(638 days)
No reference devices were used in this submission.
Yes
The description mentions "algorithms to learn the variability of the alignment on the atlases" in the training set description, which strongly suggests the use of machine learning techniques.
No
The device is a software application for analyzing medical images and providing measurements, not for directly treating or diagnosing patients. Its function is to provide information for clinicians to use in managing patients, not to provide therapy itself.
Yes
Explanation: The device is intended for automatic labeling and visualization of segmentable brain structures and lesions from MRI images, and it outputs brain region and lesion volumes in a report format. This information can be used by clinicians in the management of patients with neurological disorders, which indicates its use in aiding diagnosis or monitoring disease progression.
Yes
The device description explicitly states that Pixyl.Neuro is a "software application for the analysis of medical images of the brain." It takes MRI images as input and outputs data in a report format, indicating it is purely a software-based processing tool. There is no mention of accompanying hardware components or hardware-specific validation.
Based on the provided information, this device is NOT an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In Vitro Diagnostics are medical devices intended for use in vitro for the examination of specimens, including blood, tissue, and urine, derived from the human body, solely or principally for the purpose of providing information concerning a physiological or pathological state, or concerning a congenital abnormality, or to determine the safety and compatibility with potential recipients, or to monitor therapeutic measures.
- Pixyl.Neuro's Function: Pixyl.Neuro analyzes medical images (MRI scans) of the brain. It does not analyze biological specimens (blood, tissue, etc.) taken from the body.
- Input: The input is DICOM format MRI images, not biological samples.
- Output: The output is brain region volumes and lesion volumes in a report format, derived from the image analysis, not from the analysis of biological markers in a specimen.
Pixyl.Neuro falls under the category of medical image analysis software, which is a type of medical device, but not an IVD. It provides information based on the visual and structural data within the images, rather than chemical, biological, or immunological analysis of a specimen.
No
The letter does not explicitly state that the FDA has reviewed, approved, or cleared a PCCP for this specific device.
Intended Use / Indications for Use
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.
Product codes (comma separated list FDA assigned to the subject device)
LLZ
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.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Not Found
Input Imaging Modality
MRI scanner: 2D or 3D FLAIR or T1 MRI scans acquired with specified protocols. Pixyl.Neuro supports DICOM format as input.
Anatomical Site
Brain
Indicated Patient Age Range
Not Found
Intended User / Care Setting
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.
Description of the training set, sample size, data source, and annotation protocol
misalignments are generated on the training dataset for the algorithms to learn the variability of the alignment on the atlases.
Description of the test set, sample size, data source, and annotation protocol
The experiments included a total of 238 subject datasets. 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.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
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.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
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.
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.
No reference devices were used in this submission.
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
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)
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.
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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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
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.
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.
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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 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. | 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. | Substantially equivalent. | ||
| Design and | |||
| Incorporated | |||
| Technology | • Automated measurement | ||
| and segmentation of brain | |||
| tissue volumes and structures | • Automated measurement of | ||
| brain tissue volumes and | |||
| structures | |||
| • Automatic segmentation and | |||
| quantification of brain | |||
| structures using a | |||
| probabilistic neuroanatomical | |||
| atlas based on the MR image | |||
| intensity | Input and output data and | ||
| validation methods are | |||
| similar, although | |||
| incorporated technology | |||
| applied to the | |||
| segmentation techniques | |||
| may differ. This does not | |||
| introduce any additional | |||
| risk since results are | |||
| comparable. | |||
| Physical | |||
| characteristics | • Cloud-based software | ||
| launchable through Picture | |||
| Archive and Communications | |||
| Systems (multiple vendors) | |||
| • Operates on off-the-shelf | |||
| hardware/platform (multiple | |||
| vendors) | • Software package | ||
| • Operates on off-the-shelf | |||
| hardware (multiple vendors) | Pixyl.Neuro is accessible | ||
| through users' preferred | |||
| platforms, directly from | |||
| their work stations. This | |||
| does not introduce any | |||
| additional risk since the | |||
| use of Pixyl.Neuro is | |||
| seamlessly integrated into | |||
| their routine work | |||
| practice, and does not | |||
| require any installation or | |||
| tuning from the end users. | |||
| Operating | |||
| System | Linux | Supports Linux and Mac OS | |
| X | Substantially equivalent. | ||
| Processing | |||
| Architecture | Automated internal pipeline | ||
| that performs: |
- artifact correction
- segmentation
- volume calculation | Automated internal pipeline
that performs: - artifact correction
- segmentation
- volume calculation | Substantially equivalent. |
| Data Source | MRI scanner: 2D or 3D
FLAIR or T1 MRI scans
acquired with specified
protocols.
Pixyl.Neuro supports DICOM
format as input. | MRI scanner: 3D T1 MRI
scans acquired with specified
protocols
• NeuroQuant® Supports
DICOM format as input | Pixyl.Neuro supports more
options for input MRI
sequences. This does not
introduce any new risks.
Clinical performance
evaluation is also
performed on these
additional inputs. |
| Output | • Provides volumetric
measurements of brain
structures.
• Includes segmented color
overlays and morphometric
reports | • Provides volumetric
measurements of brain
structures
• Includes segmented color
overlays and morphometric
reports | Substantially equivalent. |
| | • Automatically compares
results to reference percentile
data and to prior scans when
available | • 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 | • 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
• 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 | Pixyl.Neuro does not
perform atlas alignment
checks as part of its
automated quality control
functions. However, the
transform used in
Pixyl.Neuro's registration
step is widely used in
neuroimaging as the base
step of further image
processing. In addition,
misalignments are
generated on the training
dataset for the algorithms
to learn the variability of
the alignment on the
atlases. It has been
estimated that this does
not introduce any
additional risk. |
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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.