(251 days)
Not Found
Yes
The document explicitly mentions "AI" multiple times in the device description and performance study sections, and also refers to "machine learning model performance" in the context of the predicate device.
No.
This device is intended for semi-automatic labeling, visualization, and volumetric quantification of glioblastoma from MRI images, serving as an aid in tumor contouring and not for primary diagnosis or as the sole diagnostic metric. It processes existing medical images to provide quantitative information rather than directly treating or mitigating a disease.
No
Explanation: The "Intended Use / Indications for Use" section explicitly states, "MRIMath i2contour is not to be used for primary diagnosis and is not intended to be the sole diagnostic metric." Furthermore, the "Device Description" clarifies that "the software does not alter the original MRI images and is not intended for tumor detection or diagnostic purposes." It is designed as an aid in the tumor contouring process for patients already diagnosed with GBM.
Yes
The device is described as a "web-based software platform" and its function is solely focused on processing and analyzing existing MRI images. There is no mention of any accompanying hardware component that is part of the device itself.
Based on the provided information, this device is not an In Vitro Diagnostic (IVD).
Here's why:
- IVDs are used to examine specimens derived from the human body. The MRIMath i2contour processes medical images (MRI scans) of the patient's brain, not biological samples like blood, urine, or tissue that are taken from the body and tested in vitro (in a lab setting).
- The intended use is for image processing and analysis. The device is designed to assist in the semi-automatic labeling, visualization, and volumetric quantification of a known tumor from existing MRI images. This is a form of medical image analysis, not a diagnostic test performed on a biological specimen.
- The device is not used for primary diagnosis. The intended use explicitly states that it is "not to be used for primary diagnosis and is not intended to be the sole diagnostic metric." IVDs are often used to aid in or confirm a diagnosis based on the analysis of biological samples.
While the device uses AI and processes medical data, its function falls under the category of medical image processing and analysis software, which is distinct from In Vitro Diagnostics.
No
The letter does not explicitly state that the FDA has reviewed and approved or cleared a PCCP for this specific device.
Intended Use / Indications for Use
MRIMath i2contour is intended for the semi-automatic labeling, visualization, and volumetric quantification of WHO grade 4 glioblastoma (GBM) 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 glioblastoma. Volumetric measurements may be compared to past measurements if available. MRIMath i2contour is not to be used for primary diagnosis and is not intended to be the sole diagnostic metric.
Product codes (comma separated list FDA assigned to the subject device)
QIH
Device Description
The MRIMath i2Contour is a web-based software platform designed for the contouring and segmentation of the T1c and FLAIR sequences of the MRIs of patients already diagnosed with GBM. It combines AI with a user interface (UI) for review, manual contouring, and approval. The software is intended to be used by trained medical professionals as an aid in the tumor contouring process. Review by a trained professional is a requirement for completion. The AI algorithm within MRIMath i2Contour generates an initial tumor contour, which serves as a starting point for medical professionals to complete the contouring process manually. It is important to note that the software does not alter the original MRI images and is not intended for turnor detection or diagnostic purposes. MRIMath i2Contour is specifically designed to generate turnor volume contours for GBM. It is not intended for use with images of other brain tumor types.
Mentions image processing
Yes
Mentions AI, DNN, or ML
Yes
Input Imaging Modality
MRI
Anatomical Site
Brain
Indicated Patient Age Range
18 years of age or older
Intended User / Care Setting
Trained medical professionals
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
To evaluate the accuracy of the MRIMath i2Contour FLAIR and T1c AI contours, we compare their outputs with the manual segmentations by three board certified neuro-radiologists of 46 pre- and post-operative MRIs of patients diagnosed with glioblastoma multiforme. The manual contours were performed using the MRIMath smart manual contouring platform. The test MRIs were obtained at 19 centers in the United States located at 13 community hospitals and clinics, 4 imaging centers, and two university hospitals and clinics. The details are as follows: University of Alabama at Birmingham Hospital and Clinics, Birmingham, AL (n=25), MD Anderson Cancer Center, Houston, TX (n = 1), St Vincent Hospital, Birmingham, AL (n =2), Southwest Diagnostic Imaging Center, Dallas, TX (n = 1), Thomas Medical Center, Fairhope, AL (n=1), Carmichael Imaging Center, Montgomery, Alabama (n=1), East Alabama Medical Center, Opelika, AL (n=1), St Dominic, Jackson, MS (n=1), Mobile Infirmary, Mobile, AL (n=1), North Mississippi Medical Center Tupelo, MS (n=1), Sacred Heart Airport Medical Center, Pensacola, FL (n=1), SHHP (n=2), LX DCH, Tuscaloosa, AL (n=1), Leeds Imaging Center, Leeds, AL (n=1), Black Warrior Medical Center, Tuscaloosa, AL (n=1), American Health Imaging, Birmingham, AL (n=1), Main (n=1), Floyd Medical Center, Rome, GA (n=1), Trinity Medical Center, Birmingham, AL (n=1), Jackson Hospital, Jackson, MS (n=1).
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
We evaluate the AI models developed by MRIMath for GBM T1c and fluid attenuation inversion recovery (FLAIR) images, by comparing their contours to three neuro-radiologists, who used the MRIMath smart manual contouring platform. We test the hypothesis that the proportion of overall AI DICE score (DSC) measurements that exceed the designated threshold of po-0.88 is different from 50%. The designated threshold is the best mean DSC achieved by the predicate device. The two-sided, one-sample Z-test shows that:
- For the FLAIR AI, the DSC proportions exceed p0, 85% of the time, with a confidence interval (CI) of (72%, 92%) and p-value of
§ 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
August 8, 2024
Image /page/0/Picture/1 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
MRIMath LLC % Paul Dryden Consultant ProMedic Consulting LLC 131 Bay Point Dr NE Saint Petersburg, Florida 33704
Re: K233822
Trade/Device Name: i2Contour Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: July 9, 2024 Received: July 10, 2024
Dear Paul Dryden:
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 (the 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 available 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.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
1
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
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 of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 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 Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 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,
Ningzhi Li-S
for
Daniel Krainak, Ph.D. Assistant Director 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
2
Enclosure
3
Indications for Use
| Submission Number ( if known ) | |
|---|---|
| --------------------------------------- | -- |
Device Name
i2Contour
Indications for Use (Describe)
MRIMath i2contour is intended for the semi-automatic labeling, visualization, and volumetric quantification of WHO grade 4 glioblastoma (GBM) 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 glioblastoma. Volumetric measurements may be compared to past measurements if available. MRIMath i2contour is not to be used for primary diagnosis 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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4
510(k) Summary Page 1 of 5
7-Aug-24
MRIMath LLC 3473 Birchwood Lane Birmingham, AL 35243 Phone: 773-484-8461
Sponsor Contact:
Hayat Rahal - RA
Submission Contact:
Paul Dryden ProMedic, LLC 131 Bay Point Dr NE St. Petersburg, FL 33704
Subject Device
Name of Device: i2Contour Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management and Processing System Product Code: QIH Class: II
Predicate Device
Neosoma, Inc. NS-HGlio - K221738 Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management and Processing System Product Code: QIH Class: II
Device Description
The MRIMath i2Contour is a web-based software platform designed for the contouring and segmentation of the T1c and FLAIR sequences of the MRIs of patients already diagnosed with GBM. It combines AI with a user interface (UI) for review, manual contouring, and approval. The software is intended to be used by trained medical professionals as an aid in the tumor contouring process. Review by a trained professional is a requirement for completion.
The AI algorithm within MRIMath i2Contour generates an initial tumor contour, which serves as a starting point for medical professionals to complete the contouring process manually. It is important to note that the software does not alter the original MRI images and is not intended for turnor detection or diagnostic purposes. MRIMath i2Contour is specifically designed to generate turnor volume contours for GBM. It is not intended for use with images of other brain tumor types.
Indications for Use
MRIMath i2contour is intended for the semi-automatic labeling, visualization, and volumetric quantification of WHO grade 4 glioblastoma (GBM) 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 glioblastoma.
Volumetric measurements may be compared to past measurements if available. MRIMath i2contour is not to be used for primary diagnosis and is not intended to be the sole diagnostic metric.
Comparison to Predicate Device
We have selected the Neosoma - NS-HGlio, K221738 as the predicate. We have compared the features and performance in the table below.
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510(k) Summary Page 2 of 5
| Subject Device | Predicate | Comparison | |
|---|---|---|---|
| i2Contour | Neosoma, Inc. - NS-HGlio | ||
| K# | K221738 | ||
| Product Code | QIH | QIH | |
| CFR | 21 CFR 892.2050 | 21 CFR 892.2050 | |
| Classification | Medical image management and processing system | Medical image management and processing system | |
| Indications for Use | MRIMath i2contour is intended for the semi-automatic | ||
| labeling, visualization, and volumetric quantification | |||
| of WHO grade 4 glioblastoma (GBM) 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 glioblastoma. | |||
| Volumetric measurements may be compared to past | |||
| measurements if available. MRIMath i2contour is not | |||
| to be used for primary diagnosis and is not intended to | |||
| be the sole diagnostic metric. | 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. | Similar except the subject | ||
| device is specific for | |||
| glioblastoma (GBM) | |||
| Patients | Male or female patients 18 years of age or older who | ||
| are known to have pathologically proven glioblastoma | Male or female patients 18 years of age or older who | ||
| are known to have pathologically proven high-grade | |||
| glioma | Similar with the subject device | ||
| is limited to GBM | |||
| Type of Scans Used | MRI: | ||
| Acquired using two different MRI sequences either in | |||
| 2D or 3D using a specified protocol: T1 post-contrast | |||
| (T1c) or FLAIR. | MRI: | ||
| Acquired using four different MRI sequences either | |||
| in 2D or 3D using a specified protocol: T1, T2, T1 | |||
| post-contrast (T1c) and FLAIR. | Different | ||
| Intended Anatomy | Brain | Brain | Similar |
| Lesion Review | 2D and 3D | 2D and 3D | Similar |
| Segmentation | Semi-automatic and manual segmentation of | ||
| glioblastoma | Semi-automatic and manual segmentation of high- | ||
| grade glioma | Similar | ||
| Quantification | Volumetric measurements of the combination of the | ||
| enhancing and necrosis subcomponents of the T1c, and | |||
| the combination of edema, tumor, and necrosis | |||
| subcomponents in the FLAIR images of glioblastoma | Volumetric measurement of the edema, necrosis, and | ||
| enhancing sub-components of high-grade glioma | Different | ||
| Subject Device | |||
| i2Contour | Predicate | ||
| Neosoma, Inc. - NS-HGlio | Comparison | ||
| Output | - Provides volumetric measurements of glioblastoma | ||
| and the tumor + edema + necrosis subcomponents in | |||
| FLAIR and the enhancing + necrosis subcomponents | |||
| in T1c series |
- Includes segmentation of the tumor + edema +
necrosis subcomponents in FLAIR and the enhancing
- necrosis subcomponents in T1c series
- Automatically compares results to prior scans when
available - Provides PDF Report of output data | - Provides volumetric measurements of glioblastoma
and the enhancing, necrosis, and edema sub-components - Includes segmentation of sub-components
- Automatically compares results to prior scans when
available - Provides PDF Report of output data | Different |
| Image Format | DICOM | DICOM | Similar |
| Input | FLAIR or T1c Series | T1, T2, FLAIR and T1c Series | Different |
| Registration | NO | YES | Different |
| Skull Stripping | NO | YES | Different |
| Number of AIs | Two | One | Different |
| Report | YES | YES | Similar |
| Indications | Grade 4 GBM | Grade 3 astrocytoma, grade 4 astrocytoma, grade 4 GBM | Different |
| Evaluation of Accuracy | Using three US board certified neuroradiologists with expertise in measuring GBM | Using three US board certified neuroradiologists with expertise in measuring high grade gliomas | Similar |
Table 1 - Comparison of Subject vs. Predicate
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7
510(k) Summary Page 4 of 5
Comparison of Technological Characteristics with the Predicate Device
AI-powered segmentation of the magnetic resonance images (MRI) of patients diagnosed with glioblastoma multiforme is the technological principle for both the subject and predicate devices. At a high level, the subject and predicate devices are based on the following same technological elements:
- The input consists of MRI of patients diagnosed with GBM. ●
- AI-powered prediction of the pixels that correspond to the tumor.
- Computing a tumor volume.
- Review and manual revisions, if needed
The following technological differences exist between the subject and predicate device:
- The subject device includes two independent AIs, one for T1c and the other for the FLAIR ● series; the predicate device consists of a single AI.
- The subject device processes individual 2D slices. The predicate device requires a 3D set of images.
- The predicate device is semi-automated as it requires skull stripping and registration. The subject device is fully automated as it does need registration nor skull stripping.
- The predicate device requires all the four series, T1, T1c, FLAIR and T2 for a single AI. The subject device has two independent AIs for T1c and FLAIR: it does not require the T1 and T2 series.
- The subject device Flair AI output is equivalent to the sum of all the sub-components of the predicate device.
- . The subject device T1c AI output is equivalent to the sum the enhancing lesion and necrosis subcomponents of the predicate device.
The Indications for use for the devices are as follows:
- Neosoma: grade 3 astrocytoma, grade 4 astrocytoma, and grade 4 GBM ●
- MRIMath: grade 4 GBM.
Performance Data
Like the predicate, the MRIMath i2Contour evaluated the accuracy of the subject device in the same manner.
The predicate evaluated using 33 subjects and 132 MRIs used for the machine learning model performance of males than females within the age range of 18 to 79. Three US board certified neuroradiologists with expertise in measuring high grade gliomas were used. The device 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.
To evaluate the accuracy of the MRIMath i2Contour FLAIR and T1c AI contours, we compare their outputs with the manual segmentations by three board certified neuro-radiologists of 46 pre- and post-operative MRIs of patients diagnosed with glioblastoma multiforme. The manual contours were performed using the MRIMath smart manual contouring platform. The test MRIs were obtained at 19 centers in the United States located at 13 community hospitals and clinics, 4 imaging centers, and two university hospitals and clinics. The details are as follows: University of Alabama at Birmingham Hospital and Clinics, Birmingham, AL (n=25), MD Anderson Cancer Center, Houston, TX (n = 1), St Vincent Hospital, Birmingham, AL (n =2), Southwest Diagnostic Imaging Center, Dallas, TX (n = 1), Thomas Medical Center, Fairhope, AL (n=1), Carmichael Imaging Center, Montgomery, Alabama (n=1), East Alabama Medical Center, Opelika, AL (n=1), St Dominic, Jackson, MS (n=1), Mobile Infirmary, Mobile, AL (n=1), North Mississippi Medical Center Tupelo, MS (n=1), Sacred Heart Airport Medical Center, Pensacola, FL
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510(k) Summary Page 5 of 5
(n=1), SHHP (n=2), LX DCH, Tuscaloosa, AL (n=1), Leeds Imaging Center, Leeds, AL (n=1), Black Warrior Medical Center, Tuscaloosa, AL (n=1), American Health Imaging, Birmingham, AL (n=1), Main (n=1), Floyd Medical Center, Rome, GA (n=1), Trinity Medical Center, Birmingham, AL (n=1), Jackson Hospital, Jackson, MS (n=1).
We evaluate the AI models developed by MRIMath for GBM T1c and fluid attenuation inversion recovery (FLAIR) images, by comparing their contours to three neuro-radiologists, who used the MRIMath smart manual contouring platform. We test the hypothesis that the proportion of overall AI DICE score (DSC) measurements that exceed the designated threshold of po-0.88 is different from 50%. The designated threshold is the best mean DSC achieved by the predicate device. The two-sided, one-sample Z-test shows that:
- · For the FLAIR AI, the DSC proportions exceed p0, 85% of the time, with a confidence interval (CI) of (72%, 92%) and p-value of