(270 days)
CDM Insights is a post-processing image analysis software that assists trained healthcare practitioners in viewing. analyzing, and evaluating MR brain images of adults > 45 years of age.
CDM Insights provides the following functionalities:
- Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
- Quantitative comparison of brain structures and derived values with normative data from a healthy population
- Presentation of results for reporting that includes numerical values as well as visualization of these results
CDM Insights is automated post-processing medical device software that is used by radiologists, neurologists, and other trained healthcare practitioners familiar with the post-processing of magnetic resonance images. It accepts DICOM images using supported protocols and performs: automatic segmentation and quantification of brain structures and lesions, automatic post-acquisition analysis of diffusionweighted magnetic resonance imaging (DWI) data, and comparison of derived image metrics from multiple time-points.
The values for a given patient are compared against age-matched percentile data from a population of healthy reference subjects. White matter hyperintensities can be visualized and quantified by volume. Output of the software provides numerical values and derived data as graphs and anatomical images with graphical color overlays.
CDM Insights output is provided in standard DICOM format as a DICOMencapsulated PDF report.
The provided text describes the acceptance criteria and the study that proves the device (CDM Insights) meets these criteria. Here's a breakdown of the requested information:
1. Table of Acceptance Criteria and Reported Device Performance
| Measure | Acceptance Criteria (from primary predicate) | Reported Device Performance |
|---|---|---|
| Accuracy of Segmentation for White Matter Hyperintensities (WMH) | Mean Dice overlap score ≥ 0.58 | Mean Dice overlap score = 0.66 (SD = 0.15) |
| Accuracy of Segmentation for Cortical Regions | Mean Dice overlap score ≥ 0.58 for each region | Orbito-frontal: 0.58 (0.10) Superior-frontal: 0.72 (0.05) Sensorimotor: 0.69 (0.14) Ventral-temporal: 0.58 (0.05) Anterior-cingulate: 0.60 (0.09) Precuneus: 0.58 (0.08) Lateral-occipital: 0.59 (0.11) Medial-occipital: 0.63 (0.06) |
| Visual Ratings of Segmentation Quality and Cortical Surface Quality | Not explicitly stated in numerical form, but implied "good" or "excellent" rating for acceptance | Typically rated by neuroradiologists as "good" or "excellent" |
| Repeatability of Measurements | Not explicitly stated in numerical form but implied successful confirmation | Confirmed on a total of 121 healthy individuals with two or three repeated MRI scans. |
| Reproducibility Across MRI Scanner Models and Protocols | Not explicitly stated in numerical form but implied successful quantification | Quantified across a range of MRI scanner model and protocol parameters using scans from over 1500 unique subjects. |
| Accuracy of Percentiles (of normative data) | Not explicitly stated in numerical form but implied successful testing | Tested with almost 2000 test scans. |
2. Sample Sizes and Data Provenance
- Test Set Sample Size:
- Accuracy of segmentation: 60 cases for brain region and WMH segmentation accuracy.
- Repeatability: 121 healthy individuals.
- Reproducibility: Over 1500 unique subjects.
- Accuracy of percentiles: Almost 2000 test scans.
- Data Provenance: The data included scans acquired on different scanner models from multiple manufacturers. It included scans from a group of cognitively healthy individuals and a mix of individuals with disorders (Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, multiple sclerosis). Data were obtained from 13 different source cohorts, with 7 of these based in the USA. The text indicates that information was available on race or ethnicity for the majority of individuals, with more than 20% non-white and more than 5% Hispanic. The studies included both retrospective (existing scans) and potentially some prospective components (implied by "repeated MRI scans" for repeatability) but primarily seems based on existing datasets.
3. Number of Experts and Qualifications for Ground Truth
- Number of Experts: Not explicitly stated as a number, but referred to as "US board-certified neuroradiologists."
- Qualifications of Experts: US board-certified neuroradiologists. Their years of experience are not specified.
4. Adjudication Method for the Test Set
The document does not explicitly describe an adjudication method (e.g., 2+1, 3+1). It states "tested against a gold standard of US board-certified neuroradiologists," implying their consensus or individual expert delineation as the ground truth.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The provided text does not mention a multi-reader, multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance. The study focuses on the standalone performance of the algorithm.
6. Standalone (Algorithm Only) Performance Study
Yes, a standalone performance study was done. The performance metrics (Dice scores, visual ratings) are presented for the algorithm's output directly against expert-established ground truth, without a human-in-the-loop component described in these performance tests.
7. Type of Ground Truth Used
The ground truth used for accuracy assessments (segmentation and cortical surfaces) was established by expert consensus/delineation (a "gold standard of US board-certified neuroradiologists").
8. Sample Size for the Training Set
The "almost 2000 test scans" for percentile accuracy are explicitly stated to be "independent of training scans used to derive percentiles." However, the sample size for the training set itself is not specified in this document.
9. How Ground Truth for Training Set Was Established
The document states that the percentiles were "used to derive percentiles," implying that the training set was used to construct the normative data. However, the method for establishing ground truth within that training set (e.g., for segmentation, if those were part of the training) is not detailed. It's implied that the normative data itself serves as the "ground truth" for the percentile comparisons.
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Image /page/0/Picture/0 description: The image shows 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 FDA logo is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
December 6, 2024
Oxford Brain Diagnostics Ltd % Yulia Nikova Regulatory Affairs Manager Ken Blocking Consulting LLC 3400 N Central Expy Suite #110-225 Richardson, Texas 75080
Re: K240680
Trade/Device Name: CDM Insights Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: QIH Dated: November 4, 2024 Received: November 5, 2024
Dear Yulia Nikova:
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.
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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).
Your device is also subject to, among other requirements, the Quality System (OS) 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.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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 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-regulatory
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assistance/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,
D. R. X.
Daniel M. Krainak, PhD 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
Enclosure
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Indications for Use
510(k) Number (if known) K240680
Device Name CDM Insights
Indications for Use (Describe)
CDM Insights is a post-processing image analysis software that assists trained healthcare practitioners in viewing. analyzing, and evaluating MR brain images of adults > 45 years of age.
CDM Insights provides the following functionalities:
- · Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
- · Quantitative comparison of brain structures and derived values with normative data from a healthy population
- · Presentation of results for reporting that includes numerical values as well as visualization of these results
| Type of Use (Select one or both, as applicable) | |
|---|---|
| ------------------------------------------------- | -- |
X Prescription Use (Part 21 CFR 801 Subpart D)
| Over-The-Counter Use (21 CFR 801 Subpart C)
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Image /page/4/Picture/0 description: The image is a logo for Oxford Brain Diagnostics. The logo features a stylized brain graphic on the left, composed of interconnected circles in white and gray, with one circle in orange. To the right of the brain graphic, the text "oxford brain diagnostics" is written in white against a dark blue background. The text is in a sans-serif font, with "oxford brain" on the first line and "diagnostics" on the second line.
This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of Safe Medical Devices Act of 1990 and 21 CFR §807.92.
| Applicant/Sponsor: | Oxford Brain Diagnostics LtdThe Oxford Centre for InnovationNew Road, Oxford. OX1 1BY United Kingdom | ||
|---|---|---|---|
| Contact Person: | Terry PollardChief Operating OfficerTEL: +44 (0)1865 261400 | ||
| Date Prepared: | December 3rd, 2024 | ||
| Subject Device: | Manufacturer: | Oxford Brain Diagnostics Ltd | |
| Trade Name: | CDM Insights | ||
| Product Name: | CDM Insights | ||
| Classification Name: | Medical Image Management and Processing System | ||
| Classification Panel: | Radiology | ||
| Product Code: | QIH | ||
| Regulation: | 21 CFR §892.2050 | ||
| Class: | II | ||
| Prescription or OTC Use: | Rx | ||
| Predicate Device: | Clearance: | K213706 | |
| Clearance Date: | April 15, 2022 | ||
| Manufacturer: | Siemens Healthcare GmbH | ||
| Trade Name: | Al-Rad Companion Brain MR | ||
| Product Name: | Al-Rad Companion Brain MR | ||
| Classification Name: | Medical Image Management and Processing System | ||
| Classification Panel: | Radiology | ||
| Product Code: | QIH | ||
| Regulation: | 21 CFR §892.2050 | ||
| Class: | II | ||
| Prescription or OTC Use | Rx |
Device CDM Insights is automated post-processing medical device software Description: that is used by radiologists, neurologists, and other trained healthcare practitioners familiar with the post-processing of magnetic resonance images. It accepts DICOM images using supported protocols and performs: automatic segmentation and quantification of brain structures and lesions, automatic post-acquisition analysis of diffusionweighted magnetic resonance imaging (DWI) data, and comparison of derived image metrics from multiple time-points.
The values for a given patient are compared against age-matched percentile data from a population of healthy reference subjects. White matter hyperintensities can be visualized and quantified by volume. Output of the software provides numerical values and derived data as graphs and anatomical images with graphical color overlays.
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Image /page/5/Picture/0 description: The image shows the logo for Oxford Brain Diagnostics. The logo consists of a stylized brain graphic on the left, with the words "oxford brain diagnostics" to the right of the graphic. The brain graphic is made up of interconnected circles, some of which are white, gray, and one orange. The text is in a sans-serif font and is white.
CDM Insights output is provided in standard DICOM format as a DICOMencapsulated PDF report.
Indications for CDM Insights is a post-processing image analysis software that assists Use: trained healthcare practitioners in viewing, analyzing, and evaluating MR brain images of adults > 45 years of age.
CDM Insights provides the following functionalities:
- Automated segmentation and quantitative analysis of individual brain structures and white matter hyperintensities
- . Quantitative comparison of brain structures and derived values with normative data from a healthy population
- . Presentation of results for reporting that includes numerical values as well as visualization of these results
Summary of Technological Characteristics: The following aspects of the subject CDM Insights device and the predicate devices are identical:
- Intended Use
- Technological Principle
- Device Classification Name ●
- . Classification Panel
- Regulation Number
- Product Code
- Classification
- Prescription Use
The Indications for Use and Intended Users for the CDM Insights device and the Al-Rad Companion Brain MR device use only slightly different wording:
- . The CDM Insights devices refers to "healthcare practitioners" whereas the Al-Rad Companion Brain MR refers to "clinicians" in the Indications for Use and "healthcare professionals" in the Intended Users. We consider these to be equivalent.
In our Instructions for Use we have, in the same way as our primary predicate, clarified our definition of healthcare practitioners to include "radiologists, neurologists, and other trained healthcare practitioners familiar with the post-processing of magnetic resonance images".
The following features are substantially equivalent between the CDM Insights device and the Al-Rad Companion Brain MRI device (the CDM Insights Device presents percentiles, whereas the Al-Rad Companion Brain MRI device presents z-scores):
- Brain Morphometry Segmentation
- Brain Morphometry Quantification
- Brain Morphometry: Deviation Map ●
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Image /page/6/Picture/0 description: The image is a logo for Oxford Brain Diagnostics. The logo features a stylized brain graphic on the left, composed of interconnected circles in white, gray, and orange. To the right of the brain graphic, the text "oxford brain diagnostics" is written in a sans-serif font. The background of the image is a dark blue color.
| New Device | Predicate Device | Additional Predicate Device | |
|---|---|---|---|
| Trade Name | CDM Insights | Al-Rad Companion Brain MR | OnQ Neuro |
| 510(k) Submitter[Number] | Oxford Brain DiagnosticsLtd | Siemens Healthcare GmBh | CorTechs Labs, Inc |
| 510(k) Number | K240680 | K213706 | K210831 |
| Indications forUse | CDM Insights is a post-processing image analysissoftware that assiststrained healthcarepractitioners in viewing,analyzing, and evaluatingMR brain images of adults> 45 years of age.CDM Insights provides thefollowing functionalities:- Automatedsegmentation andquantitative analysis ofindividual brain structuresand white matterhyperintensities- Quantitative comparisonof brain structures andderived values withnormative data from ahealthy population- Presentation of resultsfor reporting that includesnumerical values as well asvisualization of theseresults | Al-Rad Companion Brain MRis a post- processing imageanalysis software thatassists clinicians in viewing,analyzing, and evaluatingMR brain images.Al-Rad Companion Brain MRprovides the followingfunctionalities:- Automatic segmentationand quantitative analysis ofindividual brain structuresand white matterhyperintensities- Quantitative comparison ofeach brain structure withnormative data from ahealthy population- Presentation of results forreporting that includes allnumerical values as well asvisualization of these results | OnQ Neuro is a fully automatedpost-processing medical devicesoftware intended for analyzingand evaluating neurological MRimage data.OnQ Neuro is intended to provideautomatic segmentation,quantification, and reporting ofderived image metrics. OnQNeuro is additionally intended toprovide automatic fusion ofderived parametric maps withanatomical MRI data. OnQ Neurois intended for use on braintumors, which areknown/confirmed to bepathologically diagnosed cancer.OnQ Neuro is intended forcomparison of derived imagemetrics from multiple time-points.The physician retains the ultimateresponsibility for making the finaldiagnosis and treatment decision. |
| Intended Users | The device is intended forhealthcare practitionersfamiliar with the post-processing of magneticresonance images | The device is intended forhealthcare professionalsfamiliar with the postprocessing of magneticresonance images | Radiologists, Oncologists |
| TechnologicalPrinciple | Software | Software | Software |
| DeviceDescription | CDM Insights is automatedpost-processing medicaldevice software that isused by radiologists,neurologists, and othertrained healthcarepractitioners familiar withthe post-processing ofmagnetic resonanceimages. It accepts DICOMimages using supportedprotocols and performs: | Al-Rad Companion Brain MRVA40 is an enhancement tothe predicate, Al-RadCompanion Brain MR VA20(K193290). Just as in thepredicate, Al-Rad CompanionBrain MR addresses theautomatic quantification andvisual assessment of thevolumetric properties ofvarious brain structures basedon T1 MPRAGE datasets. In Al- | OnQ Neuro is a fully automatedpost-processing medical devicesoftware that is used byradiologists, oncologists, andother clinicians to assist withanalysis and interpretation ofneurological MR images. Itaccepts DICOM images usingsupported protocols andperforms 1) automaticsegmentation and volumetricquantification of brain tumors |
| Trade Name | New Device | Predicate Device | Additional Predicate Device |
| CDM Insights | Al-Rad Companion Brain MR | OnQ Neuro | |
| automatic segmentationand quantification of brainstructures and lesions,automatic post-acquisitionanalysis of diffusion-weighted magneticresonance imaging (DWI)data, and comparison ofderived image metricsfrom multiple time-points.The values for a givenpatient are comparedagainst age-matchedpercentile data from apopulation of healthyreference subjects. Whitematter hyperintensitiescan be visualized andquantified by volume.Output of the softwareprovides numerical valuesand derived data as graphsand anatomical imageswith graphical coloroverlays.CDM Insights output isprovided in standardDICOM format as aDICOM-encapsulated PDFreport. | Rad Companion Brain MRVA40, the quantification andvisual assessment extends towhite matter hyperintensitieson the basis of T1 MPRAGEand T2 weighted FLAIRdatasets. These datasets areacquired as part of a typicalhead MR acquisition. Theresults are directly archived inPACS as this is the standardlocation for reading byradiologist. From a predefinedlist of 30 structures (e.g.Hippocampus, Left FrontalGrey Matter, etc.), volumetricproperties are calculated asabsolute and normalizedvolumes with respect to thetotal intercranial volume. Thenormalized values for a givenpatient are compared againstage-matched mean andstandard deviations obtainedfrom a population of healthyreference subjects.The white matterhyperintensities can bevisualized as a 3D overlay mapand the quantification incount and volume as per 4brain regions in the report.As an update to the previouslycleared device, the followingmodifications have beenmade:1. Modified IntendedUse Statement2. Addition of whitematter hyperintensitiesoverlay map, count andvolume as per 4 brain regions3. Enhanced DICOMStructured Report (DICOM SR)4. Updated deploymentstructure | which are known/confirmed tobe pathologically diagnosedcancer, 2) automatic post-acquisition analysis of diffusion-weighted magnetic resonanceimaging (DWI) data andoptional automated fusion ofderived image data withanatomical MR images, and 3)comparison of derived imagemetrics from multiple time-points.Output of the softwareprovides values as numericalvolumes, and images of deriveddata as grayscale intensitymaps and as graphical coloroverlays on top of theanatomical image. OnQ Neurooutput is provided in standardDICOM format as image seriesand reports that can bedisplayed on most third-partycommercial DICOMworkstations | |
| BrainMorphometrySegmentation | Pre-processingfunctionality for automaticsegmentation andvolumetry of T1-weighted | Pre-processing functionalityfor automatic segmentationand volumetry of MPRAGEdata | Software performs automaticsegmentation |
| Trade Name | New Device | Predicate Device | Additional Predicate Device |
| CDM Insights | MRI data, e.g. MPRAGE orsimilar acquisition data. | Al-Rad Companion Brain MR | OnQ Neuro |
| BrainMorphometryQuantification | Calculation of label maps(display of brainsegmentation) andpartially combined labelmaps (fused with theprocessed T1-weightedMRI data, e.g. MPRAGE orsimilar acquisition. | Calculation of label maps(display of brainsegmentation) and partiallycombined label maps (fusedwith the processed MPRAGEdata). | Software performs automaticquantification |
| BrainMorphometry:Deviation Map | Calculation of deviationmap (representation ofbrain status in relation toreference data) andpartially combineddeviation maps (fusedwith the processed T1-weighted MRI data, e.g.MPRAGE or similaracquisition) | Calculation of deviation map(representation of brainstatus in relation toreference data) and partiallycombined deviation maps(fused with the processedMPRAGE data) Usercustomizable color labels forthe overlay map | Unknown |
| Diffusion Analysis | Yes, using a single-compartment diffusionmodel. | No | Yes, using single and multi-compartment diffusion models. |
| SupportLongitudinalAnalysis | Yes, performs comparisonof derived image metricsfrom multiple time points | No | Yes, performs comparison ofderived image metrics frommultiple time points. |
| Brain WhiteMatterHyperintensitiesSegmentation | Pre-processingfunctionality for automaticsegmentation andvolumetry of T1-weighteddata, that includesMPRAGE or similaracquisition, and FLAIRdata. (volumetry of whitematter hypointensities ifFLAIR data is not available) | Pre-processing functionalityfor automatic segmentationand volumetry of MPRAGEand FLAIR data. | Information on White MatterHyperintensities is not provided. |
| Brain WhiteMatterHyperintensitiesQuantification | Calculation of whitematter hyperintensitiesvolume (white matterhypointensities volume ifFLAIR data is notavailable). | Calculation of white matterhyperintensities count andvolume as per 4 brainregions. | Information on White MatterHyperintensities is not provided. |
| Brain WhiteMatterHyperintensitiesMap | Calculation of whitematter hyperintensitiesmap fused with theprocessed FLAIR data (orwhite matterhypointensities map fusedwith the processed T1-weighted data). Colorlabels for the overlay map. | Calculation of white matterhyperintensities map fusedwith the processed FLAIRdata User customizablecolor labels for the overlayтар. | Information on White MatterHyperintensities is not provided. |
| New Device | Predicate Device | Additional Predicate Device | |
| Trade Name | CDM Insights | Al-Rad Companion Brain MR | OnQ Neuro |
| Distribution & Archiving | Creation of an image series for a report.Automatic transfer of report to a PACS system. | Creation of an image series for a morphometry report.Automatic transfer of generated maps and morphometry report to a PACS system. | The software is configured at installation to receive input DICOM files from a network location, and output DICOM to a network destination. |
| User Interface Confirmation | No User Interface (UI) | Confirmation UI with basic visualization functionality | The software is designed without the need for a user interface after installation |
| User Interface Configuration | No User Interface (UI) | Configuration UI | The software is designed without the need for a user interface after installation |
| Architecture | Cloud solution | Cloud solution and Edge components deployed on customer premise. | Cloud solution or within a hospital's IT infrastructure on a server or PC-based workstation |
| Report Type | DICOM-encapsulated PDF report | DICOM structured report representation of a natural language report | Standard DICOM format as image series and reports |
| Physical Characteristics | CDM Insights is a medical device software package | Al-Rad Companion Brain MR VA40 is a medical device software package | The OnQ Neuro is a stand-alone medical device software package |
| Installation | Cloud | Server | Cloud or Server |
| Data Source | DICOM images using supported protocols | DICOM images using supported protocols | DICOM images using supported protocols |
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Image /page/7/Picture/0 description: The image shows the logo for Oxford Brain Diagnostics. The logo consists of a stylized brain graphic on the left, with the words "oxford brain diagnostics" to the right of the graphic. The brain graphic is made up of interconnected circles in white, gray, and orange. The text is in a sans-serif font and is white.
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Image /page/8/Picture/0 description: The image is a logo for Oxford Brain Diagnostics. The logo features a stylized brain made up of interconnected circles in white, gray, and orange. To the right of the brain graphic, the text "oxford brain diagnostics" is written in a clean, sans-serif font, with "oxford" and "brain" in a smaller font size than "diagnostics". The background is a solid dark blue.
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Image /page/9/Picture/0 description: The image shows the logo for Oxford Brain Diagnostics. The logo consists of a stylized brain graphic on the left, with the words "oxford brain diagnostics" to the right of the graphic. The brain graphic is made up of interconnected circles in white, gray, and orange. The text is in white.
Summary of Non-clinical tests were conducted to confirm the functionality of CDM Insights. Non-Clinical Software validation and bench testing were conducted to assess the performance Test Data: claims as well as the claim of substantial equivalence to the predicate device.
CDM Insights was developed to meet the requirements of multiple voluntary FDA Recognized Consensus Standards (i.e., ISO 13485, ISO 14971 and IEC 62304), as well as the FDA guidance document "Design Control Guidance for Medical Manufacturers." Non-clinical performance testing was conducted in compliance with IEC 62304, as well as the FDA guidance document "General Principles of Software Validation."
Software documentation has been provided for CDM Insights in compliance with the FDA guidance document "Content of Premarket Submissions for Device Software Functions" at the Basic Documentation Level, according to that guidance document. The documentation (including system-level software validation that included previously determined acceptance criteria) demonstrated conformance with FDA expectations for medical devices containing software functions. In addition, unit and integration testing was conducted during software development. Through these many activities, the CDM Insights Software Requirements Specifications were confirmed as being successfully executed in the developed and tested software, including mitigations as determined necessary through software risk management activities.
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Image /page/10/Picture/0 description: The image shows the logo for Oxford Brain Diagnostics. The logo consists of a stylized brain graphic on the left, made up of interconnected circles in white, gray, and orange. To the right of the graphic is the text "oxford brain diagnostics" in a clean, sans-serif font, with the words arranged on a single line. The background of the image is a solid dark blue.
Cybersecurity documentation has been provided for CDM Insights in compliance with the FDA guidance document "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions." Medical device cybersecurity activities for CDM Insights were conducted to meet the requirements of multiple voluntary FDA Recognized Consensus Standards (i.e., AAMI TIR57 and ANSI AAMI SW96), as well as the FDA guidance documents "Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software," "Postmarket Management of Cybersecurity in Medical Devices" and "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions."
Performance testing of CDM Insights has been conducted to assess accuracy of image processing, run/rerun and scan/rescan repeatability of the underlying measurements, reproducibility across varying MRI scanner models and MRI protocols, statistical accuracy of percentiles of normative data. Each of these aspects of performance testing is summarized below.
Accuracy of segmentation for brain regions and for white matter hyperintensities, and accuracy of cortical surfaces, was tested against a gold standard of US boardcertified neuroradiologists, using a total of 60 cases comprising a group of cognitively healthy individuals and a mix of individuals with disorders including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and multiple sclerosis. Testing data included scans acquired on different scanner models, multiple manufacturers and at field-strengths of 1.5 and 3 tesla. White matter hyperintensities were automatically segmented with mean (standard deviation, SD) Dice overlap score of 0.66 (0.15), that exceeded the acceptance criterion of 0.58 taken from the primary predicate. For eight representative cortical regions, the mean (SD) Dice scores were as follows: Orbito-frontal 0.58 (0.10), Superior-frontal 0.72 (0.05), Sensorimotor 0.69 (0.14), Ventral-temporal 0.58 (0.05), Anterior-cingulate 0.60 (0.09), Precuneus 0.58 (0.08), Lateral-occipital 0.59 (0.11), Medial-occipital 0.63 (0.06). All regional mean Dice scores passed the acceptance threshold of 0.58. Visual ratings of segmentation quality and of cortical surface quality were typically rated by neuroradiologists as "good" or "excellent".
Repeatability was confirmed on a total of 121 healthy individuals with two or three repeated MRI scans. Reproducibility was tested with scans from over 1500 unique subiects (58% female) aged 45 to 90 vears. Reproducibility was quantified across a range of MRI scanner model and protocol parameters.
Accuracy of percentiles was tested with almost 2000 test scans, independent of training scans used to derive percentiles. Information was available on race or ethnicity for the majority of individuals in both the training and test samples: more than 20% were non-white, and more than 5% were Hispanic. Data were obtained from 13 different source cohorts, 7 of which were based in the USA.
All performance tests were successfully passed in relation to pre-specified acceptance criteria, demonstrating safety and effectiveness substantially equivalent to the primary predicate.
Safety and The device labeling contains instructions for use and any necessary cautions and Effectiveness: warnings to ensure safe and effective use of the device. Risk management is ensured via ISO 14971:2019 compliance to identify and provide mitigation of
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Image /page/11/Picture/0 description: The image is a logo for Oxford Brain Diagnostics. The logo features a stylized brain made up of interconnected circles in white, gray, and orange. To the right of the brain graphic, the text "oxford brain diagnostics" is written in a clean, sans-serif font, with the words in lowercase.
potential risks in a risk analysis in the design phase and continuously throughout the development of the product. These risks are controlled via measures realized during software development, testing and product labeling, and risk control will continue throughout the life of the device.
Furthermore, the device is intended for healthcare practitioners familiar with the post-processing of magnetic resonance images.
The test results in this 510(k) premarket notification demonstrate that the CDM Insights: 1) complies with the international and FDA-recognized consensus standards and FDA guidance documents, as listed on the CDRH Premarket Review Submission Cover Sheet Form, and 2) meets the pre-defined acceptance criteria and is adequate for its intended use and specifications.
- Conclusion: Oxford Brain Diagnostics considers CDM Insights to be substantially equivalent to the predicate device(s) listed above. This conclusion is based on the similarities in primary intended use, principles of operation, functional design, and established medical use.
§ 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).