Neurological disorders present a significant challenge in global health. With the increasing availability of imaging datasets and the development of precise machine learning models, early and accurate diagnosis of neurological conditions is a promising and active area of research. However, several characteristic factors in neurology domains, such as heterogeneous imaging, inaccurate labels, or limited data, act as bottlenecks in using deep learning on clinical neuroimaging.
Given these circumstances, this dissertation attempts to provide a guideline, proposing several methods and showcasing successful implementations in broad neurological conditions, including epilepsy and neurodegeneration. Methodologically, a particular focus is on comparing a two-dimensional approach as opposed to three-dimensional neural networks. In most clinical domains of neurological disorders, data are scarce and signals are weak, discouraging the use of 3D representation of raw scan data. This dissertation first demonstrates competitive performances with 2D models in tuber segmentation and AD comorbidity detection.
Second, the potentials of ensemble learning are explored, further justifying the use of 2D models in the identification of neurodegeneration. Lastly, CleanNeuro is introduced in the context of 2D classification, a novel algorithm for denoising the datasets prior to training. CleanNeuro, on top of 2D classification and ensemble learning, demonstrates the feasibility of accurately classifying patients with comorbid AD and cerebral amyloid angiopathy from AD controls. Methods presented in this dissertation may serve as exemplars in the study of neurological disorders using deep learning and clinical neuroimaging.
Clinically, this dissertation contributes to improving automated diagnosis and identification of regional vulnerabilities of several neurological disorders on clinical neuroimaging using deep learning. First, the classification of patients with Alzheimer’s disease from cognitively normal group demonstrates the potentials of using positron emission tomography with tau tracers as a competitive biomarker for precision medicine. Second, the segmentation of tubers in patients with tuberous sclerosis complex proves a successful 2D modeling approach in quantifying neurological burden of a rare yet deadly disease. Third, the detection of comorbid pathologies from patients with Alzheimer’s disease is analyzed and discussed in depth. Based on prior findings that comorbidities of Alzheimer’s disease affect the brain structure in a distinctive pattern, this dissertation proves for the first time the effectiveness of using deep learning on the accurate identification of comorbid pathology in vivo. Leveraging postmortem neuropathology as ground truth labels on top of the proposed methods records competitive performances in comorbidity prediction. Notably, this dissertation discovers that structural magnetic resonance imaging is a reliable biomarker in differentiating the comorbid cereberal amyloid angiopathy from Alzheimer’s disease patients.
The dissertation discusses experimental findings on a wide range of neurological disorders, including tuberous sclerosis complex, dementia, and epilepsy. These results contribute to better decision-making on building neural network models for understanding and managing neurological diseases. With the thorough exploration, the dissertation may provide valuable insights that can push forward research in clinical neurology.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/efqf-wx23 |
| Date | January 2024 |
| Creators | Park, David Keetae |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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