(122 days)
Not Found
Yes
The device description explicitly mentions the use of "neural network models" and the performance studies section refers to "deep-learning models".
No.
The device is a medical imaging post-processing software that provides quantitative measurements and visualizations of brain structures and lesions. It does not directly provide therapy or treatment.
Yes
The "Intended Use" states that NeuroQuant is for "automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions" and implicitly indicates that these measurements "may be compared to reference percentile data." Furthermore, the "Device Description" clarifies that the output can be used by medical professionals, and "when used clinically, the output must be reviewed by a radiologist or neuroradiologist," implying its use in clinical decision-making, which is characteristic of a diagnostic device.
Yes
The device is described as a "fully automated MR imaging post-processing software medical device" and is packaged as a "computing appliance" that supports DICOM input and output. While it runs on hardware, the device itself is the software that performs the image processing and analysis. The description focuses entirely on the software's functions and capabilities, not on any specific hardware components included as part of the medical device itself.
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- Device Function: NeuroQuant processes MR images of the brain. It does not analyze biological samples taken from the body.
- Intended Use: The intended use is for automatic labeling, visualization, and volumetric quantification of brain structures and lesions from MR images. This is image analysis, not in vitro testing.
Therefore, NeuroQuant falls under the category of a medical device that processes medical images, not an In Vitro Diagnostic device.
No
The letter does not state that the FDA has reviewed and approved or cleared a Predetermined Change Control Plan (PCCP) for this specific device. The "Control Plan Authorized (PCCP) and relevant text" section explicitly states "Not Found".
Intended Use / Indications for Use
NeuroQuant is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric measurements may be compared to reference percentile data.
Product codes (comma separated list FDA assigned to the subject device)
QIH, LLZ
Device Description
NeuroQuant is a fully automated MR imaging post-processing software medical device that provides automatic labeling, visualization, and volumetric quantification of brain structures and lesions from a set of MR images and returns segmented images and morphometric reports.
NeuroQuant provides morphometric measurements of brain structures based on a 3D T1 MRI series. The optional use of the T2 FLAIR MR series and T2* GRE/SWI series allows for additional quantification of T2 FLAIR hyperintense lesions and T2* GRE/SWI hypointense lesions.
The device is used by medical professionals in imaging centers, hospitals, and other healthcare facilities as well as by clinical researchers. When used clinically, the output must be reviewed by a radiologist or neuroradiologist. The results are typically forwarded to the referring physician, most commonly a neurologist. The device is a "Prescription Device" and is not intended to be used by patients or other untrained individuals.
From a workflow perspective, the device is packaged as a computing appliance that is capable of supporting DICOM standard input and output. NeuroQuant supports data from all major MRI manufacturers and a variety of field strengths. For best results, scans should be acquired using specified protocols provided by CorTechs Labs.
As part of processing, the data is corrected by NeuroQuant for image acquisition artifacts, including gradient nonlinearities and bias field inhomogeneity, to improve overall image quality.
Next, image baseline intensity levels for gray and white matter are identified and corrected for scanner variability. The scan is then aligned with the internal anatomical atlas by a series of transformations. Probabilistic methods and neural network models are then used to label each voxel with an anatomical structure based on location and signal intensities.
Output of the software provides values as numerical volumes, and images of derived data as grayscale intensity maps and as color overlays on top of the anatomical image. The outputs are provided in standard DICOM format as image series and reports that can be displayed on many commercial DICOM workstations.
The software is designed without the need for a user interface after installation. Any processing errors are reported either in the output series error report or system log files.
The software can provide data on age and gender-matched normative percentiles. The default reference percentile data for NeuroQuant comprises normal population data.
The device provides DICOM Storage capabilities to receive MRI series in DICOM format from an external source, such as an MRI scanner or PACS server. The device provides transient data storage only. If additional scans from other time points are available, the software can perform change analysis.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes (neural network models, deep-learning models)
Input Imaging Modality
MR images (3D T1 MRI, T2 FLAIR MR series, T2* GRE/SWI series)
Anatomical Site
Brain structures and lesions
Indicated Patient Age Range
Not Found
Intended User / Care Setting
Medical professionals in imaging centers, hospitals, and other healthcare facilities, and clinical researchers. Results must be reviewed by a radiologist or neuroradiologist; forwarded to the referring physician (neurologist).
Description of the training set, sample size, data source, and annotation protocol
Brain Segmentation Model:
- Sample size: 1,473 3D T1-weighted MRI series
- Data source: From over 16 institutions, encompassing a wide range of scanner protocols, field strengths, manufacturers, and scanner models.
- Annotation protocol: Not specified.
FLAIR Segmentation Model:
- Sample size: 340 T1 and FLAIR MRI series
- Data source: From 22 institutions, incorporating various scan protocols, MRI scanner models, and field strengths.
- Annotation protocol: Not specified.
MCH Detection Model:
- Sample size: 463 2D T2*GRE/SWI MRI series
- Data source: From over 68 institutions, encompassing a wide range of scanner protocols, manufacturers, and models.
- Annotation protocol: Not specified.
Description of the test set, sample size, data source, and annotation protocol
Brain Segmentation Model:
- Sample size: 30 patients
- Data source: Separate dataset from training set, curated to represent diverse patient population across the United States.
- Annotation protocol: Not specified.
FLAIR Segmentation Model:
- Sample size: 63 patients
- Data source: Test data acquired across Philips, GE, and Siemens scanners. Stratified sampling method for an 80%/20% train/test split. Curated to represent diverse patient population across the United States.
- Annotation protocol: Not specified.
MCH Detection Model:
- Sample size: 117 patients
- Data source: Test data acquired using Philips, GE, and Siemens scanners. Stratified sampling method for an 80%/20% train/test split, with a power analysis conducted to determine the minimum sample size required for statistical significance. Curated to represent diverse patient population across the United States.
- Annotation protocol: Not specified.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Study Type: Verification and Validation tests including objective unit testing, system testing, and clinical validation testing. Performance testing for deep-learning models (Brain Segmentation, FLAIR Lesion Segmentation, MCH Segmentation).
Brain Segmentation Model:
- Sample size: Test set of 30 patients.
- Key Metrics: Dice Similarity Coefficient (DSC)
- Key results: Meets the acceptance criteria for accuracy and reproducibility when evaluated against the predicate device.
FLAIR Segmentation Model:
- Sample size: Test set of 63 patients.
- Key Metrics: Mean Dice Similarity Coefficient (DSC)
- Key results: Achieved a mean DSC of 0.70 with a standard deviation of 0.14, surpassing the acceptance criteria of mean DSC ≥ 0.50 and standard deviation ≤ 0.18.
MCH Detection Model:
- Sample size: Test set of 117 patients.
- Key Metrics: Median F1 Score
- Key results: Achieved a median F1 Score of 0.60, which exceeded the acceptance criteria of ≥ 0.51.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
- Brain Segmentation Model: Dice Similarity Coefficient (DSC)
- FLAIR Segmentation Model: Mean Dice Similarity Coefficient (DSC)
- MCH Detection Model: Median F1 Score
Predicate Device(s): If the device was cleared using the 510(k) pathway, identify the Predicate Device(s) K/DEN number used to claim substantial equivalence and list them here in a comma separated list exactly as they appear in the text. List the primary predicate first in the list.
Reference Device(s): Identify the Reference Device(s) K/DEN number and list them here in a comma separated list exactly as they appear in the text.
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information for the subject device only (e.g. presence / absence, what scope was granted / cleared under the PCCP, any restrictions, etc).
Not Found
§ 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).
0
FDA U.S. FOOD & DRUG
ADMINISTRATION
CorTechs Labs, Inc. Kora Marinkovic VP of Quality and Regulatory Affairs 5060 Shoreham Place CA Ste 240 San Diego, California 92122
August 22, 2024
Re: K241098
Trade/Device Name: NeuroQuant Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH, LLZ Dated: July 22, 2024 Received: July 22, 2024
Dear Kora Marinkovic:
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/cdrb/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.
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).
1
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 820.70) 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 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-reportingcombination-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.
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-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/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-device-advice-comprehensive-regulatoryassistance/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).
Sincerelv.
Douglas W. Fletcher - S
Digitally signed by
for 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 Ouality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
Submission Number (if known)
K241098
Device Name
NeuroQuant
Indications for Use (Describe)
NeuroQuant is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric measurements may be compared to reference percentile data.
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)
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3
Image /page/3/Picture/2 description: The image shows the alphanumeric string "K241098" in a simple, sans-serif font. The text is black against a white background. The string appears to be a code or identifier, possibly a serial number or product key. The characters are evenly spaced and clearly legible.
510(k) Summary: NeuroQuant
1. Submitter
| Name: | CorTechs Labs, Inc | |
|---|---|---|
| Address: | 5060 Shoreham Place, Ste 240 | |
| San Diego, CA 92122 | ||
| Contact Person: | Kora Marinkovic | |
| Telephone Number: | (858) 459-9700 | |
| Fax Number: | (858) 459-9705 | |
| E-mail: | koram@cortechslabs.com | |
| Date Prepared: | 8/21/2024 |
2. Device
| Device Trade Name: | NeuroQuant |
|---|---|
| Common Name: | Medical Image Processing Software |
| Classification Name: | System, Image Processing, Radiological |
| Regulation Number: | 21 CFR 892.2050 |
| Regulation Description: | Medical image management and processing system |
| Product Code: | QIH, LLZ |
| Classification Panel: | Radiology |
3. Predicate Device
| Device: | NeuroQuant |
|---|---|
| 510(k) Number | K170981 |
| Manufacturer | CorTechs Labs, Inc |
| Product Code: | LLZ |
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4. Device Description
NeuroQuant is a fully automated MR imaging post-processing software medical device that provides automatic labeling, visualization, and volumetric quantification of brain structures and lesions from a set of MR images and returns segmented images and morphometric reports.
NeuroQuant provides morphometric measurements of brain structures based on a 3D T1 MRI series. The optional use of the T2 FLAIR MR series and T2* GRE/SWI series allows for additional quantification of T2 FLAIR hyperintense lesions and T2* GRE/SWI hypointense lesions.
The device is used by medical professionals in imaging centers, hospitals, and other healthcare facilities as well as by clinical researchers. When used clinically, the output must be reviewed by a radiologist or neuroradiologist. The results are typically forwarded to the referring physician, most commonly a neurologist. The device is a "Prescription Device" and is not intended to be used by patients or other untrained individuals.
From a workflow perspective, the device is packaged as a computing appliance that is capable of supporting DICOM standard input and output. NeuroQuant supports data from all major MRI manufacturers and a variety of field strengths. For best results, scans should be acquired using specified protocols provided by CorTechs Labs.
As part of processing, the data is corrected by NeuroQuant for image acquisition artifacts, including gradient nonlinearities and bias field inhomogeneity, to improve overall image quality.
Next, image baseline intensity levels for gray and white matter are identified and corrected for scanner variability. The scan is then aligned with the internal anatomical atlas by a series of
transformations. Probabilistic methods and neural network models are then used to label each voxel with an anatomical structure based on location and signal intensities.
Output of the software provides values as numerical volumes, and images of derived data as grayscale intensity maps and as color overlays on top of the anatomical image. The outputs are provided in standard DICOM format as image series and reports that can be displayed on many commercial DICOM workstations.
The software is designed without the need for a user interface after installation. Any processing
errors are reported either in the output series error report or system log files.
The software can provide data on age and gender-matched normative percentiles. The default reference percentile data for NeuroQuant comprises normal population data.
The device provides DICOM Storage capabilities to receive MRI series in DICOM format from an external source, such as an MRI scanner or PACS server. The device provides transient data storage only. If additional scans from other time points are available, the software can perform change analysis.
5. Intended Use / Indications for Use
NeuroQuant is intended for automatic labeling, visualization, and volumetric quantification of segmentable brain structures and lesions from a set of MR images. Volumetric measurements may be compared to reference percentile data.
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6. Comparison to Predicate Device
Summary Comparison Table for the device and predicate device (K170981):
| Device Name | NeuroQuant (Predicate, K170981) | NeuroQuant (Current Submission) |
|---|---|---|
| Classification | Class II | Class II |
| Product Code | LLZ | QIH, LLZ |
| Indications for | ||
| Use | Automatic labeling, visualization and volumetric | |
| quantification of segmentable brain structures | ||
| and lesions from a set of MR images. | ||
| Volumetric data may be compared to reference | ||
| percentile data | Automatic labeling, visualization and volumetric | |
| quantification of segmentable brain structures | ||
| and lesions from a set of MR images. Volumetric | ||
| data may be compared to reference percentile | ||
| data | ||
| Design and | ||
| Incorporated | ||
| Technology | • Automated measurement of brain tissue | |
| volumes and structures and lesions | ||
| • Automatic segmentation and quantification of | ||
| brain structures using a dynamic probabilistic | ||
| neuroanatomical atlas, with age and gender | ||
| specificity, based on the MR image intensity | Automated measurement of brain tissue | |
| volumes and structures and lesions | ||
| Automatic segmentation and quantification of | ||
| brain structures and lesions using a dynamic | ||
| probabilistic neuroanatomical atlas, with age | ||
| and gender specificity, based on the MR image | ||
| intensity and static deep-learning technologies | ||
| Physical | ||
| characteristics | • Software package | |
| • Operates on off-the-shelf hardware (multiple | ||
| vendors) | • Software package | |
| • Operates on off-the-shelf hardware (multiple | ||
| vendors) | ||
| Operating | ||
| System | Supports Linux, Mac OS X and Windows. | Supports Linux, Mac OS X and Windows. |
| Processing | ||
| Architecture | Automated internal pipeline that performs: |
- artifact correction
- segmentation
- lesion quantification
- volume calculation
- report generation | Automated internal pipeline that performs:
- artifact correction
- segmentation
- lesion quantification
- volume calculation
- report generation |
| Data Source | • MRI scanner: 3D T1 and T2 FLAIR MRI scans
acquired with specified protocols
• NeuroQuant Supports DICOM format as input | MRI scanner: 3D T1 and T2 FLAIR and T2*GRE / SWI MRI scans acquired with specified
protocols
NeuroQuant Supports DICOM format as input |
| Output | • Provides volumetric measurements of brain
structures and lesions
• Includes segmented color overlays and
morphometric reports
Automatically compares results to reference
percentile data and to prior scans when
available
• Supports DICOM format as output of results
that can be displayed on DICOM workstations
and Picture Archive and Communications
Systems | Provides volumetric measurements of brain
structures and lesions
Includes segmented color overlays and
morphometric reports
Automatically compares results to reference
percentile data and to prior scans when
available
Supports DICOM format as output of results
that can be displayed on DICOM workstations
and Picture Archive and Communications
Systems |
| Safety | Automated quality control functions: - Tissue contrast check
- Scan protocol verification
- Atlas alignment check
Results must be reviewed by a trained physician | Automated quality control functions: - Tissue contrast check
- Scan protocol verification
- Atlas alignment check
Results must be reviewed by a trained physician |
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Image /page/6/Picture/2 description: The image shows three circles that are connected. The circle on the left is black and has a dot inside of it with a line extending from the dot to the left. The middle circle is light blue, and the circle on the right is white.
Similarities between NeuroQuant and NeuroQuant (Predicate K170981) software
- Both devices are post-processing software applications for analysis of MR imaging data ●
- . Both devices have the ability to perform volumetric quantification of MR imaging data
- Both devices offer the ability to compare medical images and/or multiple time points .
- . Both enable visualization of information that would otherwise have to be visually compared disjointedly
- . Both devices have the ability to report derived imaging metrics
- Both devices are intended for use on anatomical MR images to provide volumetric quantification of brain structures.
Differences between NeuroQuant and NeuroQuant (Predicate K170981) software
- Addition of T2* GRE / SWI MRI as a data source .
- Addition of static deep-learning technologies .
7. Verification and Validation
NeuroQuant software was tested in accordance with CorTechs verification and validation (V&V) processes. All product and engineering specifications were verified and validated. Software V&V testing was conducted, and documentation was provided at the documentation level as recommended for premarket submissions for software devices in the FDA's "Content of Premarket Submission for Device Software Functions" guidance document.
Verification and Validation tests have been performed to address the intended use, the technological characteristics claims, requirement specifications, and the risk management results.
The V&V and performance data were provided in support of safety and effectiveness for the substantial equivalence determination.
NeuroQuant verification and validation testing included:
-
objective unit testing comparing the software-derived values to the known ground truth values,
-
system testing to verify that the RGB Overlays and Reports are correctly generated when compatible anatomical images are input to NeuroQuant, and
-
Clinical validation testing to ensure that the RGB Overlays and Reports are correct, meet clinical expectations, and are safe and effective.
Testing performed demonstrated that NeuroQuant meets all defined functionality requirements and performance claims.
The test results demonstrate that NeuroQuant complies with the international and FDArecognized consensus standards and FDA quidance documents listed in the Premarket Submission, meets the acceptance criteria, and is adequate for its intended use and specifications.
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8. Performance Testing
In this NeuroQuant software, three deep-learning models were included: Brain Segmentation; FLAIR Lesion Segmentation; and MCH Segmentation.
Performance testing was designed specifically to ensure the robustness, generalizability, and accuracy of the models, as described below.
Brain Segmentation Model
The NeuroQuant Brain Segmentation Model was evaluated using the Dice Similarity Coefficient (DSC) as the primary performance metric. The model's performance was assessed against the predicate device and meets the acceptance criteria for accuracy and reproducibility.
The model was trained on a diverse dataset of 1,473 3D T1-weighted MRI series from over 16 institutions, encompassing a wide range of scanner protocols, field strengths, manufacturers, and scanner models. The test set comprised 30 patients with a gender distribution of 47% female and 53% male, aged 20-40 years. By utilizing a separate dataset for testing, independence between training and test data was ensured. Both datasets were curated to represent the diverse patient population across the United States, with no exclusion criteria based on race or ethnicity.
FLAIR Segmentation Model
The NeuroQuant FLAIR Lesion Segmentation Model demonstrated strong performance, achieving a mean Dice Similarity Coefficient (DSC) of 0.70 with a standard deviation of 0.14, surpassing the acceptance criteria in comparison to the predicate device of mean DSC ≥ 0.50 and standard deviation ≤ 0.18. This model was specifically designed to identify FLAIR hyperintensities, which may be caused by conditions such as multiple sclerosis, microvascular ischemic disease, or vasogenic edema.
The model was developed using a training set of 340 T1 and FLAIR MRI series from 22 institutions, incorporating various scan protocols, MRI scanner models, and field strengths. The test set comprised 63 patients, with a gender distribution of 67% female and 33% male, ranging in age from 25 to 87 years. Test data was acquired across Philips, GE, and Siemens scanners. To ensure independence between training and test data, a stratified sampling method was employed for an 80%/20% train/test split. Both datasets were curated to represent the diverse patient population across the United States, with no exclusion criteria based on race or ethnicity.
MCH Detection Model
The MCH Detection Model in NeuroQuant exhibited robust performance, achieving a median F1 Score of 0.60, which exceeded the acceptance criteria of ≥ 0.51. This model was developed to detect cerebral hypointensities associated with blood products, such as cerebral microbleeds and superficial siderosis, which may be caused by conditions including vascular disease, cerebral amyloid angiopathy (CAA), and anti-amyloid therapy (ARIA-H).
The model was trained on 463 2D T2*GRE/SWI MRI series from over 68 institutions, encompassing a wide range of scanner protocols, manufacturers, and models. The test set included 117 patients, with a gender distribution of 42% female and 58% male, spanning ages from 21 to over 81 years. The racial distribution of the combined dataset was 87.8% White, 2.9%
8
Cortechs.ai
5060 Shoreham Place Ste 240, San Diego, CA
Image /page/8/Picture/2 description: The image shows three circles that are linked together. The circle on the left is black and has a dot in the center. The circle in the middle is blue, and the circle on the right is white. The circles are arranged in a horizontal line and are touching each other.
Black, 1.8% More than One, 1.5% Asian, and 5.7% No Data. Test data was acquired using Philips, GE, and Siemens scanners. Independence between training and test data was ensured through a stratified sampling method for an 80%/20% train/test split, with a power analysis conducted to determine the minimum sample size required for statistical significance. Both datasets were curated to represent the diverse patient population across the United States, with no exclusion criteria based on race or ethnicity.
9. Conclusions
The testing summarized above shows that the device is as safe, as effective and performs as well as the predicate device, and as well as gold standard - computer-aided expert manual segmentation.
By virtue of its physical characteristics and intended use, NeuroQuant is substantially equivalent to its predicate device, and its technological improvements do not raise new questions of safety and effectiveness.