(359 days)
The CorInsights MRI Medical Image Processing Software is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from MRI images. Volumetric measurements are compared to reference percentile data. CorInsights MRI is for adults age 45 to 95.
CorInsights MRI is a fully automated MR medical image processing software intended for automatic labeling, visualization and volumetric quantification of identifiable brain structures from DICOM formatted magnetic resonance images. The resulting output consists of a pdf report for review, which can be used in research and clinical use, and a DICOM image showing the anatomical structure boundaries identified by the software. The proposed device provides morphometric measurements based on T1 MRI series. The output of this software only device includes morphometric reports that provide comparison of measured volumes to age and gender-matched reference data and an image volume that has been annotated with color overlays representing each segmented region. The architecture has a proprietary automated internal process that includes artifact correction, atlas-based segmentation, volume calculation, and report generation. Quality control measures include automated quality control including image header checks to verify that the scan acquisition protocol and provided data adhere to system requirements, an image morphometry check, a tissue contrast check, and value range checks.
Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly state "acceptance criteria" as a separate, pre-defined column with specific numeric thresholds. Instead, it describes accuracy and reproducibility goals in terms of achieving certain statistical measures (ICC, DICE coefficient, mean absolute percentage difference) against ground truth or in test-retest scenarios.
I will infer the "acceptance criteria" based on the reported "Performance" values, as these are the results presented to demonstrate the device's capability against known ground truths or expected variations.
| Category | Specific Measure | Acceptance Criteria (Implied from Reported Performance) | Reported Device Performance |
|---|---|---|---|
| Accuracy | Hippocampal Volume | ||
| IntraClass Correlation (ICC) | ICC >= 0.95 (Highly correlated with ground truth) | 0.95 | |
| DICE Coefficient (Left) | DICE >= 83% (Good overlap with ground truth) | 83% (SD 2.5%) | |
| DICE Coefficient (Right) | DICE >= 83% (Good overlap with ground truth) | 83% (SD 2.7%) | |
| Mean Absolute % Difference (Left) | MAD <= 6.2% (Low deviation from ground truth) | 6.2% (SD 4.4%) | |
| Mean Absolute % Difference (Right) | MAD <= 5.9% (Low deviation from ground truth) | 5.9% (SD 5.1%) | |
| Cortical Segmentation (Total Gray Volume) | |||
| IntraClass Correlation (ICC) | ICC >= 0.99 (Highly correlated with ground truth) | 0.99 | |
| DICE Coefficient | DICE >= 95% (Excellent overlap with ground truth) | 95% (SD 1.6%) | |
| Mean Absolute % Difference | MAD <= 4.5% (Very low deviation from ground truth) | 4.5% (SD 1.8%) | |
| Cortical Subregions | |||
| DICE Coefficient Range | DICE (range) 81-93% (Good to excellent overlap) | 81-93% | |
| Mean Absolute % Difference Range | MAD (range) 4.6-13.8% (Low to moderate deviation) | 4.6% to 13.8% | |
| Intracranial Volume (ICV) | |||
| IntraClass Correlation (ICC) | ICC >= 0.89 (Strong correlation with ground truth) | 0.89 | |
| DICE Coefficient | DICE >= 95% (Excellent overlap with ground truth) | 95% (SD 1.1%) | |
| Mean Absolute % Difference | MAD <= 5.2% (Low deviation from ground truth) | 5.2% (SD 4.4%) | |
| Ventricular Accuracy (Left & Right) | |||
| IntraClass Correlation (ICC) | ICC >= 0.98 (Very strong correlation with ground truth) | 0.98 (left and right) | |
| DICE Coefficient (Left) | DICE >= 88% (Good overlap with ground truth) | 88% (SD 5.2%) | |
| DICE Coefficient (Right) | DICE >= 87% (Good overlap with ground truth) | 87% (SD 5.6%) | |
| Mean Absolute % Difference (Left) | MAD <= 13.9% (Moderate deviation from ground truth) | 13.9% (SD 9.2%) | |
| Mean Absolute % Difference (Right) | MAD <= 15.2% (Moderate deviation from ground truth) | 15.2% (SD 9.9%) | |
| Reproducibility | |||
| Average IntraClass Correlation (ICC) | ICC >= 0.97 (Very high test-retest consistency) | 0.97 | |
| DICE Coefficient | DICE >= 89% (Good test-retest overlap) | 89% (SD 4.0%) | |
| Mean Absolute % Difference Range (Across all volumes) | MAD (range) 0.7-5.8%, Average <= 2.3% (Very low test-retest variation) | 0.7% to 5.8%, Average 2.3% (SD 2.7%) |
2. Sample Size Used for the Test Set and Data Provenance
- Hippocampal Volume Accuracy: 80 subjects from the HaRP database.
- Cortical Segmentation Accuracy: 80 subjects from a variety of databases.
- Intracranial Volume (ICV) and Ventricular Accuracy: An additional 70 subjects from a variety of databases.
Total Test Set Size (approximate, with potential overlap for "variety of databases"): 80 (hippocampal) + 80 (cortical) + 70 (ICV/ventricular) = 230 subjects. The text later states "In total, more than 1,400 scans from over 1,100 individuals were used in testing of CorInsights MRI," implying that the 230 subjects mentioned above are part of this larger testing pool.
Data Provenance:
The text refers to "the HaRP database" (Boccardi, et al.) and "a variety of databases." It also mentions testing scenarios including "normal subject scans, as well as data sets expected to have below normal gray tissue volumes or above normal ventricle volumes based upon well-established literature."
While specific countries are not mentioned, the use of "HaRP database" (likely referring to the Hippocampal atrophy in patients with dementia research project which is international) and "variety of databases" suggests a diverse, likely multi-center, collection. The study appears to be retrospective, as it uses existing databases with pre-established ground truths.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
- Number of Experts: Not explicitly stated as a specific number. The text mentions "manual ground truth segmentation generated by neuroanatomy experts."
- Qualifications of Experts: "Neuroanatomy experts." No specific years of experience or titles (e.g., radiologist) are provided for these experts.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method (such as 2+1 or 3+1 consensus) for establishing the ground truth. It simply states that "manual ground truth segmentation generated by neuroanatomy experts" was used. This suggests that the ground truth for the test sets was derived from these experts' segmentations, but the process for resolving disagreements among multiple experts (if multiple were used per case) is not detailed.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not reported. The performance testing focuses solely on the device's algorithmic performance against established ground truth volumes and its reproducibility. There is no mention of human readers improving with or without AI assistance, which would be the focus of an MRMC study. A clinical investigation was explicitly stated as "not required."
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done
Yes, the reported study is a standalone performance assessment of the CorInsights MRI algorithm. All accuracy and reproducibility metrics (ICC, DICE, MAD) compare the algorithm's output directly to the "ground truth volumes" or between multiple runs of the algorithm, without involving human interpretation in the performance evaluation.
7. The Type of Ground Truth Used
The primary type of ground truth used for the test set was expert consensus/manual segmentation.
- For hippocampal volume, it was derived from "80 subjects from the HaRP database as ground truth (Boccardi, et al.)," which is typically manually segmented and curated.
- For cortical segmentation, it was "manual ground truth segmentation generated by neuroanatomy experts."
- Similarly for ICV and ventricular accuracy, it was "manual ground truth segmentation generated by neuroanatomy experts."
Additionally, for the "Validation of Volume Measurement in Clinically Relevant Cases," the device's measurements were compared against "percentile and z-score ranges expected based upon peer reviewed published literature for these data sets," which could be considered outcomes data or established clinical expectations based on literature.
8. The Sample Size for the Training Set
The document does not explicitly state the sample size of the training set used to develop the CorInsights MRI algorithm. It only details the sample size for the "Normative Reference Database Development," which lists 269 male and 331 female individuals (total 600 individuals). This database was used to establish reference percentile data for clinical comparison, but it's not explicitly stated if or how this specific cohort was used in the training of the segmentation algorithm itself.
9. How the Ground Truth for the Training Set Was Established
Since the training set size is not explicitly stated, the method for establishing its ground truth is also not detailed.
However, for the "Normative Reference Database Development" (which might overlap with training or be used for post-segmentation comparison), the ground truth for the "normal" cohort was established through:
- Clinical diagnosis of being cognitively normal.
- Confirmation of being negative for amyloid pathology.
- Confirmation of being negative for other potential confounding abnormalities (overt vascular disease, stroke, tumor, normal pressure hydrocephalus, no history of traumatic brain injury or severe neuropsychiatric illness).
This thorough screening process aimed to ensure the reference data represented genuinely normal values. It's an "expert-defined" ground truth based on clinical criteria and screening rather than manual segmentation for training purposes.
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November 20, 2020
ADM Diagnostics, Inc. % Robin Martin Co-Founder, Regulatory Strategist Kinetic Compliance Solutions, LLC PO Box 2134 MILWAUKEE WI 53201
Re: K193287
Trade/Device Name: CorInsights MRI Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: October 15, 2020 Received: October 19, 2020
Dear Robin Martin:
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
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requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) 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) K193287
Device Name CorInsights MRI
Indications for Use (Describe)
The CorInsights MRI Medical Image Processing Software is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from MRI images. Volumetric measurements are compared to reference percentile data. CorInsights MRI is for adults age 45 to 95.
| 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 | K193287 |
|---|---|
| Submission Date: | November 18, 2020 |
| Submitter Information: | |
| Submitted By: | Dawn Matthews, CEOADM Diagnostics, Inc.555 Skokie Blvd; Suite 500Northbrook, IL 60062 |
| Secondary Contact: | Robin MartinKinetic Compliance Solutions, LLCMilwaukee, WI 53201 |
| Device Information: | |
| Trade Name: | CorInsights MRI |
| Common Name: | Medical Image Processing Software |
| Classification Name: | System, Image Processing, Radiological, Picture archiving andcommunication system |
| Device Classification: | 21 CFR 892.2050 |
| Predicate Device(s): | K170981 NeuroQuantLLZ |
| Device Description: | CorInsights MRI is a fully automated MR medical imageprocessing software intended for automatic labeling, visualizationand volumetric quantification of identifiable brain structures fromDICOM formatted magnetic resonance images. The resultingoutput consists of a pdf report for review, which can be used inresearch and clinical use, and a DICOM image showing theanatomical structure boundaries identified by the software.The proposed device provides morphometric measurements basedon T1 MRI series. The output of this software only device includesmorphometric reports that provide comparison of measuredvolumes to age and gender-matched reference data and an imagevolume that has been annotated with color overlays representingeach segmented region. |
| The architecture has a proprietary automated internal process thatincludes artifact correction, atlas-based segmentation, volumecalculation, and report generation. | |
| Quality control measures include automated quality controlincluding image header checks to verify that the scan acquisitionprotocol and provided data adhere to system requirements, animage morphometry check, a tissue contrast check, and valuerange checks. | |
| Indications for Use: | The CorInsights MRI Medical Image Processing Software isintended for automatic labeling, visualization and volumetricquantification of segmentable brain structures from MR images.Volumetric measurements are compared to reference percentiledata. CorInsights MRI is intended for adults age 45 to 95. |
| Comparison to Predicate: | Functionally, both devices include the ability to automaticallylabel, visualize and conduct volumetric quantification ofsegmentable brain structures from MR images. Performancetesting outlined below demonstrated equivalent performance insegmentation accuracy and reproducibility compared to publishedpredicate values. |
| The predicate device indications include lesion analysis. Theproposed CorInsights MRI does not. There is no significant impactto safety or efficacy based on this. | |
| Both systems are used by medical professionals, such asradiologists, neurologists and neuroradiologists, as well as byclinical researchers, as a support tool in assessment of structuralMRIs. | |
| The proposed device employs the same fundamental scientifictechnology and the change in indications does not impact theintended use of the device. | |
| Performance Testing: | No mandatory performance standards have been established forthis device (Section 514 of the Act). |
| The following testing was conducted / standards complied with tosupport the substantial equivalence of the proposed device to thepredicate: | |
| Category | Performance |
| Accuracy | CorInsights MRI hippocampal volume accuracy was tested using 80 subjectsfrom the HaRP database as ground truth (Boccardi, et al.). |
| • CorInsights MRI measured hippocampal volume with an IntraClassCorrelation coefficient of 0.95 and a DICE coefficient of 83% left (standarddeviation 2.5%) and 83% right (standard deviation 2.7%) and a meanabsolute percentage difference of 6.2% left (standard deviation 4.4%) and5.9% right (standard deviation 5.1%) as compared to ground truth volumes. | |
| CorInsights MRI cortical segmentation accuracy was tested using 80subjects from a variety of databases with manual ground truth segmentationgenerated by neuroanatomy experts. | |
| • CorInsights MRI measured total gray volume with an IntraClass Correlationcoefficient of 0.99, a DICE coefficient of 95% (standard deviation 1.6%),and a mean absolute percentage difference of 4.5% (standard deviation,1.8%). | |
| • CorInsights MRI measured cortical subregions with DICE coefficientsranging from 81-93% and a mean absolute percentage difference range of4.6% to 13.8% across all regions. | |
| CorInsights MRI Intracranial Volume (ICV) and ventricular accuracy were testedusing an additional 70 subjects from a variety of databases with manual groundtruth segmentation generated by neuroanatomy experts. | |
| • CorInsights MRI measured ICV with an IntraClass Correlation coefficientof 0.89, a DICE coefficient of 95% (standard deviation 1.1%) and a meanabsolute percentage difference of 5.2% (standard deviation of 4.4%). | |
| • CorInsights MRI measured ventricular accuracy with an IntraClassCorrelation coefficient of 0.98 (left and right), a DICE coefficient of 88%left (standard deviation 5.2%) and 87% right (standard deviation 5.6%) anda mean absolute percentage difference of 13.9% left (standard deviation | |
| Category | Performance |
| 9.2%) and 15.2% right (standard deviation 9.9%) compared to ground truthvolumes. | |
| Reproducibility | In test-retest processing of two different scans from the same subject onthe same scanner on the same day, CorInsights MRI measured volumeswith an average IntraClass Correlation coefficient of 0.97, a DICEcoefficient of 89% (standard deviation 4.0%) and a mean absolutepercentage difference range of 0.7% to 5.8% with an average of 2.3%(standard deviation 2.7%) across all volumes listed in its report. |
| NormativeReferenceDatabaseDevelopment | The CorInsights MRI reference database was developed using T1 weighted MRIscans from 269 male and 331 female individuals of age 42 to 95 who wereclinically diagnosed to be cognitively normal.In addition, these individuals were confirmed to be negative for amyloid pathology,and negative for a variety of other potential confounding abnormalities includingovert vascular disease as evidenced by white matter lesions, stroke or tumor,normal pressure hydrocephalus, and with no history of traumatic brain injury orsevere neuropsychiatric illness as documented with the data set or provided in thedata set's inclusion criteria. This screening was performed to ensure that theCorInsights MRI reference represented normal values without potential influenceby disease, even when asymptomatic. |
| Validation ofVolumeMeasurementin ClinicallyRelevant Cases | Testing of CorInsights MRI included scans acquired from scanners with 1.5T and3T field strengths, using accelerated and non-accelerated acquisition sequences,and representing a variety of scanner models from Siemens, GE, and Philips.Validation testing was conducted using patient scans from ages 45 to 95, with abroad spectrum of clinical diagnoses including cognitively normal, Mild CognitiveImpairment, typical and atypical Alzheimer's disease, and non-Alzheimer'sdementias. Alzheimer's disease (AD) cases (confirmed for amyloid positivity)included late onset AD, Early Onset AD, Posterior Cortical Atrophy (PCA),Logopenic Progressive Aphasia, and Corticobasal Syndrome. Non-Alzheimer'scases included behavioral variant (bv) Frontotemporal Dementia (FTD), semanticvariant (sv) FTD, Nonfluent Primary Progressive Aphasia, Primary ProgressiveAphasia (other), amyloid negative Corticobasal Syndrome, vascular disease,moderate to severe white matter disease, and ventricular enlargement. MildCognitive Impairment (MCI) cases included early MCI, late MCI, and persons who |
| Category | Performance |
| Volumes measured by CorInsights MRI were tested using normal subject scans, aswell as data sets expected to have below normal gray tissue volumes or abovenormal ventricle volumes based upon well-established literature. These data setsincluded individuals with diagnoses of MCI, MCI who converted to a clinicaldiagnosis of AD at 12 months post-scan, late onset AD, Early Onset AD, bvFTD,svFTD, and PCA. CorInsights MRI values were compared to the percentile and z-score ranges expected based upon peer reviewed published literature for these datasets, and were confirmed to be in these ranges. Relationships between regionsbased on z-score ranking were also compared to published data and were inagreement with the literature. In all cases, the cognitively normal test group wasconfirmed not to differ from the normative reference group or reference values.In total, more than 1,400 scans from over 1,100 individuals were used in testing ofCorInsights MRI. |
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- Software testing was conducted to verify performance in accordance . with FDA's guidance document entitled "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices."
- In addition, the following standard was followed for DICOM . Conformance: DICOM NEMA PS 3.1 - 3.20 (2016) Digital Imaging and Communications in Medicine (DICOM) Set
- Animal testing was not required to demonstrate substantial . equivalence for the CorInsights MRI device.
- Performance Validation: The following performance data was . analyzed to support substantial equivalence:
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Clinical:
A clinical investigation was not required to demonstrate substantial equivalence to the predicate device.
The above testing supports that the proposed device is as safe, Conclusion: effective and performs as well as the legally marketed predicate in its intended use. Therefore, the proposed device is substantially equivalent to the predicate.
N/A