(104 days)
Not Found
Yes
The device description explicitly states that it "uses a deep learning methodology" and the performance studies section refers to the "evaluation of the machine learning model performance."
No.
The device is intended for labeling, visualization, and volumetric quantification of high-grade brain glioma, which are diagnostic functions. It is not described as providing treatment or therapy.
No
The device is explicitly stated as "not to be used for primary diagnosis" and "is not intended to be the sole diagnostic metric." It aids in volumetric quantification for patients already diagnosed with high-grade glioma.
Yes
The device description explicitly states "NS-HGlio is a non-invasive software as a medical device (SaMD) tool". It takes DICOM images as input and provides labeling, visualization, and volumetric quantification as output, all of which are software functions. There is no mention of accompanying hardware that is part of the 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. They are used to provide information for diagnosis, monitoring, or screening.
- NS-HGlio's Function: NS-HGlio operates on medical images (MRI), not on biological samples taken from the body. It processes existing images to provide information about the size and location of a known tumor.
- Intended Use: The intended use clearly states it's for "semi-automatic labeling, visualization, and volumetric quantification of high-grade brain glioma... from a set of standard MRI images." It explicitly states it's "not to be used for primary diagnosis" and is an "additional source of information." This aligns with image analysis software, not an IVD.
- Device Description: The description reinforces that it's a "non-invasive software as a medical device (SaMD) tool" that takes "DICOM images" as input.
In summary, NS-HGlio is a medical image processing software designed to assist clinicians in managing and monitoring high-grade brain gliomas based on MRI scans. It does not perform tests on biological samples, which is the defining characteristic of an IVD.
No
The letter explicitly states "Control Plan Authorized (PCCP) and relevant text: Not Found", indicating no PCCP was cleared or approved for this device.
Intended Use / Indications for Use
NS-HGlio is intended for the semi-automatic labeling, visualization, and volumetric quantification of high-grade brain glioma (WHO grade 3 astrocytoma, WHO grade 4 astrocytoma and WHO grade 4 glioblastoma) from a set of standard MRI images of male or female patients 18 years of age or older who are known to have pathologically proven high-grade glioma. Volumetric measurements may be compared to past measurements if available. NS-HGlio is not to be used for primary diagnosis, and is intended to be used by qualified clinical personnel as an additional source of information and is not intended to be the sole diagnostic metric.
Product codes
QIH
Device Description
NS-HGlio is a non-invasive software as a medical device (SaMD) tool intended for labeling, visualization, and volumetric quantification of high-grade brain gliomas for a population that has been pathologically diagnosed to have brain tumors. The device is used as a tool by clinicians in determining the patient's disease conditions on pre- and post-operative MRI images. The device is not used for primary diagnosis.
NS-HGlio device takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images of high-grade brain glioma acquired with standard brain tumor MRI protocols and uses a deep learning methodology to semi-automatically label the different subcomponents of the high-grade glioma. Results are displayed on a Neosoma viewing software. Optionally, the software connects to clinicians' applications (e.g., PACS).
Mentions image processing
Yes
Mentions AI, DNN, or ML
deep learning methodology
Input Imaging Modality
MRI
Anatomical Site
Brain
Indicated Patient Age Range
18 years of age or older
Intended User / Care Setting
qualified clinical personnel
Description of the training set, sample size, data source, and annotation protocol
Not Found
Description of the test set, sample size, data source, and annotation protocol
The testing dataset consisted of 33 subjects and 132 MRIs used for the evaluation of the machine learning model performance. The test dataset was acquired from medical sites that were not included in the training dataset to ensure device generalizability. The data were acquired using standard of care MRI protocols on Siemens, GE, and Toshiba scanners. Following the real world prevalence of the high grade glioma, the data consisted of more males than females within the age range of 18 to 79 and covering a diverse group of ethnic backgrounds.
The reference standard (ground truth) was established using three US board certified neuroradiologists with expertise in measuring high grade gliomas.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
Safety and performance of NS-HGlio have been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/AC:2015 - Medical device software -Software life cycle processes, in addition to the FDA Guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
The testing dataset consisted of 33 subjects and 132 MRIs used for the evaluation of the machine learning model performance. The test dataset was acquired from medical sites that were not included in the training dataset to ensure device generalizability. The data were acquired using standard of care MRI protocols on Siemens, GE, and Toshiba scanners. Following the real world prevalence of the high grade glioma, the data consisted of more males than females within the age range of 18 to 79 and covering a diverse group of ethnic backgrounds.
The reference standard (ground truth) was established using three US board certified neuroradiologists with expertise in measuring high grade gliomas. The dataset was evaluated using the DSC (Dice Similarity Coefficient) assessing the degree of overlap between device output and the reference standard as well as the Intraclass correlation coefficient (ICC) of the device output volumes and the reference standard.
The device achieved a mean DSC of 0.88 with 95% CI of 0.86-0.90 on preoperative imaging and 0.80 with 95% CI of 0.77-0.83 on postoperative imaging which is higher than the mean DSC of the average of the three experts for the same task, which was 0.84 on preoperative imaging and 0.74 for postoperative imaging respectively. The mean ICC was 0.98 with 95% CI of 0.97-0.99.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
Mean DSC of 0.88 with 95% CI of 0.86-0.90 on preoperative imaging.
Mean DSC of 0.80 with 95% CI of 0.77-0.83 on postoperative imaging.
Mean ICC of 0.98 with 95% CI of 0.97-0.99.
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
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 and Human Services and the Food and Drug Administration (FDA). The Department of Health and Human Services logo is on the left, and the FDA logo is on the right. The FDA logo includes the letters "FDA" in a blue square, followed by the words "U.S. Food & Drug Administration" in blue text.
September 27, 2022
Neosoma, Inc. % Aly Abayazeed Co-founder and Chief Medical Officer 44 Farmers Row GROTON MA 01450
Re: K221738
Trade/Device Name: NS-HGlio Regulation Number: 21 CFR 892.2050 Regulation Name: Medical image management and processing system Regulatory Class: Class II Product Code: QIH Dated: September 1, 2022 Received: September 2, 2022
Dear Aly Abayazeed:
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
1
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,
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) K221738
Device Name NS-HGlio
Indications for Use (Describe)
NS-HGlio is intended for the semi-automatic labeling, visualization, and volumetric quantification of high-grade brain glioma (WHO grade 3 astrocytoma, WHO grade 4 astrocytoma and WHO grade 4 glioblastoma) from a set of standard MRI images of male or female patients 18 years of age or older who are known to have pathologically proven high-grade glioma. Volumetric measurements may be compared to past measurements if available. NS-HGlio is not to be used for primary diagnosis, and is intended to be used by qualified clinical personnel as an additional source of information and is not intended to be the sole diagnostic metric.
| 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
1.1 General Information
K221738
| 510(k) Sponsor | Neosoma, Inc. |
|---|---|
| Address | 44 Farmers Row |
| Groton, MA 01450 | |
| Correspondence | |
| Person | Aly H. Abayazeed, MD. |
| Co-founder and Chief Medical Officer | |
| Contact Information | aly.abayazeed@neosomainc.com |
| 443-804-8096 | |
| Date Prepared | June 14, 2022 |
1.2 Proposed Device
| Proprietary Name | NS-HGlio |
|---|---|
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | QIH |
| Regulatory Class | II |
1.3 Predicate Device
| Proprietary Name | NeuroQuant |
|---|---|
| Premarket Notification | K170981 |
| Classification Name | Automated Radiological Image Processing Software |
| Regulation Number | 21 CFR 892.2050 |
| Product Code | LLZ |
| Regulatory Class | II |
1.4 Device Description
NS-HGlio is a non-invasive software as a medical device (SaMD) tool intended for labeling, visualization, and volumetric quantification of high-grade brain gliomas for a population that has been pathologically diagnosed to have brain tumors. The device is used
4
as a tool by clinicians in determining the patient's disease conditions on pre- and post-operative MRI images. The device is not used for primary diagnosis.
NS-HGlio device takes as an input imported Digital Imaging and Communications in Medicine (DICOM) images of high-grade brain glioma acquired with standard brain tumor MRI protocols and uses a deep learning methodology to semi-automatically label the different subcomponents of the high-grade glioma. Results are displayed on a Neosoma viewing software. Optionally, the software connects to clinicians' applications (e.g., PACS).
1.5 Indications for Use
NS-HGlio is intended for the semi-automatic labeling, visualization, and volumetric quantification of high-grade brain glioma (WHO grade 3 astrocytoma, WHO grade 4 astrocytoma and WHO grade 4 glioblastoma) from a set of standard MRI images of male or female patients 18 years of age or older who are known to have pathologically proven high-grade glioma. Volumetric measurements may be compared to past measurements if available. NS-HGlio is not to be used for primary diagnosis, and is intended to be used by qualified clinical personnel as an additional source of information and is not intended to be the sole diagnostic metric.
| Feature/
Function | Subject Device:
NS-HGlio | Predicate Device
NeuroQuant manufactured by
CorTechs Labs
K170981 |
|----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|
| Type of Scans | MRI:
Acquired using four different MRI
sequences either in 2D or 3D using
a specified protocol
(T1 pre-contrast, T1 post-contrast,
T2 and FLAIR) | MRI:
Neuroquant: 3D T1 pre-contrast scans
acquired with specified protocols
Lesionquant: 3D FLAIR scan acquired
with specified protocol |
| Intended
Anatomy | Brain | Brain |
| Lesion Review | 2D and 3D | 2D |
1.6 Comparison of Technological Characteristics with the Predicate Device
5
| Feature/
Function | Subject Device:
NS-HGlio | Predicate Device
NeuroQuant manufactured by
CorTechs Labs
K170981 |
|----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Segmentation | Semi-automatic and manual
segmentation of high-grade glioma | Semi-automatic and manual
segmentation of brain structures |
| Quantification | Volumetric measurement of the
sub-components of high-grade
glioma | Automated measurement of brain
tissue volumes and structures and
lesions |
| Output | - Provides volumetric
measurements of high-grade
brain glioma and
sub-components
- Includes segmented color
overlays of sub-components and
reports - Automatically compares results
to prior scans when available | - 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 |
| Image Format | DICOM | DICOM |
| Report | YES | YES |
1.7 Performance Data
Safety and performance of NS-HGlio have been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing. Additionally, the software validation activities were performed in accordance with IEC 62304:2006/AC:2015 - Medical device software -Software life cycle processes, in addition to the FDA Guidance document, "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
6
The testing dataset consisted of 33 subjects and 132 MRIs used for the evaluation of the machine learning model performance. The test dataset was acquired from medical sites that were not included in the training dataset to ensure device generalizability. The data were acquired using standard of care MRI protocols on Siemens, GE, and Toshiba scanners. Following the real world prevalence of the high grade glioma, the data consisted of more males than females within the age range of 18 to 79 and covering a diverse group of ethnic backgrounds.
The reference standard (ground truth) was established using three US board certified neuroradiologists with expertise in measuring high grade gliomas. The dataset was evaluated using the DSC (Dice Similarity Coefficient) assessing the degree of overlap between device output and the reference standard as well as the Intraclass correlation coefficient (ICC) of the device output volumes and the reference standard.
The device achieved a mean DSC of 0.88 with 95% CI of 0.86-0.90 on preoperative imaging and 0.80 with 95% CI of 0.77-0.83 on postoperative imaging which is higher than the mean DSC of the average of the three experts for the same task, which was 0.84 on preoperative imaging and 0.74 for postoperative imaging respectively. The mean ICC was 0.98 with 95% CI of 0.97-0.99.
1.8 Conclusion
Based on the information submitted in this premarket notification, and based on the indications for use, technological characteristics, and performance testing, NS-HGlio raises no new questions of safety or effectiveness and is substantially equivalent to the predicate device in terms of safety, efficacy, and performance.