(427 days)
THINQ is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. Volumetric measurements may be compared to reference percentile data.
THINQ™ is a software-only, non-interactive, medical device for quantitative imaging, accepting as input 3D T1-weighted MRI scan data of the human head. THINQ™ produces as output a quantitative neuromorphometry report in PDF format. The report contains morphometric (volume) measurements and visualizations of various structures in the brain, and compares these measures to age and gender-matched reference percentile data. The report includes images of the brain with color-coded segmentations, as well as plots showing how measurements compare to reference data. Additionally, in order to visually confirm the accuracy of the results, three segmentation overlays are created in DICOM-JPEG format; one in each anatomical plane: sagittal, coronal and axial.
The THINQ™ processing pipeline performs an atlas-based segmentation of brain structures followed by measurement of those structures and a comparison to a reference dataset. The pipeline includes automated QA checks on the input DICOM 3D T1 MRI series to ensure adherence to imaging sequence requirements, checks on the data elements generated during the processing pipeline, and usage of a classifier to filter potentially incorrect reports due to corrupted image input.
THINQ™ is packaged as a container, for deployment and operation in a high-performance computing environment within a clinical workflow.
Here's a summary of the acceptance criteria and study details for the THINQ device, based on the provided text:
1. Acceptance Criteria and Device Performance
The provided document does not explicitly state acceptance criteria in a quantitative format (e.g., "Dice similarity coefficient must be >= 0.85"). Instead, it presents the reported device performance and implies that these results meet an acceptable standard, likely derived from a "literature review of neuroimaging publications."
Table of Reported Device Performance
| Structure | Accuracy Metric | Mean (StDev) |
|---|---|---|
| Whole Brain | Dice | 0.94 (0.01) |
| AVE (cm³) | 327.00 (111.48) | |
| RVE | 0.30 (0.13) | |
| Total Gray Matter | Dice | 0.82 (0.02) |
| AVE (cm³) | 174.63 (46.42) | |
| RVE | 0.24 (0.05) | |
| Total White Matter | Dice | 0.87 (0.04) |
| AVE (cm³) | 65.67 (37.38) | |
| RVE | 0.18 (0.12) | |
| Left Cortical Gray Matter | Dice | 0.92 (0.06) |
| AVE (cm³) | 10.59 (6.51) | |
| RVE | 0.05 (0.03) | |
| Right Cortical Gray Matter | Dice | 0.92 (0.07) |
| AVE (cm³) | 10.43 (6.67) | |
| RVE | 0.05 (0.03) | |
| Left Frontal Lobe | Dice | 0.90 (0.06) |
| AVE (cm³) | 5.44 (3.83) | |
| RVE | 0.07 (0.05) | |
| Right Frontal Lobe | Dice | 0.90 (0.06) |
| AVE (cm³) | 5.07 (3.86) | |
| RVE | 0.06 (0.05) | |
| Left Parietal Lobe | Dice | 0.88 (0.08) |
| AVE (cm³) | 4.06 (3.04) | |
| RVE | 0.08 (0.06) | |
| Right Parietal Lobe | Dice | 0.88 (0.08) |
| AVE (cm³) | 3.85 (2.86) | |
| RVE | 0.07 (0.06) | |
| Left Occipital Lobe | Dice | 0.82 (0.07) |
| AVE (cm³) | 1.57 (1.22) | |
| RVE | 0.07 (0.05) | |
| Right Occipital Lobe | Dice | 0.82 (0.08) |
| AVE (cm³) | 1.92 (1.97) | |
| RVE | 0.08 (0.09) | |
| Left Temporal Lobe | Dice | 0.89 (0.06) |
| AVE (cm³) | 2.08 (1.89) | |
| RVE | 0.04 (0.04) | |
| Right Temporal Lobe | Dice | 0.89 (0.06) |
| AVE (cm³) | 2.11 (1.81) | |
| RVE | 0.04 (0.04) | |
| Left Cerebral White Matter | Dice | 0.86 (0.04) |
| AVE (cm³) | 32.60 (18.80) | |
| RVE | 0.18 (0.12) | |
| Right Cerebral White Matter | Dice | 0.86 (0.04) |
| AVE (cm³) | 33.07 (18.88) | |
| RVE | 0.18 (0.12) | |
| Left Lateral Ventricle | Dice | 0.86 (0.07) |
| AVE (cm³) | 2.32 (1.69) | |
| RVE | 0.17 (0.15) | |
| Right Lateral Ventricle | Dice | 0.85 (0.07) |
| AVE (cm³) | 2.19 (1.59) | |
| RVE | 0.18 (0.14) | |
| Left Hippocampus | Dice | 0.78 (0.03) |
| AVE (cm³) | 0.45 (0.29) | |
| RVE | 0.14 (0.09) | |
| Right Hippocampus | Dice | 0.79 (0.03) |
| AVE (cm³) | 0.39 (0.28) | |
| RVE | 0.12 (0.10) | |
| Left Amygdala | Dice | 0.66 (0.05) |
| AVE (cm³) | 0.60 (0.17) | |
| RVE | 0.68 (0.24) | |
| Right Amygdala | Dice | 0.64 (0.06) |
| AVE (cm³) | 0.69 (0.19) | |
| RVE | 0.74 (0.27) | |
| Left Caudate | Dice | 0.78 (0.07) |
| AVE (cm³) | 0.50 (0.35) | |
| RVE | 0.17 (0.14) | |
| Right Caudate | Dice | 0.78 (0.07) |
| AVE (cm³) | 0.53 (0.34) | |
| RVE | 0.18 (0.13) | |
| Left Putamen | Dice | 0.82 (0.04) |
| AVE (cm³) | 0.83 (0.35) | |
| RVE | 0.20 (0.10) | |
| Right Putamen | Dice | 0.82 (0.03) |
| AVE (cm³) | 0.89 (0.35) | |
| RVE | 0.21 (0.08) | |
| Left Thalamus | Dice | 0.82 (0.03) |
| AVE (cm³) | 1.51 (0.53) | |
| RVE | 0.19 (0.05) | |
| Right Thalamus | Dice | 0.83 (0.03) |
| AVE (cm³) | 1.38 (0.45) | |
| RVE | 0.18 (0.04) | |
| Left Cerebellum | Dice | 0.91 (0.02) |
| AVE (cm³) | 2.10 (1.43) | |
| Right Cerebellum | Dice | 0.92 (0.02) |
| AVE (cm³) | 2.07 (1.49) | |
| RVE | 0.03 (0.02) | |
| Intracranial Volume ICV | APD | 3.42 (2.05) |
| AVE (cm³) | 50.18 (31.92) | |
| RVE | 0.03 (0.02) |
Reproducibility Results:
| Structure | Reproducibility APD Mean (StDev) |
|---|---|
| Whole Brain | 0.34 (0.29) |
| Total Gray Matter | 0.83 (0.80) |
| Total White Matter | 1.04 (1.12) |
| Left Cortical Gray Matter | 1.08 (0.89) |
| Right Cortical Gray Matter | 1.04 (0.88) |
| Left Frontal Lobe | 1.31 (1.30) |
| Right Frontal Lobe | 1.57 (2.41) |
| Left Parietal Lobe | 1.50 (1.31) |
| Right Parietal Lobe | 1.67 (2.73) |
| Left Occipital Lobe | 1.49 (1.16) |
| Right Occipital Lobe | 2.00 (1.41) |
| Left Temporal Lobe | 1.24 (1.37) |
| Right Temporal Lobe | 1.39 (1.15) |
| Left Cerebral White Matter | 1.15 (1.09) |
| Right Cerebral White Matter | 1.10 (1.20) |
| Left Lateral Ventricle | 1.44 (1.21) |
| Right Lateral Ventricle | 1.55 (1.12) |
| Left Hippocampus | 1.56 (1.76) |
| Right Hippocampus | 1.49 (1.57) |
| Left Amygdala | 1.25 (1.23) |
| Right Amygdala | 1.72 (1.36) |
| Left Caudate | 1.14 (1.22) |
| Right Caudate | 1.24 (1.10) |
| Left Putamen | 1.41 (1.15) |
| Right Putamen | 1.29 (0.90) |
| Left Thalamus | 0.86 (0.59) |
| Right Thalamus | 0.75 (0.63) |
| Left Cerebellum | 0.62 (0.58) |
| Right Cerebellum | 0.60 (0.54) |
| Intracranial Volume ICV | 0.30 (0.29) |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: The "validation dataset was composed of 645 unique MR images."
- Data Provenance: The document states that the dataset included "a wide range of patient characteristics (e.g. age, gender, disease case) and image acquisition varieties (e.g. scanner manufacturer, image acquisition protocols, data noise and artifacts)." However, specific details such as the country of origin or whether the data was retrospective or prospective are not provided.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
- Number of Experts: The document states that performance testing involved comparisons "to expert-labeled brain images" but does not specify the number of experts used.
- Qualifications of Experts: The document refers to them as "expert-labeled," but does not provide specific qualifications (e.g., radiologist with X years of experience). It does refer to "gold-standard computer-aided expert manual segmentation" in the conclusion, implying a high standard of expertise.
4. Adjudication Method for the Test Set
The document does not specify an adjudication method for establishing ground truth, beyond stating it was "expert-labeled."
5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done
- No, an MRMC comparative effectiveness study was not explicitly mentioned or performed to assess how much human readers improve with AI vs without AI assistance. The study focuses on the standalone performance of the THINQ device (segmentation accuracy and reproducibility) against established ground truth.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Yes, a standalone study was done. The performance data presented (Dice, AVE, RVE, APD for accuracy and reproducibility) directly represents the algorithm's performance in automatically segmenting brain structures and quantifying volumes without human intervention during the segmentation process. The device explicitly states it is "a software-only, non-interactive, medical device for quantitative imaging."
7. The Type of Ground Truth Used
- Expert Consensus / Manual Segmentation: The segmentation accuracy was evaluated by comparing THINQ's output to "expert-labeled brain images" and "gold-standard computer-aided expert manual segmentation."
8. The Sample Size for the Training Set
- The document does not explicitly state the sample size for the training set. It only mentions the "validation dataset" of 645 unique MR images, which is typically distinct from the training set.
9. How the Ground Truth for the Training Set Was Established
- The document does not explicitly describe how the ground truth for the training set was established. It only mentions the process for the validation/test set, which involved "expert-labeled brain images."
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September 30, 2020
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CorticoMetrics LLC % Mr. Nick Schmansky Co-Founder, CEO 128 Granite Street ROCKPORT MA 01966
Re: K192051
Trade/Device Name: THINO Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: August 26, 2020 Received: August 31, 2020
Dear Mr. Schmansky:
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/cfpmp/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 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
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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 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-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,
For
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) K192051
Device Name THINQ
Indications for Use (Describe)
THINQ is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. Volumetric measurements may be compared to reference percentile data.
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/3/Picture/0 description: The image contains the logo for CorticoMetrics. The logo consists of a stylized brain graphic on the left, followed by the company name "CorticoMetrics" in text. The word "Cortico" is in a teal color, while "Metrics" is in a dark brown color.
THINQ™ 510(k) Summary
CorticoMetrics, LLC
August 22, 2020
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This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR §807.92:
1 Submitter
| Name | CorticoMetrics, LLC |
|---|---|
| Address | 128 Granite St., Rockport MA 01966 USA |
| Contact Person | Nick Schmansky |
| Telephone Number | 617-329-5042 |
| Fax Number | none |
| nicks@corticometrics.com | |
| Date Prepared | August 22, 2020 |
2 Device
| Device Trade Name | THINQ™ |
|---|---|
| Common Name | Medical Imaging Processing Software |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 21 CFR §892.2050 |
| Regulation Description | Picture archiving and communications system |
| Regulatory Class | Class II |
| Product Classification Code | LLZ |
| Classification Panel | Radiology |
Predicate Device 3
| Device | NeuroQuant |
|---|---|
| 510(k) Number | K170981 |
| Manufacturer | CorTechs Labs, Inc. |
| Common Name | Medical Imaging Processing Software |
| Classification Name | System, Image Processing, Radiological |
| Regulation Number | 21 CFR §892.2050 |
| Regulation Description | Picture archiving and communications system |
| Regulatory Class | Class II |
| Product Classification Code | LLZ |
| Classification Panel | Radiology |
Copyright ©2019-2020 CorticoMetrics LLC. All Rights Reserved.
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Device Description 4
THINQ™ is a software-only, non-interactive, medical device for quantitative imaging, accepting as input 3D T1-weighted MRI scan data of the human head. THINQ™ produces as output a quantitative neuromorphometry report in PDF format. The report contains morphometric (volume) measurements and visualizations of various structures in the brain, and compares these measures to age and gender-matched reference percentile data. The report includes images of the brain with color-coded segmentations, as well as plots showing how measurements compare to reference data. Additionally, in order to visually confirm the accuracy of the results, three segmentation overlays are created in DICOM-JPEG format; one in each anatomical plane: sagittal, coronal and axial.
The THINQ™ processing pipeline performs an atlas-based segmentation of brain structures followed by measurement of those structures and a comparison to a reference dataset. The pipeline includes automated QA checks on the input DICOM 3D T1 MRI series to ensure adherence to imaging sequence requirements, checks on the data elements generated during the processing pipeline, and usage of a classifier to filter potentially incorrect reports due to corrupted image input.
THINQ™ is packaged as a container, for deployment and operation in a high-performance computing environment within a clinical workflow.
Intended Use 5
THINQ™ is intended for automatic labeling, visualization and volumetric quantification of segmentable brain structures from a set of MR images. It is intended to automate the manual process of identifying, labeling and quantifying the volume of segmentable brain structures identified on MR images.
Comparison of Technological Characteristics with the б Predicate Device
| Subject Device | Predicate Device | |
|---|---|---|
| Device | THINQ™ | NeuroQuant v2.2 |
| 510(k) Number | K192051 | K170981 |
| RegulationNumber | 21 CFR §892.2050 | 21 CFR §892.2050 |
| RegulationDescription | Picture archiving andcommunications system | Picture archiving andcommunications system |
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| Subject Device | Predicate Device | |
|---|---|---|
| Device: | THINQ™ | NeuroQuant v2.2 |
| ClassificationName | System, Image Processing,Radiological | System, Image Processing,Radiological |
| Classification: | Class II | Class II |
| Product Code: | LLZ | LLZ |
| Indicationsfor Use | THINQ™ is intended for automaticlabeling, visualization andvolumetric quantification ofsegmentable brain structures froma set of MR images. Volumetricmeasurements may be comparedto reference percentile data. | NeuroQuant is intended forautomatic labeling, visualizationand volumetric quantification ofsegmentable brain structures andlesions from a set of MR images.Volumetric measurements may becompared to reference percentiledata. |
| Design andIncorporatedTechnology | • Automated measurement of braintissue volumes and structures• Automatic segmentation andquantification of brain structuresusing a probabilisticneuroanatomical atlas based onthe MR image intensity | • Automated measurement of braintissue volumes and structures andlesions• Automatic segmentation andquantification of brain structuresusing a dynamic probabilisticneuroanatomical atlas, with ageand gender specificity, based onthe MR image intensity |
| PhysicalCharacteristics | • Software package• Operates on off-the-shelfhardware (multiple vendors) | • Software package• Operates on off-the-shelfhardware (multiple vendors) |
| OperatingSystem | Supports Linux | Supports Linux, Mac OS X andWindows |
| Deployment: | Container installation | Cloud based or installed |
| ProcessingArchitecture | Automated internal pipeline thatperforms:• Artifact correction• Segmentation• Volume calculation• Report generation | Automated internal pipeline thatperforms:• Artifact correction• Segmentation• Lesion quantification• Volume calculation• Report generation |
| Data Source | • MRI scanner: 3D T1 MRI scansacquired with specified protocols• THINQ™ supports DICOM format asinput | • MRI scanner: 3D T1 MRI scansacquired with specified protocols• NeuroQuant supports DICOMformat as input |
| Subject Device | Predicate Device | |
| Device: | THINQ™ | NeuroQuant v2.2 |
| Output: | • Provides volumetricmeasurements of brain structures• Includes segmented coloroverlays and morphometric reports• Automatically compares results toreference percentile data• Report output is PDF file format• Outputs segmentation results asoverlay in DICOM EncapsulatedJPEG format allowing display onDICOM workstations and PictureArchive and CommunicationsSystem | • Provides volumetricmeasurements of brain structuresand lesions• Includes segmented coloroverlays and morphometric reports• Automatically compares results toreference percentile data and toprior scans when available• Supports DICOM format as outputof results that can be displayed onDICOM workstations and PictureArchive and CommunicationsSystems |
| Safety: | • Automated quality controlfunctions:• Scan sequence (protocol) checks• Atlas alignment checks• Cortical surface checks• Result validity checks• Results must be reviewed by atrained physician | • Automated quality controlfunctions:• Tissue contrast check• Scan protocol verification• Atlas alignment check• Results must be reviewed by atrained physician |
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Performance Data 7
THINQ™ was designed, developed and tested in accordance with the following process standards:
- 21 CFR part 820 Quality System Regulation / Medical Device Good Manufacturing Practices
- ISO 13485:2016 Medical devices Quality management systems
- ISO 14971:2007 Medical devices Application of risk management to medical devices
- ISO 62304:2006 Medical device software Software life-cycle processes
- ISO 62366-1:2015 Medical devices Part 1: Application of usability engineering to medical devices
Validation of THINQ included performance testing for accuracy, where comparisons were made to expert-labeled brain images, and reproducibility, where test-retest image data was used. The accuracy of the reference population model was validated against statistical tests of normality using both subject test data expected to align within the reference ranges, as well as subjects with neurological disorders where affected brain structures are known to lie outside
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the reference ranges. A literature review of neuroimaging publications informed the selection of acceptance criteria.
THINQ's segmentation accuracy of brain structures compared to ground-truth segmentations of 3D T1 MRI scans was evaluated using the Dice similarity coefficient metric. Accuracy of Intracranial Volume (ICV) was evaluated using the Absolute Percent Difference (APD) metric. Table 1 lists the results, including the Absolute Volume Error (AVE) and Relative Volume Error (RVE) metrics.
| Structure | Accuracy Metric | Mean (StDev) | |
|---|---|---|---|
| Whole Brain | Dice | 0.94 (0.01) | |
| AVE (cm³) | 327.00 (111.48) | ||
| RVE | 0.30 (0.13) | ||
| Total Gray Matter | Dice | 0.82 (0.02) | |
| AVE (cm³) | 174.63 (46.42) | ||
| RVE | 0.24 (0.05) | ||
| Total White Matter | Dice | 0.87 (0.04) | |
| AVE (cm³) | 65.67 (37.38) | ||
| RVE | 0.18 (0.12) | ||
| Left Cortical Gray Matter | Dice | 0.92 (0.06) | |
| AVE (cm³) | 10.59 (6.51) | ||
| RVE | 0.05 (0.03) | ||
| Right Cortical Gray Matter | Dice | 0.92 (0.07) | |
| AVE (cm³) | 10.43 (6.67) | ||
| RVE | 0.05 (0.03) | ||
| Left Frontal Lobe | Dice | 0.90 (0.06) | |
| AVE (cm³) | 5.44 (3.83) | ||
| RVE | 0.07 (0.05) | ||
| Right Frontal Lobe | Dice | 0.90 (0.06) | |
| AVE (cm³) | 5.07 (3.86) | ||
| RVE | 0.06 (0.05) | ||
| Left Parietal Lobe | Dice | 0.88 (0.08) | |
| AVE (cm³) | 4.06 (3.04) | ||
| RVE | 0.08 (0.06) | ||
| Right Parietal Lobe | Dice | 0.88 (0.08) | |
| AVE (cm³) | 3.85 (2.86) | ||
| RVE | 0.07 (0.06) | ||
| Left Occipital Lobe | Dice | 0.82 (0.07) | |
| AVE (cm³) | 1.57 (1.22) | ||
| RVE | 0.07 (0.05) | ||
| Right Occipital Lobe | Dice | 0.82 (0.08) | |
| AVE (cm³) | 1.92 (1.97) | ||
| RVE | 0.08 (0.09) | ||
| Left Temporal Lobe | Dice | 0.89 (0.06) | |
| AVE (cm³) | 2.08 (1.89) | ||
| RVE | 0.04 (0.04) | ||
| Right Temporal Lobe | Dice | 0.89 (0.06) | |
| AVE (cm³) | 2.11 (1.81) | ||
| RVE | 0.04 (0.04) | ||
| Left Cerebral White Matter | Dice | 0.86 (0.04) | |
| AVE (cm³) | 32.60 (18.80) | ||
| RVE | 0.18 (0.12) | ||
| Right Cerebral White Matter | Dice | 0.86 (0.04) | |
| AVE (cm³) | 33.07 (18.88) | ||
| RVE | 0.18 (0.12) | ||
| Left Lateral Ventricle | Dice | 0.86 (0.07) | |
| AVE (cm³) | 2.32 (1.69) | ||
| RVE | 0.17 (0.15) | ||
| Right Lateral Ventricle | Dice | 0.85 (0.07) | |
| AVE (cm³) | 2.19 (1.59) | ||
| RVE | 0.18 (0.14) | ||
| Left Hippocampus | Dice | 0.78 (0.03) | |
| AVE (cm³) | 0.45 (0.29) | ||
| RVE | 0.14 (0.09) | ||
| Right Hippocampus | Dice | 0.79 (0.03) | |
| AVE (cm³) | 0.39 (0.28) | ||
| RVE | 0.12 (0.10) | ||
| Left Amygdala | Dice | 0.66 (0.05) | |
| AVE (cm³) | 0.60 (0.17) | ||
| RVE | 0.68 (0.24) | ||
| Right Amygdala | Dice | 0.64 (0.06) | |
| AVE (cm³) | 0.69 (0.19) | ||
| RVE | 0.74 (0.27) | ||
| Left Caudate | Dice | 0.78 (0.07) | |
| AVE (cm³) | 0.50 (0.35) | ||
| RVE | 0.17 (0.14) | ||
| Right Caudate | Dice | 0.78 (0.07) | |
| AVE (cm³) | 0.53 (0.34) | ||
| RVE | 0.18 (0.13) | ||
| Left Putamen | Dice | 0.82 (0.04) | |
| AVE (cm³) | 0.83 (0.35) | ||
| RVE | 0.20 (0.10) | ||
| Right Putamen | Dice | 0.82 (0.03) | |
| AVE (cm³) | 0.89 (0.35) | ||
| RVE | 0.21 (0.08) | ||
| Left Thalamus | Dice | 0.82 (0.03) | |
| AVE (cm³) | 1.51 (0.53) | ||
| RVE | 0.19 (0.05) | ||
| Right Thalamus | Dice | 0.83 (0.03) | |
| AVE (cm³) | 1.38 (0.45) | ||
| RVE | 0.18 (0.04) | ||
| Left Cerebellum | Dice | 0.91 (0.02) | |
| AVE (cm³) | 2.10 (1.43) | ||
| Right Cerebellum | Dice | 0.92 (0.02) | |
| AVE (cm³) | 2.07 (1.49) | ||
| RVE | 0.03 (0.02) | ||
| Intracranial Volume ICV | APD | 3.42 (2.05) | |
| AVE (cm³) | 50.18 (31.92) | ||
| RVE | 0.03 (0.02) |
{9}------------------------------------------------
{10}------------------------------------------------
Table 1: Accuracy testing results
Reproducibility of brain structure segmentation of repeated 3D T1 MRI scans for same-subjects was evaluated by using the Absolute Percent Difference (APD) metric, shown in Table 2.
| Structure | Reproducibility APDMean (StDev) |
|---|---|
| Whole Brain | 0.34 (0.29) |
| Total Gray Matter | 0.83 (0.80) |
| Total White Matter | 1.04 (1.12) |
| Left Cortical Gray Matter | 1.08 (0.89) |
| Right Cortical Gray Matter | 1.04 (0.88) |
| Left Frontal Lobe | 1.31 (1.30) |
| Right Frontal Lobe | 1.57 (2.41) |
| Left Parietal Lobe | 1.50 (1.31) |
| Right Parietal Lobe | 1.67 (2.73) |
| Left Occipital Lobe | 1.49 (1.16) |
| Right Occipital Lobe | 2.00 (1.41) |
| Left Temporal Lobe | 1.24 (1.37) |
| Right Temporal Lobe | 1.39 (1.15) |
| Left Cerebral White Matter | 1.15 (1.09) |
| Right Cerebral White Matter | 1.10 (1.20) |
| Left Lateral Ventricle | 1.44 (1.21) |
| Right Lateral Ventricle | 1.55 (1.12) |
| Left Hippocampus | 1.56 (1.76) |
| Right Hippocampus | 1.49 (1.57) |
| Left Amygdala | 1.25 (1.23) |
| Right Amygdala | 1.72 (1.36) |
| Left Caudate | 1.14 (1.22) |
| Right Caudate | 1.24 (1.10) |
| Left Putamen | 1.41 (1.15) |
| Right Putamen | 1.29 (0.90) |
| Left Thalamus | 0.86 (0.59) |
| Right Thalamus | 0.75 (0.63) |
| Left Cerebellum | 0.62 (0.58) |
| Right Cerebellum | 0.60 (0.54) |
| Intracranial Volume ICV | 0.30 (0.29) |
Table 2: Reproducibility testing results
{11}------------------------------------------------
THINQ performs a quantitative imaging function. The validation of THINQ addressed typical sources of error in quantitative imaging values by including in its image testing dataset a wide range of patient characteristics (e.g. age, gender, disease case) and image acquisition varieties (e.g. scanner manufacturer, image acquisition protocols, data noise and artifacts). The validation dataset was composed of 645 unique MR images.
Conclusion 8
The performance testing presented above shows that the device is as safe, as effective, and performs as well as the predicate device, and as well as gold-standard computer-aided expert manual segmentation.
By virtue of the physical characteristics and intended use, THINQ™ is substantially equivalent to its predicate device, and its technological differences do not raise questions of safety and effectiveness.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).