Pixyl.Neuro is intended for automatic labeling, visualization of segmentable brain structures and lesions from a set of MRI images. Volumetric measurements may be compared to reference percentile data.

Device Description

Pixyl.Neuro is a software application for the analysis of medical images of the brain. Specifically, the application takes as input MRI images and outputs brain region volumes and lesion volumes in a report format. The application is designed to be used by clinicians treating patients with a range of neurological disorders. The application can be used in the management of patients in a routine setting and in clinical research.

AI/ML Overview

Here's an analysis of the acceptance criteria and study detailed in the provided text for Pixyl.Neuro:

1. Table of Acceptance Criteria and Reported Device Performance

The text clearly states: "Relevant acceptance criteria have been set based on the results of a literature review for each type of experiment. All experiments passed the acceptance criteria." However, the specific numerical acceptance criteria are not explicitly provided in the excerpt. Instead, the reported device performance is given, which implicitly met the undisclosed acceptance criteria.

Metric (Pipeline)	Reported Device Performance (Mean +/- Std Dev)	Acceptance Criteria (Not explicitly stated in excerpt)
Accuracy (Dice Coefficient)
MS (Multiple Sclerosis)	0.80 (+/- 0.06)	(Implicitly met based on literature review)
FL (Fluid Attenuated Inversion Recovery)	0.730 (+/- 0.10)	(Implicitly met based on literature review)
BV (Brain Volume)	0.84 (+/- 0.02)	(Implicitly met based on literature review)
Reproducibility (Mean Absolute Volume Difference)
MS and FL Modules (Total Lesion Load)	0.199 ml (+/- 0.193)	(Implicitly met based on literature review)
BV Module (Mean across 20 brain structures)	0.966 ml (+/- 1.098)	(Implicitly met based on literature review)

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

Sample Size: A total of 238 subject datasets were included in the experiments.
Data Provenance: The text states, "The device was tested upon subjects from the following groups: healthy subjects, multiple sclerosis patients, Alzheimer's patients, microangiopathy patients and white matter hyperintensities (of presumed vascular origin) patients." The country of origin is not specified, and it does not explicitly state whether the data was retrospective or prospective.

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

This information is not provided in the excerpt. The text mentions "ground truth volumes or volume changes" but does not detail how this ground truth was established, including the number or qualifications of experts.

4. Adjudication Method for the Test Set

This information is not provided in the excerpt.

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 evaluating human reader improvement with and without AI assistance was not mentioned in this excerpt. The study focuses on the standalone performance of the device (accuracy and reproducibility).

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

Yes, a standalone performance study was done. The performance data presented (Dice coefficients for accuracy, and mean absolute volume differences for reproducibility) directly assess the algorithm's performance without explicit mention of human-in-the-loop interaction for performance metrics. The safety section does note that "Results must be reviewed by a trained physician," indicating a human-in-the-loop for clinical use, but the reported performance metrics are for the algorithm's output itself.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The text refers to "ground truth volumes or volume changes." While the method of establishing this ground truth (e.g., expert consensus, manual segmentation by experts, or another reference standard) is not explicitly stated, the comparison of segmentations to "ground truth" implies a reference standard was used for validation.

8. The Sample Size for the Training Set

This information is not provided in the excerpt. The text only mentions the sample size for the test set (238 subject datasets).

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

This information is not provided in the excerpt. The text does mention, "misalignments are generated on the training dataset for the algorithms to learn the variability of the alignment on the atlases," which implies a training dataset exists and has some form of ground truth or simulated variability, but the method of establishing this ground truth is not detailed.

Summary

{0}------------------------------------------------

Image /page/0/Picture/0 description: The image contains the logos of the Department of Health & Human Services and the Food and Drug Administration (FDA). The Department of Health & Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the agency's name, "U.S. Food & Drug Administration," in blue text.

June 30, 2023

Pixyl SA % Robert Packard President Medical Device Academy Inc. 345 Lincoln Hill Road SHREWSBURY VT 05738

Re: K213253

Trade/Device Name: Pixyl.Neuro Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: LLZ Dated: May 26, 2023 Received: May 26, 2023

Dear Robert Packard:

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. 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 located 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.

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

{1}------------------------------------------------

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 803) for devices or postmarketing safety reporting (21 CFR 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 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

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 https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-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/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).

Sincerely,

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

{2}------------------------------------------------

Indications for Use

510(k) Number (if known) K213253

Device Name Pixyl.Neuro

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)

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 Food and Drug Administration Office of Chief Information Officer Paperwork Reduction Act (PRA) Staff 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."

{3}------------------------------------------------

system

510(k) SUMMARY

This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR §807.92:

SUBMITTER
Pixyl SAS
5 Avenue du Grand Sablon
La Tronche, 38700 France
+33 6 19 53 14 48

Contact Person:	Senan Doyle
Date Prepared:	September 29th, 2021

II. DEVICE
Name of Device:	Pixyl.Neuro
Classification Name:	Medical image management and processing
Regulation:	21 CFR §892.2050
Regulatory Class:	Class II
Product Classification Code:	LLZ
III. PREDICATE DEVICE

Predicate Manufacturer:	CorTechs Labs, Inc
Predicate Trade Name:	NeuroQuant
Predicate 510(k):	K170981

No reference devices were used in this submission.

IV. DEVICE DESCRIPTION

V. INDICATIONS FOR USE

Pixyl.Neuro is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures and lesions from a set of MRI images. Volumetric measurements may be compared to reference percentile data.

{4}------------------------------------------------

COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE VI. PREDICATE DEVICE

The following characteristics were compared between the subject device and the predicate device in order to demonstrate substantial equivalence:

Device	Pixyl.Neuro	NeuroQuant® (K170981)	Comments
Indications forUse	Pixyl.Neuro is intended forautomatic labeling,visualization and volumetricquantification of segmentablebrain structures and lesionsfrom a set of MRI images.Volumetric measurementsmay be compared to referencepercentile data.	NeuroQuant® is intended forautomatic labeling,Visualization and volumetricquantification of segmentablebrain structures and lesionsfrom a set of MR images.Volumetric measurementsmay be compared to referencepercentile data.	Substantially equivalent.
Design andIncorporatedTechnology	• Automated measurementand segmentation of braintissue volumes and structures	• Automated measurement ofbrain tissue volumes andstructures• Automatic segmentation andquantification of brainstructures using aprobabilistic neuroanatomicalatlas based on the MR imageintensity	Input and output data andvalidation methods aresimilar, althoughincorporated technologyapplied to thesegmentation techniquesmay differ. This does notintroduce any additionalrisk since results arecomparable.
Physicalcharacteristics	• Cloud-based softwarelaunchable through PictureArchive and CommunicationsSystems (multiple vendors)• Operates on off-the-shelfhardware/platform (multiplevendors)	• Software package• Operates on off-the-shelfhardware (multiple vendors)	Pixyl.Neuro is accessiblethrough users' preferredplatforms, directly fromtheir work stations. Thisdoes not introduce anyadditional risk since theuse of Pixyl.Neuro isseamlessly integrated intotheir routine workpractice, and does notrequire any installation ortuning from the end users.
OperatingSystem	Linux	Supports Linux and Mac OSX	Substantially equivalent.
ProcessingArchitecture	Automated internal pipelinethat performs:- artifact correction- segmentation- volume calculation	Automated internal pipelinethat performs:- artifact correction- segmentation- volume calculation	Substantially equivalent.
Data Source	MRI scanner: 2D or 3DFLAIR or T1 MRI scansacquired with specifiedprotocols.Pixyl.Neuro supports DICOMformat as input.	MRI scanner: 3D T1 MRIscans acquired with specifiedprotocols• NeuroQuant® SupportsDICOM format as input	Pixyl.Neuro supports moreoptions for input MRIsequences. This does notintroduce any new risks.Clinical performanceevaluation is alsoperformed on theseadditional inputs.
Output	• Provides volumetricmeasurements of brainstructures.• Includes segmented coloroverlays and morphometricreports	• Provides volumetricmeasurements of brainstructures• Includes segmented coloroverlays and morphometricreports	Substantially equivalent.
	• Automatically comparesresults to reference percentiledata and to prior scans whenavailable	• Automatically comparesresults to reference percentiledata and to prior scans whenavailable
	• Supports DICOM format asoutput of results that can bedisplayed on DICOMworkstations and PictureArchive and CommunicationsSystems	• Supports DICOM format asoutput of results that can bedisplayed on DICOMworkstations and PictureArchive and CommunicationsSystems
Safety	• Automated quality controlfunctions:- Tissue contrast check- Scan protocol verification• Results must be reviewed bya trained physician	• Automated quality controlfunctions- Tissue contrast check- Scan protocol verification- Atlas alignment check• Results must be reviewed bya trained physician	Pixyl.Neuro does notperform atlas alignmentchecks as part of itsautomated quality controlfunctions. However, thetransform used inPixyl.Neuro's registrationstep is widely used inneuroimaging as the basestep of further imageprocessing. In addition,misalignments aregenerated on the trainingdataset for the algorithmsto learn the variability ofthe alignment on theatlases. It has beenestimated that this doesnot introduce anyadditional risk.

{5}------------------------------------------------

{6}------------------------------------------------

VII. PERFORMANCE DATA

To demonstrate the performance of Pixyl.Neuro, the measured volumes and volume changes of the segmented brain structures are validated for accuracy and reproducibility. The device was tested upon subjects from the following groups: healthy subjects, multiple sclerosis patients, Alzheimer's patients, microangiopathy patients and white matter hyperintensities (of presumed vascular origin) patients.

In the accuracy experiments, the volumes or volume changes are compared to ground truth volumes or volume changes. In the reproducibility experiments, scan / re-scan sessions of the same patients are processed by Pixyl.Neuro and the calculated volumes compared between the two scans. Relevant acceptance criteria have been set based on the results of a literature review for each type of experiment. All experiments passed the acceptance criteria.

The experiments included a total of 238 subject datasets. The segmentation accuracy of Pixyl.Neuro compared to the reference was evaluated using the Dice coefficient metric. The Dice coefficient between the compared measurements is as follows for the different analysis pipelines of Pixyl.Neuro: 0.80 (+/- 0.06) and 0.730 (+/- 0.10) (calculated at the subject level) for MS and FL according to the used testing dataset; 0.84 (+/- 0.02) and 0.83 (+/- 0.02) (averaged Dice scores across the labeled brain structures) for BV according to the used testing dataset. For reproducibility analyses, the mean absolute volume difference between the calculated total lesion volumes from the two scans is: 0.199 ml (+/- 0.193) for the MS and FL modules (total lesion load) and 0.966 ml (+/- 1.098) (mean across de 20 brain structures) for the BV module.

VIII. CONCLUSIONS

The performance testing presented above shows that the device is as safe, as effective and performs as well as the predicate device. By virtue of the physical characteristics and intended use, Pixyl Neuro 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).