(88 days)
QyScore is intended for 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. QyScore is not intended for use in clinical scenarios that require evaluation of the white matter hyperintensities.
QyScore automatically provides segmentations and measures of brain structures and lesions from a set of MR images for patients between the ages of 20 and 90.
The software retrieves DICOM MRI data (3DT1 and T2FLAIR series) from a DICOM server and sends it to an analysis server for automatic segmentation of grey matter, white matter, hippocampus, amygdala and white matter hyperintensities. The outputs of the software include an electronic report and color overlays of the segmentation on the input images.
The results are displayed in a dedicated graphical user interface, allowing the user to:
- . Browse the segmentations and the measures,
- Compare the results of segmented brain structures to a reference healthy population, ●
- Read and edit a PDF report.
QyScore integrates with leading RIS/PACS systems and can be operated with any MRI scan from 1.5T and 3T scanners for T1 MRI processing, and 3T scanners for T2FLAIR MRI processing.
Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
| Assessment Category | Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|---|
| Segmentation Accuracy | - Dice coefficients > 85% for whole brain regions (3DT1 MRI scans) | - Dice coefficients exceed 85% on whole brain regions (3DT1 MRI scans) |
| - Dice coefficients 75%-85% for subcortical brain structures (3DT1 MRI scans) | - Dice coefficients are in the range of 75%-85% for subcortical brain structures (3DT1 MRI scans) | |
| Lesion Segmentation Accuracy | - (Implicitly, a low mean absolute volume difference for lesions) | - Mean absolute volume difference for lesions: 3.34mL |
2. Sample Size Used for the Test Set and Data Provenance:
The document mentions that the lesion segmentation was assessed "on a broad validation population, with lesion loads ranging from 0.09mL to 87.65mL." However, the exact sample size (number of cases/patients) for the test set is NOT explicitly stated.
The data provenance is also NOT explicitly stated (e.g., country of origin, retrospective or prospective).
3. Number of Experts Used to Establish Ground Truth and Qualifications:
The document mentions "expert manual segmentations" were used as ground truth. However, the number of experts and their specific qualifications (e.g., radiologist with X years of experience) are NOT explicitly stated.
4. Adjudication Method for the Test Set:
The document states that "QyScore segmentations were compared to expert manual segmentations." This implies a direct comparison, but the adjudication method (e.g., 2+1, 3+1, none for discordances) is NOT explicitly stated. It appears to be a direct comparison against a single expert's "manual segmentation" or an agreed-upon expert ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was NOT done according to the provided text. The document focuses on the standalone performance of the QyScore software against expert manual segmentations. There's no mention of human readers using or not using the AI and their performance improvement.
6. Standalone (Algorithm Only) Performance:
Yes, a standalone (algorithm only without human-in-the-loop performance) study was done. The "accuracy evaluation" explicitly compared "QyScore segmentations to expert manual segmentations" to establish the device's inherent performance.
7. Type of Ground Truth Used:
The ground truth used was expert manual segmentations.
8. Sample Size for the Training Set:
The sample size for the training set is NOT explicitly stated in the provided text. The document only mentions "a dynamic probabilistic neuroanatomical atlas" used in the segmentation process, which is likely built from a training set, but its size is not detailed.
9. How the Ground Truth for the Training Set Was Established:
The document states that the predicate device uses "a dynamic probabilistic neuroanatomical atlas, with age and gender specificity, based on the MR image intensity." The subject device (QyScore) also uses "a dynamic probabilistic neuroanatomical atlas based on the MR image intensity." This implies that the atlas, which serves as a form of ground truth for training, was established through probabilistic modeling of neuroanatomical structures based on a dataset of MR images and their intensities, likely involving expert anatomical knowledge or segmentations used to build the atlas. However, the specific methodology and the process of establishing the ground truth for this training atlas are NOT explicitly detailed.
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December 13, 2019
Qynapse % Mr. Michael Daniel President Daniel & Daniel Consulting, LLC 340 Jones Lane GARDNERVILLE NV 89460
Re: K192531
Trade/Device Name: OyScore Software Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: November 29, 2019 Received: December 2, 2019
Dear Mr. Daniel:
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 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
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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 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).
Sincerely,
Thalia T Mills, Ph.D. Director Division of Radiological Health OHT7: Office of In Vitro Diagnostics and Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
510(k) Number (if known) K192531
Device Name Qyscore Software
Indications for Use (Describe)
QyScore is intended for 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. QyScore is not intended for use in clinical scenarios that require evaluation of the white matter hyperintensities.
| 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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Image /page/3/Picture/0 description: The image shows the word "Qynapse" in a blue sans-serif font. The word is written in all lowercase letters except for the first letter, which is capitalized. There is a registered trademark symbol to the right of the word. The word is centered and takes up most of the image.
Premarket Notification 510(k) Summary
This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of SMDA 1990 and 21 CFR 807.92.
510(k) Number: K192531
Applicant Information:
| Date Prepared: | December 4, 2019 |
|---|---|
| Name: | Qynapse |
| Address: 67 rue Saint Jacques | |
| 75005 Paris, France |
| Contact Person: | Michael A Daniel, Consultantmadaniel@clinregconsult.com |
|---|---|
| Mobile Number: | (415) 407-0223 |
| Office Number: | (775) 392-2970 |
| Facsimile Number: | (610) 545-0799 |
Device Information:
| Trade Name: | QyScore Software |
|---|---|
| Common Names: | Medical Image Processing Software |
| Classification Name(s): | Picture archiving and communications system |
| Product Code/ Regulation: | LLZ/21 CFR 892.2050 |
| Classification: | Class II |
Predicate Device:
- NeuroQuant Medical Image Processing Software K170981 ●
Device Description:
QyScore automatically provides segmentations and measures of brain structures and lesions from a set of MR images for patients between the ages of 20 and 90.
The software retrieves DICOM MRI data (3DT1 and T2FLAIR series) from a DICOM server and sends it to an analysis server for automatic segmentation of grey matter, white matter, hippocampus, amygdala and white matter hyperintensities. The outputs of the software include an electronic report and color overlays of the segmentation on the input images.
The results are displayed in a dedicated graphical user interface, allowing the user to:
- . Browse the segmentations and the measures,
- Compare the results of segmented brain structures to a reference healthy population, ●
- Read and edit a PDF report.
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QyScore integrates with leading RIS/PACS systems and can be operated with any MRI scan from 1.5T and 3T scanners for T1 MRI processing, and 3T scanners for T2FLAIR MRI processing.
Indications for Use:
QyScore is intended for 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. QyScore is not intended for use in clinical scenarios that require evaluation of the number of the white matter hyperintensities.
| Subject device | Predicate device | |
|---|---|---|
| QyScore v1.7.0 | NeuroQuant v2.2 | |
| 510(k) number | N/A | K170981 |
| Regulationnumber | 21 CFR 892.2050 | 21 CFR 892.2050 |
| Regulationdescription | Picture archiving andcommunications system | Picture archiving andcommunications system |
| Classificationname | System, Image Processing,Radiological | System, Image Processing,Radiological |
| Classification | Class II | Class II |
| Product Code | LLZ | LLZ |
| Indications foruse | QyScore is intended for automaticlabeling, visualization andvolumetric quantification ofsegmentable brain structures andlesions from a set of MR images.Volumetric data may be compared toreference percentile data. QyScore isnot intended for use in clinicalscenarios that require evaluation ofthe number of the white matterhyperintensities. | NeuroQuant is intended forautomatic labeling, visualization andvolumetric quantification ofsegmentable brain structures andlesions from a set of MR images.Volumetric measurements may becompared to reference percentiledata. |
| Design andincorporatedtechnology | - Automated measurement of braintissue volumes and structures andlesions- Automatic segmentation andquantification of brain structuresusing a dynamic probabilisticneuroanatomical atlas based on theMR image intensity | - Automated measurement of braintissue volumes and structures andlesions- Automatic segmentation andquantification of brain structuresusing a dynamic probabilisticneuroanatomical atlas, with age andgender specificity, based on the MRimage intensity |
| - Results displayed through graphical user interface | ||
| Physicalcharacteristics | - Software package- Operates on off-the-shelf hardware (multiple vendors) | - Software package- Operates on off-the-shelf hardware (multiple vendors) |
| Operatingsystem | Supports Linux | Supports Linux, Mac OS X and Windows |
| Processingarchitecture | Automated internal pipeline that performs:- bias correction- segmentation- lesions quantification- volume calculation- report generation | Automated internal pipeline that performs:- bias correction- segmentation- lesions quantification- volume calculation- report generation |
| Data Source | MRI scanner: 3DT1 and FLAIR MRI scans acquired with specified protocols.Supports DICOM format as input. | MRI scanner: 3DT1 and FLAIR MRI scans acquired with specified protocols.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 function:scan protocol verificationResults must be reviewed by a trained physician | Automated quality control functions:- Tissue contrast check- Scan protocol verification- Atlas alignment checkResults must be reviewed by a trained physician |
Summary Comparison to Predicate:
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QyScore and the predicate device are software for automatically identifying and quantifying the volumes of brain structures, automatic labeling and visualization. The devices have the
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same intended use and operating principle. They take MR brain images as input and generate an electronic report with similar quantitative information. For both devices, output volumes are compared to a normative dataset of control subjects computed based on MRI data from normal control subjects.
QyScore and NeuroQuant achieve their intended use based on a similar principle, since the quantification system relies on skull stripping (brain extraction), a brain segmentation based on a probabilistic atlas and image intensity information, and volume calculations of the segmented brain structures. Both devices normalize the volumes with the intracranial volume to allow for the statistical comparison with a normative dataset.
Both devices are DICOM compatible and operate on off-the-shelf hardware. OyScore is used by medical personnel or neuroimaging trained personnel. NeuroQuant is used by physicians skilled in brain MR imaging.
Summary of Performance Testing
Testing performed on the OyScore software included:
- Software verification testing .
- Software validation testing
- Human factors testing
- Validation studies based on scientific literature
- Evaluation of segmentation accuracy ●
For the accuracy evaluation, QyScore segmentations were compared to expert manual segmentations for each segmented structure. Several acceptance criteria have been set in agreement with the literature, on both overlap metrics and volume difference metrics.
The Dice coefficients exceed 85% on whole brain regions and are in the range of 75%-85% for subcortical brain structures segmented from 3DT1 MRI scans. The lesions segmentation, performed on (3DT1+T2FLAIR) paired sequences, was assessed on a broad validation population, with lesion loads ranging from 0.09mL to 87.65mL. The resulting mean absolute volume difference is 3.34mL.
The performance testing demonstrated that the software meets its intended use, product specifications and user needs.
Software
Qynapse followed IEC 62304 and the FDA Guidance Document, "General Principles of Software Validation; Final Guidance for Industry and FDA Staff" (January, 2002) with respect to software development and validation. The QyScore software is classified as a "moderate level of concern" per the FDA guidance document.
Verification and validation testing was completed in compliance with the following standards and guidance documents:
- · ISO 14971:2012. Medical devices application of risk management to medical devices
- AAMI ANSI IEC 62304:2006, Medical device software Software life cycle processes ●
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- . General Principles of Software Validation; Final Guidance for Industry and FDA Staff" (January, 2002)
- AAMI ANSI IEC 62366:2007, Medical devices Application of usability engineering . to medical devices
Testing described in this 510(k) consists of verification of all design input requirements and product specifications. All clinical input requirements were validated.
Conclusion
Based upon the intended use, product technical information, performance evaluation, and standards compliance provided in this premarket notification, the QyScore software has been shown to be substantially equivalent to the legally marketed predicate device. The technological differences do not raise any new questions of safety and effectiveness.
§ 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).