Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation.

The SARS-CoV2 virus, which causes COVID-19 (coronavirus disease) has become a pandemic and has expanded all over the world. Because of increasing number of cases day by day, it takes time to interpret the laboratory findings thus the limitations in terms of both treatment and findings are emerged. Due to such limitations, the need for clinical decisions making system with predictive algorithms has arisen. Predictive algorithms could potentially ease the strain on healthcare systems by identifying the diseases. In this study, we perform clinical predictive models that estimate, using deep learning and laboratory data, which patients are likely to receive a COVID-19 disease. To evaluate the predictive performance of our models, precision, F1-score, recall, AUC, and accuracy scores calculated. Models were tested with 18 laboratory findings from 600 patients and validated with 10 fold cross-validation and train-test split approaches. The experimental results indicate that our predictive models identify patients that have COVID-19 disease at an accuracy of 86.66%, F1-score of 91.89%, precision of 86.75%, recall of 99.42%, and AUC of 62.50%. It is observed that predictive models trained on laboratory findings could be used to predict COVID-19 infection, and can be helpful for medical experts to prioritize the resources correctly. Our models (available at (https://github.com/burakalakuss/COVID-19-Clinical)) can be employed to assists medical experts in validating their initial laboratory findings, and can also be used for clinical prediction studies.

Comparison of deep learning approaches to predict COVID-19 infection

Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Coronavirus is an epidemic that spreads very quickly. For this reason, it has very devastating effects in many areas worldwide. It is vital to detect COVID-19 diseases as quickly as possible to restrain the spread of the disease. The similarity of COVID-19 disease with other lung infections makes the diagnosis difficult. In addition, the high spreading rate of COVID-19 increased the need for a fast system for the diagnosis of cases. For this purpose, interest in various computer-aided (such as CNN, DNN, etc.) deep learning models has been increased. In these models, mostly radiology images are applied to determine the positive cases. Recent studies show that, radiological images contain important information in the detection of coronavirus. In this study, a novel artificial neural network, Convolutional CapsNet for the detection of COVID-19 disease is proposed by using chest X-ray images with capsule networks. The proposed approach is designed to provide fast and accurate diagnostics for COVID-19 diseases with binary classification (COVID-19, and No-Findings), and multi-class classification (COVID-19, and No-Findings, and Pneumonia). The proposed method achieved an accuracy of 97.24%, and 84.22% for binary class, and multi-class, respectively. It is thought that the proposed method may help physicians to diagnose COVID-19 disease and increase the diagnostic performance. In addition, we believe that the proposed method may be an alternative method to diagnose COVID-19 by providing fast screening.

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

pdf/automated-classification-of-alzheimer-s-disease-and-mild-4qica7jpqi.pdf

Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

Objective: To build and validate a deep learning (DL) algorithm that predicts the individual diagnosis of Alzheimer’s disease (AD) and the development of AD in mild cognitive impairment (MCI) patients based on a single cross-sectional brain structural MRI scan. Background: The utility of structural MRI in the clinical assessment of patients with suspected AD will be increased by development of robust algorithms for automated assessment. DL algorithms differ from conventional machine learning methods by the fact that they require little or no image pre-processing and can automatically infer an optimal representation of the data from the raw images without requiring prior feature selection, resulting in a more objective and less bias-prone process. Design/Methods: 3D T1-weighted images from ADNI (352 healthy controls [HC], 294 AD, 253 MCI converters, 510 MCI stable) and subjects recruited at our Institute (non-ADNI dataset: 55 HC, 124 AD, 27 MCI converters, 23 MCI stable) were used. Deep neural networks (DNNs), which are mathematical representations of the human neural architecture with multiple hidden layers of artificial neurons, were applied. The whole dataset was randomly divided into a training/validation set (90%) and a testing set (10%). DNN performance was improved by adding to the original dataset synthetic images created using data augmentation algorithms, as well as transfer learning to subsequent comparisons. Results: DNNs with different architectures and parameters were optimized. In the reference ADNI dataset and in a second dataset including also non-ADNI images, DNNs discriminated AD and HC with an accuracy up to 98%, with no difference between ADNI and non-ADNI images. In the whole dataset, DNN was also able to discriminate: HC vs (AD + all MCI) with an accuracy up to 86%, and MCI converters vs MCI stable with an accuracy up to 75%. Conclusions: DNNs provide a powerful tool for the automatic individual patient diagnosis along the AD continuum. Study Supported by: The study was supported by the Italian Ministry of Health (GR-2011-02351217). Data collection and sharing was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). Disclosure: Dr. Basaia has nothing to disclose. Dr. Agosta has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with EXCEMED— Excellence in Medical Education. Dr. Wagner has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Effeventi s.r.l. Dr. Magnani has nothing to disclose. Dr. Filippi has received personal compensation for consulting, serving on a scientific advisory board, speaking, or other activities with Biogen Idec, Merk-Serono, Novartis, Teva Pharmaceutical Industries. Dr. Filippi has received personal compensation in an editorial capacity for Journal of Neurology. Dr. Filippi has received research support from Biogen Idec, Novartis, Teva Pharmaceutical Industries.

Automatic classification of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) who will convert to AD using deep neural networks (P3.179)

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Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks

Automatic classification of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) who will convert to AD using deep neural networks (P3.179)