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نتیجه جستجو - Magnetic Resonance Imaging data

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 A deep learning model for early prediction of Alzheimers disease dementia based on hippocampal magnetic resonance imaging data
یک مدل یادگیری عمیق برای پیش بینی اولیه زوال عقل بیماری آلزایمر بر اساس داده های تصویربرداری رزونانس مغناطیسی هیپوکامپ-2019
It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer’s disease (AD) dementia. Methods: A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects’ progression to AD dementia in a time-to-event analysis setting. Results: The deep-learning time-to-event model predicted individual subjects’ progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer’s Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P ,.0002). Improved performance for predicting progression to AD dementia (concordance index 5 0.864) was obtained when the deep learning–based progression risk was combined with baseline clinical measures. Discussion: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
Keywords: Deep learning | Hippocampus | Time-to-event analysis | Alzheimer’s disease
مقاله انگلیسی
2 Computer aided Alzheimers disease diagnosis by an unsupervised deep learning technology
تشخیص بیماری آلزایمر به کمک کامپیوتر توسط یک تکنولوژی یادگیری عمیق-2019
Deep learning technologies have played more and more important roles in Computer Aided Diagnosis (CAD) in medicine. In this paper, we tackled the problem of automatic prediction of Alzheimer’s Disease (AD) based on Magnetic Resonance Imaging (MRI) images, and propose a fully unsupervised deep learn- ing technology for AD diagnosis. We first implement the unsupervised Convolutional Neural Networks (CNNs) for feature extraction, and then utilize the unsupervised predictor to achieve the final diagnosis. In the proposed method, two kinds of data forms, one slice and three orthogonal panels (TOP) of MRI image, are employed as the input data respectively. Experimental results run on all the 1075 subjects in database of the Alzheimer’s Disease Neuroimaging Initiative (ADNI 1 1.5T) show that the proposed method with one slice data yields the promising prediction results for AD vs. MCI (accuracy 95.52%) and MCI vs. NC (accuracy 90.63%), and the proposed methods with TOP data yields the best overall prediction results for AD vs. MCI (accuracy 97.01%) and MCI vs. NC (accuracy 92.6%).
Keywords: Deep learning | Unsupervised learning | Convolutional neural network | Alzheimer’s disease prediction | Magnetic Resonance Imaging data | Computer aided diagnosis
مقاله انگلیسی
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