AccuBrain
Device Facts
| Record ID | K202847 |
|---|---|
| Device Name | AccuBrain |
| Applicant | Brainnow Medical Technology Limited |
| Product Code | LLZ · Radiology |
| Decision Date | Jan 15, 2021 |
| Decision | SESE |
| Submission Type | Traditional |
| Regulation | 21 CFR 892.2050 |
| Device Class | Class 2 |
| Attributes | Software as a Medical Device, Real-World Evidence |
Real-World Evidence
| Submission | Device | Sponsor | RWD Sources | RWE Use Summary | Key Tags |
|---|---|---|---|---|---|
| K202847 · Jan 15, 2021 | AccuBrain | Brainnow Medical Technology Limited | EADC-ADNI HarP (European Alzheimer's Disease Consortium - Alzheimer's Disease Neuroimaging Initiative Harmonized Protocol) dataset | The EADC-ADNI HarP dataset was used to validate the accuracy of the AccuBrain software by comparing its automated hippocampal segmentation results against expert manual segmentations. | Performance validation; Accuracy; EADC-ADNI HarP; Retrospective clinical data |
Clinical Evidence
| Study Design | Population | Comparator | Key Endpoints |
|---|---|---|---|
| EADC-ADNI HarP validation; Retrospective performance validation study | Healthy subjects, Alzheimer's disease patients, and Mild Cognitive Impairment patients; Sample Size: 135 | Expert manual segmentation | Dice coefficient for hippocampal volume segmentation accuracy |
AI Performance
| Output | Algorithm | Acceptance | Observed | Dev DS | Dev Readers | Test DS | Test Readers |
|---|---|---|---|---|---|---|---|
| Hippocampus volume quantification | Multi-atlas-based segmentation with STAPLE label fusion | — | Mean Dice coefficient of 0.89 for right, left, and total hippocampus. | Atlas pool of 300 brain MRIs from different individuals and scanners. | — | 135 data provided by the EADC-ADNI HarP. | >1 (manual segmenters) |
Indications for Use
AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report.
Device Story
AccuBrain is a fully automated post-processing software for 3D T1-weighted MRI scans; used by clinicians (radiologists, neurologists) in clinical research and routine care. Input: 3D T1 MRI series. Processing: automated pipeline including artifact correction (non-local mean filtering, N4 bias correction, histogram matching intensity normalization), multi-atlas-based segmentation (atlas pool of 300 brain MRIs), and STAPLE label fusion. Output: morphometric analysis report (PDF) with hippocampal volumes and segmented color overlays. Device operates on off-the-shelf hardware; supports DICOM. Benefits: provides objective volumetric quantification to support clinical assessment of structural MRIs.
Clinical Evidence
Bench testing only. Accuracy evaluated against manual segmentation using 135 EADC-ADNI HarP datasets; mean Dice coefficient 0.89 (std 0.03) for right, left, and total hippocampus. Reproducibility evaluated via intrascanner Coefficient of Variation (CV): 3.20% (left) and 1.23% (right). Mean percentage absolute volume differences were 4.52% (left) and 1.74% (right).
Technological Characteristics
Software-based image processing; operates on off-the-shelf hardware. Uses multi-atlas-based segmentation with STAPLE label fusion. Input: 3D T1W MRI (DICOM). Connectivity: standalone/networked. Software lifecycle: ANSI AAMI IEC 62304:2006/A1:2016 compliant. Algorithms: non-local mean filtering, N4 bias correction, histogram matching, STAPLE label fusion.
Indications for Use
Indicated for automatic labeling, visualization, and volumetric quantification of the hippocampus from T1-weighted MRI scans in patients, including those with Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI), as well as healthy subjects.
Regulatory Classification
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.
Special Controls
*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).
Predicate Devices
Submission Summary (Full Text)
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Image /page/0/Picture/0 description: The image contains the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which is a blue square with the letters "FDA" in white. To the right of the blue square is the text "U.S. FOOD & DRUG ADMINISTRATION" in blue.
BrainNow Medical Technology Limited % You Yijie General Manager Qimmiq Medical Consulting Service Co., Ltd. RM.1711, Building K. NO.101 Science Ave International Creative Valley Guangzhou, Guangdong 510663 CHINA
January 15, 2021
Re: K202847
Trade/Device Name: AccuBrain Regulation Number: 21 CFR 892.2050 Regulation Name: Picture archiving and communications system Regulatory Class: Class II Product Code: LLZ Dated: September 11, 2020 Received: December 11, 2020
Dear You Yijie:
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,
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) K202847
Device Name AccuBrain
Indications for Use (Describe)
AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of MRIs and returns an analysis report.
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
## "510(k) Summary" as required by 21 CFR Part 807.92.
# 1. Submitter's Information
## Establishment Registration Information
Name: BrainNow Medical Technology Limited Address: Unit 201, 2/F, Lakeside 2, No. 10 Science Park West Avenue, Hong Kong Science Park, Shatin, N.T., Hong Kong ZIP/Postal Code: 999077
#### Contact Person of applicant
Contact Person: Junbing Huang Telephone Number: 86-13416183887 Fax Number: 852-36221760 Email: 417731983@qq.com
#### Contact Person of the Submission:
Name: You Yijie Address: RM.1711, Building K, NO.101 Science Ave International Creative Valley Development Zone, Guangzhou China TEL: +86 020-8224 5821 FAX: +86 020-8224 5821 Email: Jet.you@qimmig-med.com
Date to prepare: 9/11/2020
## 2. Device Information
Device Name: AccuBrain Common Name: Neuroimage Analysis Software Model: AccuBrain_Intl Software version: V1.0.0.200703 Classification Name: System, Image Processing, Radiological Regulation Number: 21 CFR 892.2050 Regulation Description: Picture archiving and communications system Product Code: LLZ Classification Panel: Radiology Regulation Class: -
#### 3. Predicate Device Information
| Item | Primary Predicate(A) | Predicate or Reference Device (B) |
|-------------------------|--------------------------------------|--------------------------------------------------|
| 510(k) submitter/holder | CorTechs Labs, Inc | Brainreader Aps |
| 510(K) Number | K170981 | K140828 |
| Device name | NeuroQuant | NeuroReader Medical<br>Image Processing Software |
| Common name | Medical Image<br>Processing Software | Neuroreader |
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| Regulation Description | Picture archiving and<br>communications system | Picture archiving and<br>communication system |
|------------------------|------------------------------------------------|-----------------------------------------------|
| Review panel | Radiology | Radiology |
| Product code | LLZ | LLZ, LNH |
| Regulation Class | Class II | Class II |
| Regulation Number | 21 CFR 892.2050 | 21 CFR 892.2050 |
# 4. Device Description
AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report. The resulting output is a morphometric report in PDF format. The software is suitable for use in both clinical trial research and routine patient care as a support tool for clinicians in assessment of structural MRIs.
AccuBrain provides morphometric measurements based on 3D T1 MRI series. The output of the software includes volumes of hippocampus.
The AccuBrain processing architecture includes an automated internal pipeline that performs artifact correction, segmentation, hippocampus quantification, volume calculation and report generation.
Additionally, automated safety measures include automated quality control functions, such as DICOM check, age check and image resolution check and image quality check.
#### 5. Principle of operation
AccuBrain automatically segmented the subject's hippocampi using the uploaded T1W MRIs in a multi-atlas-based segmentation manner. The quantification and visualization of hippocampal segmentation results were output in the form of a morphometric analysis report.
The hippocampal segmentation procedure is described as follows. 1) Pre-processing to increase the image quality, including noise reduction, bias field correction, and intensity normalization to normalize intensity level of MRIs from different scanners. For noise reduction method, we used non-local mean filtering method [1]. Bias correction method used in AccuBrain is N4 bias correction [2]. Intensity Normalization method used in AccuBrain is histogram matching [3]. 2) Atlas selection. The atlas pool, consisting of 300 brain MRIs together with their segmentation labels, were previously obtained from different individuals using different scanners and have highly variable appearances. Each atlas contains both brain MRI and the prior encoded radiologist-specified anatomy information for hippocampus. The detailed of demographic information about the atlas data was described in Table 1. During processing, AccuBrain selects a number of brain images from the atlas pool based on similarity with the subject images. The similarity measures used in this step is Normalized Cross correlation (NCC). AccuBrain will select 10 images from the atlas pool with highest NCC scores. 3) Image segmentation. The non-rigid image registration is performed to match the selected image with the subject image. The resulting transformation field will be applied to transform the predefined atlas label to the subject image. As 10 template images are selected, 10 segmentation results are obtained and will be merged using STAPLE label fusion method l41 to fuse the final segmentation labels of the subject image.
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| Demographic Categories | Frequency (subject<br>number) | Percentage(%) |
|-------------------------|-------------------------------|---------------|
| Gender | | |
| Female | 151 | 50.3 |
| Male | 149 | 49.7 |
| Disease Status | | |
| AD | 123 | 41 |
| NC | 115 | 38.3 |
| MCI | 62 | 20.7 |
| Age | | |
| 51-60 | 25 | 8.3 |
| 61-70 | 96 | 32 |
| 71-80 | 118 | 39.3 |
| 81-90 | 61 | 20.3 |
| Magnetic Field Strength | | |
| 1.5T | 68 | 22.7 |
| 3T | 232 | 77.3 |
| Manufacturer | | |
| GE | 131 | 43.7 |
| Philips | 44 | 14.7 |
| Siemens | 125 | 41.7 |
| In-plane resolution | | |
| 1x1 | 214 | 71.3 |
| 0.9375x0.9375 | 84 | 28 |
| 0.8594x0.8594 | 2 | 0.7 |
| FOV (mm²) | | |
| 220 | 2 | 0.7 |
| 230 | 135 | 45 |
| 240 | 84 | 28 |
| 256 | 79 | 26.3 |
| Slice Thickness(mm) | | |
| 1 | 216 | 72 |
| 1.2 | 84 | 28 |
# Table 1. Demographic information of the atlas data
#### 6. Indications for Use
AccuBrain is a fully automated post-processing software that provides automatic labeling, visualization and volumetric quantification of hippocampus from a set of T1W MRIs and returns an analysis report.
# 7. Comparison of Predicate Devices
Summary Comparison Table for the subject device and predicate devices (K170981 and K140828):
| Comparison<br>Elements | Subject Device | Predicate<br>Device (A) | Predicate or<br>Reference<br>Device (B) | Discussion of<br>difference |
|------------------------|----------------|-------------------------|-----------------------------------------|-----------------------------|
| Device Name | AccuBrain | NeuroQuant | NeuroReader | / |
| 510(k) No | / | K170981 | K140828 | / |
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| Regulation No | 21 CFR<br>892.2050 | 21 CFR<br>892.2050 | 21 CFR<br>892.2050 | Same | | | | | according to the ANSI<br>AAMI IEC<br>62304:2006/A1:2016<br>(Software<br>Documentation,<br>section 004) and the<br>Accuracy and<br>Reproducibility of the<br>Subject Device was<br>verified (section 006).<br>The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. |
|------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|------------------------------------------------------------------------------|------------------------------------------------------------------------------|------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Regulation<br>Description | "Picture<br>archiving and<br>communications<br>system" | "Picture<br>archiving and<br>communications<br>system" | "Picture<br>archiving and<br>communications<br>system" | Same | Processing<br>architecture | Automated<br>internal pipeline<br>that performs: | Automated<br>internal pipeline<br>that performs: | Information not<br>publicly<br>available | SE-within the<br>predicate |
| Classification<br>name | System, Image<br>Processing,<br>Radiological | System, Image<br>Processing,<br>Radiological | System, Image<br>Processing,<br>Radiological | Same | | -artifact<br>correction | -artifact<br>correction | | Both are performed: |
| Classification | Class II | Class II | Class II | Same | | -segmentation | -segmentation | | -artifact correction |
| Product code | LLZ | LLZ | LLZ | Same | | -hippocampus<br>quantification | -lesion<br>quantification | | -segmentation |
| Indications for<br>use | AccuBrain is a<br>fully automated<br>post-processing<br>software that<br>provides<br>automatic<br>labeling,<br>visualization<br>and volumetric<br>quantification of<br>hippocampus<br>from a set of<br>T1W MRIs and<br>returns an<br>analysis report. | Automatic<br>labeling,<br>visualization and<br>volumetric<br>quantification of<br>segmentable<br>brain structures<br>and lesions from<br>a set of MR<br>images.<br>Volumetric data<br>may be<br>compared to<br>reference<br>percentile data | Automatic<br>labeling,<br>visualization<br>and volumetric<br>quantification of<br>segmentable<br>brain structures<br>from a set of<br>MR images.<br>This software is<br>intended to<br>automate the<br>current manual<br>process of<br>identifying,<br>labeling and<br>quantifying the<br>volume of<br>segmentable<br>brain structures<br>identified on MR<br>images. | SE-within the<br>predicate<br>Both are indicated to<br>automatic labeling,<br>visualization and<br>volumetric<br>quantification of<br>hippocampus from a<br>set of T1W MRIs Is<br>and returns an<br>analysis report. | | -volume<br>calculation | -volume<br>calculation | | -hippocampus<br>quantification |
| Design and<br>incorporated<br>technology | • Automated<br>measurement of<br>hippocampus<br>volumes<br>• Automatic<br>atlas-based<br>segmentation<br>and<br>quantification of<br>hippocampus<br>using an atlas<br>pool consisting<br>of difference | • Automated<br>measurement of<br>brain tissue<br>volumes and<br>structures and<br>lesions<br>• Automatic<br>segmentation<br>and<br>quantification of<br>brain structures<br>using a dynamic<br>probabilistic | Information not<br>publicly<br>available | SE<br>Both are indicated to<br>automate<br>measurement of<br>hippocampus<br>volumes through<br>automatic atlas-based<br>segmentation and<br>quantification of<br>hippocampus base on<br>an atlas pool<br>consisting of<br>difference template | | -report<br>generation | -report<br>generation | | -volume calculation |
| template<br>images with<br>highly variable<br>appearance<br>together with<br>their prior<br>encoded<br>radiologist-<br>specified<br>anatomy<br>information | images with highly<br>variable appearance<br>together with their<br>prior encoded<br>radiologist-specified<br>anatomy information | neuroanatomical<br>atlas, with age<br>and gender<br>specificity,<br>based on the<br>MR image<br>intensity | Information not<br>publicly available | images with highly<br>variable appearance<br>together with their<br>prior encoded<br>radiologist-specified<br>anatomy information.<br>The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. | | | | | -report generation |
| Physical<br>characteristics | • Web-based<br>application<br>• Operates on<br>off-the -shelf<br>hardware<br>(multiple<br>vendors) | • Software<br>package<br>• Operates on<br>off-the-shelf<br>hardware<br>(multiple<br>vendors) | Information not<br>publicly<br>available | SE<br>The Cybersecurity of<br>Subject Device was<br>verified<br>(Cybersecurity<br>Information<br>Document, section<br>005).<br><br>And the Subject<br>Device was verified<br>according to the<br>ANSI AAMI IEC<br>62304:2006/A1:2016<br>(section 004) and<br>the Accuracy and<br>Reproducibility of<br>the Subject Device<br>was verified (section<br>006).<br><br>The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. | | | | | The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. |
| Operating<br>system | Supports<br>Windows | Supports Linux,<br>Mac OS X and<br>Windows. | Information not<br>publicly<br>available | SE--within the<br>predicate.<br>The Operating<br>system of Subject<br>Device is fewer than<br>Predicate Device (A),<br>the risk of the Subject<br>Device is fewer than<br>Predicate Device (A).<br>And the Subject<br>Device was verified | Data source | • MRI scanner:<br>3D T1 MRI<br>scans acquired<br>with specified<br>protocols | • MRI scanner:<br>3D T1 MRI<br>scans acquired<br>with specified<br>protocols | Information not<br>publicly<br>available | Same |
| | • AccuBrain<br>supports<br>DICOM format<br>as input | • NeuroQuant<br>supports DICOM<br>format as input | | | | | | | |
| Output | -Provides<br>volumetric | • Provides<br>volumetric<br>measurements | Information not<br>publicly<br>available | SE-within the<br>predicate | | | | | |
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| | measurements<br>of hippocampus<br>-Includes<br>segmented<br>color overlays<br>and an analysis<br>report (just<br>volumetric of<br>the<br>hippocampus) | of brain<br>structures and<br>lesions<br>• Includes<br>segmented color<br>overlays and<br>morphometric<br>reports<br>• Automatically<br>compares<br>results to<br>reference<br>percentile data<br>and to prior<br>scans when<br>available<br>• Supports<br>DICOM format<br>as output of<br>results that can<br>be displayed on<br>DICOM<br>workstations<br>and Picture<br>Archive and<br>Communications<br>Systems | | Both provide<br>volumetric<br>measurements of<br>hippocampus<br>-Includes segmented<br>color overlays and an<br>analysis report<br>including the<br>volumetric of the<br>hippocampus.<br><br>The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. |
|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Accuracy | The mean DICE<br>of AccuBrain<br>results and<br>manual<br>segmentation<br>results is 0.89,<br>0.89 and 0.89<br>for right, left and<br>total<br>hippocampus,<br>respectively. | For major<br>subcortical brain<br>structures Dice's<br>coefficients are<br>in the range of<br>80%-90%. | NeuroReader<br>can segment<br>the<br>hippocampus<br>with a Dice<br>similarity index<br>of 0.87 for both<br>the right and left<br>hippocampus. | SE<br>The accuracy and<br>reproducibility of<br>hippocampus<br>segmentation of<br>AccuBrain with T1W<br>MRI images are<br>comparable with<br>Predicate Device<br>(A) and Predicate<br>Device (B). The<br>accuracy and<br>reproducibility of<br>Subject Device was<br>verified (Accuracy<br>and Reproducibility<br>Test Report, section<br>006). |
| | | | | The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. |
| Safety | • Automated<br>quality control<br>functions<br>- DICOM check<br>- Age check<br>- Image<br>resolution check<br>- Image quality<br>check<br>Diagnostic<br>decisions<br>should be made<br>by trained<br>clinicians. | • Automated<br>quality control<br>functions<br>- Tissue contrast<br>check<br>- Scan protocol<br>verification<br>- Atlas alignment<br>check<br>• Results must<br>be reviewed by<br>a trained<br>physician | Information not<br>publicly<br>available | SE<br>Both are conducted<br>the - DICOM check,<br>Age check, Image<br>resolution check,<br>Image quality check.<br>The Cybersecurity of<br>Subject Device was<br>verified<br>(Cybersecurity<br>Information<br>Document, section<br>005).<br>And the Subject<br>Device was verified<br>according to the IEC<br>62304(Software<br>Documentation,<br>section 004) and the<br>Accuracy and<br>Reproducibility of<br>the Subject Device<br>was verified<br>(Accuracy and<br>Reproducibility test<br>report, section 006).<br>The difference does<br>not affect the<br>determination of<br>substantial<br>equivalence. |
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Subject device and predicate devices are softwares for automatically identifying and quantifying volumes of brain structures. Subject and predicate devices take 3D MR images of the brain as input and generate electronic report with similar quantitative information.
AccurBrain and NeuroQuant achieve the intended use based on similar principle and processing architecture, since the quantification systems implement brain segmentation and quantification using atlas-based segmentation scheme. Both hippocampi are segmented and the volumes are calculated.
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AccurBrain and NeuroQuant are DICOM compatible and operate on off-the-shelf hardware. Meanwhile, both devices are used by medical professional, such as radiologists, neurologists and neuroradiologists, as well as by clinical researchers, as a support tool in assessment of structural MRIs.
The output volumes which both devices provide include volumes of left hippocampus, right hippocampus and whole hippocampi.
# 8. Performance Testing
To demonstrate the performance of AccuBrain (model: AccuBrain_Intl), the measured volumes and volume differences of hippocampus are validated for accuracy and reproducibility. The subjects upon whom the device was tested include healthy subjects, Alzheimer's disease patients and Mild Cognitive Impairment patients. AccuBrain segmentation accuracy with 3D T1 MRI scans was evaluated using Dice coefficient metric. With 135 data provided by the EADC-ADNI HarP, the mean Dice coefficient by comparing AccuBrain results and manual segmentation results was 0.89 (std: 0.03), 0.89 (std: 0.03) and 0.89 (std: 0.03) for right, left and total hippocampal volumes, respectively. Segmentation reproducibility of repeated 3D T1 MRI scans for the same subjects was evaluated using Coefficient of Variation (CV). The mean intrascanner CV values were 3.20% and 1.23%, the mean percentage absolute volume differences DIFF values were 4.52% and 1.74% for left and right hippocampus, respectively. Compared with the performances of the predicate devices, the results presented above shows that the subject device is safe and effective and performs as well as the predicate devices. The AccuBrain (Model: AccuBrain_Intl) was designed, verified, and validated according to the company's Design Control process and has been subjected to extensive safety and performance testing as shown in the test results provided in this submission. Verification and Validation testing data demonstrate that the device meets all of its specifications.
The Accuracy and Reproducibility of AccuBrain (Model: AccuBrain Intl) was verified please see section 006 for the Accuracy and Reproducibility Test Report.
The software of AccuBrain (Model: AccuBrain Intl) was verified according to the ANSI AAMI IEC 62304:2006/A1:2016 Medical device software - Software life cycle processes [Including Amendment 1 (2016)]. Please see section 004 for the Software Documentation.
The Cybersecurity of AccuBrain (Model: AccuBrain Intl) was verified. Please see section 005 for the Cybersecurity Information Document.
During the verification and validation activity the following quidance documents were used:
General Principles of Software Validation: Guidance for Industry and FDA Staff Postmarket Management of Cybersecurity in Medical Devices: Guidance for Industry and Food and Drug Administration Staff
Content of Premarket Submissions for Management of Cybersecurity in Medical Devices: Draft Guidance for Industry and Food and Drug Administration Staff Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices: Guidance for Industry and FDA Staff
#### 9. Conclusions
The performance testing presented above shows that the device is as safe, as effective and performs as well as the predicate devices(A) and predicate devices(B),
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and as well as gold standard-computer-aided expert manual segmentation. By virtue of the physical characteristics and intended user, AccuBrain(AccuBrain_Intl) is substantially equivalent to its predicate devices (A) (K170981) and predicate devices (B)(K140828).
# 10. Bibliography
[1] Coll, Bartomeu & Morel, Jean-Michel. (2005). A non-local algorithm for image denoising. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2. 60- 65 vol. 2.
[2] Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging. 2010;29(6):1310-1320.
[3] Laszlo G. Nyul, Jayaram K. Udupa, and Xuan Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Transactions on Medical Imaging, 19(2):143-150, 2000.
[4] Warfield SK, et al. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. Medical Imaging, IEEE Transactions on. 2004;23:903-921.
