Meuro Quant 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.

Device Description

NeuroQuant is a fully automated MR imaging post-processing medical device software 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. The resulting output is provided in a standard DICOM format as additional MR series with segmented color overlays and morphometric reports that can be displayed on third-party DICOM workstations and Picture Archive and Communications Systems (PACS). The high throughput capability makes the software suitable for use in both clinical trial research and routine patient care as a support tool for clinicians in assessment of structural MRIs.

NeuroQuant provides morphometric measurements based on 3D T1 MRI series. The output of the software includes volumes that have been annotated with color overlays, with each color representing a particular segmented region, and morphometric reports that provide comparison of measured volumes to age and gender-matched reference percentile data. In addition, the adjunctive use of the T2 FLAIR MR series allows for improved identification of some brain abnormalities such as lesions, which are often associated with T2 FLAIR hyperintensities.

The NeuroQuant processing architecture includes a proprietary automated internal pipeline that performs artifact correction, atlas-based segmentation, volume calculation and report generation.

Additionally, automated safety measures include automated quality control functions, such as tissue contrast check, atlas alignment check and scan protocol verification, which validate that the imaging protocols adhere to system requirements.

From a workflow perspective, NeuroQuant is packaged as a computing appliance that is capable of supporting DICOM file transfer for input and output of results.

AI/ML Overview

The provided text describes the 510(k) summary for the NeuroQuant device (K170981). Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the performance statistics reported. While explicit acceptance thresholds are not given in a "PASS/FAIL" format, the document presents quantitative results from the performance testing.

Acceptance Criteria (Implied)	Reported Device Performance
Segmentation Accuracy (Dice's Coefficient):
- Major Subcortical Structures (compared to expert manual)	In the range of 80-90%
- Major Cortical Regions (compared to expert manual)	In the range of 75-85%
- Brain Lesions (T1 and T2 FLAIR, compared to expert manual)	Exceeds 80%
Segmentation Reproducibility (Percentage Absolute Volume Differences):
- Major Subcortical Structures (repeated T1 MRI scans)	Mean percentage absolute volume differences were in the range of 1-5%
- Brain Lesions (repeated T1 and T2 FLAIR MRI scans)	Mean absolute lesion volume difference was less than 0.25cc, while the mean percentage lesion absolute volume difference was less than 2.5%.

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

The document does not explicitly state the sample size used for the test set. It mentions "3D T1 MRI scans" and "3D T1 and T2 FLAIR MRI scan pairs of subjects with brain lesions" were used for evaluation.

The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

The document states that segmentation accuracy was evaluated by "comparing segmentation accuracy with expert manual segmentations." However, it does not specify the number of experts used or their qualifications (e.g., radiologist with 10 years of experience).

4. Adjudication Method for the Test Set

The document mentions "expert manual segmentations" as the ground truth, but it does not describe any adjudication method (e.g., 2+1, 3+1, none) used to establish this ground truth among multiple experts if more than one was involved.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study or any effect size of how human readers improve with or without AI assistance. The performance testing focuses solely on the device's accuracy and reproducibility against manual segmentation and repeated scans.

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

Yes, a standalone performance evaluation was done. The "Performance Testing" section describes how "NeuroQuant performance was evaluated by comparing segmentation accuracy with expert manual segmentations and by measuring segmentation reproducibility between same subject scans." This refers to the algorithm's performance directly, independent of a human reader's interaction with the output for primary diagnosis.

7. The Type of Ground Truth Used

The ground truth used for the segmentation accuracy evaluation was "expert manual segmentations." For reproducibility, the ground truth was the measurements from repeated scans of the same subjects, with the expectation that the device produces consistent results on these repeated scans.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set. It describes the device's "proprietary automated internal pipeline that performs... atlas-based segmentation," and "dynamic probabilistic neuroanatomical atlas, with age and gender specificity." This implies a trained model, but the size of the dataset used for this training is not disclosed.

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

The document states the device uses "atlas-based segmentation" and a "dynamic probabilistic neuroanatomical atlas, with age and gender specificity." This suggests the training involves the creation or utilization of an anatomical atlas, which typically involves expert anatomical labeling and segmentation of a representative set of MR images to build probabilities for different brain regions. However, the specific methodology for establishing this ground truth for the training set (e.g., number of experts, their qualifications, adjudication) is not detailed in this summary.

Summary

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Public Health Service

Food and Drug Administration 10903 New Hampshire Avenue Document Control Center - WO66-G609 Silver Spring, MD 20993-0002

September 7, 2017

CorTechs Labs, Inc Kora Marinkovic Director of Quality and Regulatory Affairs 4690 Executive Drive. Suite 250 San Diego, CA 92121

Re: K170981

Trade/Device Name: NeuroOuant Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: August 9, 2017 Received: August 11, 2017

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 (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. 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.

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

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related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please contact the Division of Industry and Consumer Education (DICE) at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address

http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to

http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.

You may obtain other general information on your responsibilities under the Act from the Division of Industry and Consumer Education (DICE) at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address

http://www.fda.gov/MedicalDevices/ResourcesforYou/Industry/default.htm.

Sincerely,

Michael D. O'Hara For

Robert A. Ochs, Ph.D Director Division of Radiological Health Office of In Vitro Diagnostics and Radiological Health Center for Devices and Radiological Health

Enclosure

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DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration

Indications for Use

510(k) Number (if known)

K170981

Device Name NeuroQuant

Indications for Use (Describe)

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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510(k) Summary: NeuroQuant®

1. Submitter

Name:	CorTechs Labs, Inc
Address:	4690 Executive Drive, Suite 250San Diego, CA 92121
Contact Person:	Kora Marinkovic
Telephone Number:	(858) 459-9703
Fax Number:	(858) 459-9705
E-mail:	koram@cortechslabs.com
Date Prepared:	8/4/2017

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:	Picture archiving and communications system
Product Code:	LLZ
Classification Panel:	Radiology

3. Predicate Device

Device:	NeuroQuant™
510(k) Number	K061855
Manufacturer	CorTechs Labs, Inc
Product Code:	LLZ

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Image: CORTECHS Labs Logo	510(k) Section Number: 5	Document No: 007
	Title	NeuroQuant 510(k) Premarket Submission: 510(k) Summary
	Revision: 01	Pages 2 of 4 Date: 8/4/2017

4. Device Description

The NeuroQuant processing architecture includes a proprietary automated internal pipeline that performs artifact correction, atlas-based segmentation, volume calculation and report generation.

From a workflow perspective, NeuroQuant is packaged as a computing appliance that is capable of supporting DICOM file transfer for input and output of results.

5. Intended Use

NeuroQuant® is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. It is intended to automate the manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images.

6. Comparison to Predicate Device

	CorTechs Labs, Inc	CorTechs Labs, Inc
Device Name	NeuroQuantTM V1.0	NeuroQuant® V2.2
510(k) No	K061855	N/A
Regulation No	21 CFR 892.2050	21 CFR 892.2050
Regulation Description	"Picture archiving and communications system"	"Picture archiving and communications system"

Summary Comparison Table for the device and predicate device (K061855):

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Image: CORTECHS Labs logo	510(k) Section Number: 5	Document No: 007
	Title	NeuroQuant 510(k) Premarket Submission: 510(k) Summary
	Revision: 01	Pages 3 of 4	Date: 8/4/2017

Classification Name	System, Image Processing, Radiological	System, Image Processing, Radiological
Classification	Class II	Class II
Product Code	LLZ	LLZ
Indications for Use	Automatic labeling, visualization and volumetricquantification of segmentable brain structuresfrom a set of MR images, intended to automatethe current manual process of identifying,labeling and quantifying the volume ofsegmentable brain structures identified on MRimages	Automatic labeling, visualization and volumetricquantification of segmentable brain structuresand lesions from a set of MR images. Volumetricdata may be compared to reference percentiledata
Design andIncorporatedTechnology	• Automated measurement of brain tissuevolumes and structures• Automatic segmentation and quantification ofbrain structures using a probabilisticneuroanatomical atlas based on the MR imageintensity	• Automated measurement of brain tissuevolumes and structures and lesions• Automatic segmentation and quantification ofbrain structures using a dynamic probabilisticneuroanatomical atlas, with age and genderspecificity, based on the MR image intensity
Physicalcharacteristics	• Software package• Operates on off-the-shelf hardware (multiplevendors)	• Software package• Operates on off-the-shelf hardware (multiplevendors)
Operating System	Supports Linux and Mac OS X	Supports Linux, Mac OS X and Windows.
ProcessingArchitecture	Automated internal pipeline that performs:- artifact correction- segmentation- volume calculation- report generation	Automated internal pipeline that performs:- artifact correction- segmentation- lesion quantification- volume calculation- report generation
Data Source	• MRI scanner: 3D T1 MRI scans acquired withspecified protocols• NeuroQuant Supports DICOM format as input	• MRI scanner: 3D T1 MRI scans acquired withspecified protocols• NeuroQuant Supports DICOM format as input
Output	• Provides volumetric measurements of brainstructures• Includes segmented color overlays andmorphometric reports• Automatically compares results to referencepercentile data and to prior scans when available• Supports DICOM format as output of results thatcan be displayed on DICOM workstations andPicture Archive and Communications Systems	• Provides volumetric measurements of brainstructures and lesions• Includes segmented color overlays andmorphometric reports• Automatically compares results to referencepercentile data and to prior scans whenavailable• Supports DICOM format as output of results thatcan be displayed on DICOM workstations andPicture 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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CORTECHS Labs		510(k) Section Number: 5		Document No: 007
	Title	NeuroQuant 510(k) Premarket Submission: 510(k) Summary
	Revision: 01		Pages 4 of 4	Date: 8/4/2017

NeuroQuant is functionally similar and improved from a previous 510(k) market-cleared CorTechs Labs NeuroQuant software device (NeuroQuant K061855).

Both devices have same intended use and basic design and similar operating principle.

Both systems use clinical MR brain scans as input and automatically identify and measure volumes of brain structures. Both systems provide morphometric measurements based on 3D T1 MRI series. The resulting output is provided in a standard DICOM format as additional MR series that can be displayed on third-party DICOM workstations and PACS.

Both systems produce similar reports. The output includes volumes that have been annotated with color overlays, with each color representing a particular segmented reqion, and morphometric reports that provide comparison of measured volumes to reference percentile data.

They utilize the same automated safety measures and have similar processing architecture.

Both devices are DICOM compatible and operate on off-the-shelf hardware.

Both systems are used by medical professionals, such as radiologists, neurologists and neuroradiologists, as well as by clinical researchers, as a support tool in assessment of structural MRIs.

7. Performance Testing

NeuroQuant performance was evaluated by comparing segmentation accuracy with expert manual segmentations and by measuring segmentation reproducibility between same subject scans. The system yields reproducible results that are well correlated with computer-aided expert manual segmentations.

NeuroQuant's segmentation accuracy compared to expert manual segmentations of 3D T1 MRI scans was evaluated using Dice's coefficient metric. For major subcortical brain structures Dice's coefficients are in the range of 80-90% and for major cortical regions are in the range of 75-85%. For lesion segmentations evaluated separately using 3D T1 and T2 FLAIR MRI scan pairs of subjects with brain lesions, Dice's coefficient exceeds 80%.

Brain structure segmentation reproducibility of repeated 3D T1 MRI scans for same subjects was evaluated by using the percentage absolute volume differences. The mean percentage absolute volume differences for all major subcortical structures were in the range1-5%. Brain lesion seqmentation reproducibility was evaluated separately using 3D T1 and T2 FLAIR MRI repeated scan pairs of subjects with brain lesions. The mean absolute lesion volume difference was less than 0.25cc, while the mean percentage lesion absolute volume difference was less than 2.5%.

8. Conclusions

The performance testing presented 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 the 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.

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).