Development of Data-driven Prediction Model using 3D Multimodal Deep Neural Networks for Estimating the Evolution of White Matter Hyperintensities Associated with Small Vessel Disease in Brain MRI
| Project/Area Number |
20K23356
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| Research Category |
Grant-in-Aid for Research Activity Start-up
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| Allocation Type | Multi-year Fund |
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| Review Section |
1002:Human informatics, applied informatics and related fields
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
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| Project Period (FY) |
2020-09-11 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2021: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | White matter lesions / Progression of WMHs / Disease prediction model / Deep learning / Dementia / Alzheimer's disease / WMHs / Human brain MRI / Progression of WMLs / Deep learning model |
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| Outline of Research at the Start |
Detecting early age-related brain diseases and predicting their progression over time are utmost important for early treatment. We are developing a new deep learning method for predicting brain diseases progression which will help doctors designing more effective patient-specific interventions.
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| Outline of Final Research Achievements |
White matter hyperintensities (WMHs) and their evolution over time are the focus of this research. WMHs are neuroradiological features seen in T2-FLAIR brain MRI and associated with stroke and dementia. Clinical studies indicate that the volume of WMHs on a patient may decrease (i.e., regress), stay the same, or increase (i.e., progress) over time.
In this project, we successfully developed a more accurate predictive model for WMHs evolution using deep learning by performing joint prediction of WMHs evolution and stroke lesions segmentation. Furthermore, auxiliary input of stroke lesions probability maps also improved the performance of our model. These findings are important because (1) they confirmed previous clinical studies which elucidated that is as strong correlation between WMHs evolution and stroke lesions and (2) more accurate prediction of WMHs evolution can help physicians to create patient specific treatment for dementia patient.
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| Academic Significance and Societal Importance of the Research Achievements |
In aging society like Japan, it is important for physicians to be able to predict the progression of neurodegenerative diseases such as dementia and Alzheimer's diseases. With our proposed model, physicians can perform patient specific treatment for dementia patient.
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Report
(3 results)
Research Products
(1 results)
