIR-DSC, FLAIR-T1, FLAIR-T1g, FLAIR-T2, FLAIR-follow-up FLAIR) provided by Neuro Insight V1.0 subject device were considered as acceptable for reading and interpretation.

Neuro Insight V1.0 subject device has substantially equivalent indications for use, technological characteristics, and principles of operation as Olea Sphere V3.0 predicate device. The minor technological differences between Neuro Insight V1.0 and its predicate device raise no new questions of safety or effectiveness, as the user confirmation feature is included in the reference device which was cleared for the same intended use. The methods for verification and validation testing of the subject device are well-supported in this regulation and by the predicate and reference devices' clearances, and data from such testing demonstrates the device's safety and performance.

Thus, Neuro Insight V1.0 is substantially equivalent to predicate Olea Sphere V3.0.

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).