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Evaluation Algorithm for the Effectiveness of Stroke Rehabilitation Treatment Using Cross-Modal Deep Learning
Comput Math Methods Med. 2022 Apr 27;2022:5435207. doi: 10.1155/2022/5435207. eCollection 2022.ABSTRACTIt is important to study the evaluation algorithm for the stroke rehabilitation treatment effect to make accurate evaluation and optimize the stroke disease treatment plan according to the evaluation results. To address the problems of poor restoration effect of positron emission tomography (PET) image and recognition restoration effect of evaluation data and so on. In the paper, we propose a stroke rehabilitation treatment effect evaluation algorithm based on cross-modal deep learning. Magnetic resonance images (MRI) and...
Source: Computational and Mathematical Methods in Medicine - May 9, 2022 Category: Statistics Authors: Lei Wang Rongxing Zhang Qinming Yu Source Type: research

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images
In this study, we propose an automated transfer deep learning method that combines ResNet-50 and dense layer for accurate prediction of intracranial hemorrhage on NCCT brain images. A total of 1164 NCCT brain images were collected from 62 patients with hemorrhagic stroke from Kalinga Institute of Medical Science, Bhubaneswar and used for evaluating the model. The proposed model takes individual CT images as input and classifies them as hemorrhagic or normal. This deep transfer learning approach reached 99.6% accuracy, 99.7% specificity, and 99.4% sensitivity which are better results than that of ResNet-50 only. It is evide...
Source: Computational and Mathematical Methods in Medicine - April 26, 2022 Category: Statistics Authors: B Nageswara Rao Sudhansu Mohanty Kamal Sen U Rajendra Acharya Kang Hao Cheong Sukanta Sabut Source Type: research

Object-Specific Four-Path Network for Stroke Risk Stratification of Carotid Arteries in Ultrasound Images
In this study, we propose an object-specific four-path network (OSFP-Net) for stroke risk assessment by integrating ultrasound carotid plaques in both transverse and longitudinal sections of the bilateral carotid arteries. Each path of the OSFP-Net comprises of a feature extraction subnetwork (FE) and a feature downsampling subnetwork (FD). The FEs in the four paths use the same network structure to automatically extract features from ultrasound images of carotid plaques. The FDs use different object-specific pooling strategies for feature downsampling based on the observation that the sizes and shapes in the feature maps ...
Source: Computational and Mathematical Methods in Medicine - May 5, 2022 Category: Statistics Authors: Wei Ma Yujiao Xia Xiaoyan Wu Zheng Yue Xinyao Cheng Aaron Fenster Mingyue Ding Source Type: research

Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects.
Authors: Zhao B, Liu Z, Liu G, Cao C, Jin S, Wu H, Ding S Abstract Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely diagnose AIS, it is of paramount importance to quantitatively evaluate the AIS lesions. By adopting a convolutional neural network (CNN), many automatic methods for ischemic stroke lesion segmentation on magnetic resonance imaging (MRI) have been proposed. However, most CNN-based methods should be trained on a large amount of fully labeled subjects, and the label annotation is a labor-intensive and ti...
Source: Computational and Mathematical Methods in Medicine - February 12, 2021 Category: Statistics Tags: Comput Math Methods Med Source Type: research

Calibrating random forests for probability estimation
Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so‐called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest ...
Source: Statistics in Medicine - April 14, 2016 Category: Statistics Authors: Theresa Dankowski, Andreas Ziegler Tags: Research Article Source Type: research

Machine learning methods for leveraging baseline covariate information to improve the efficiency of clinical trials
Clinical trials are widely considered the gold standard for treatment evaluation, and they can be highly expensive in terms of time and money. The efficiency of clinical trials can be improved by incorporating information from baseline covariates that are related to clinical outcomes. This can be done by modifying an unadjusted treatment effect estimator with an augmentation term that involves a function of covariates. The optimal augmentation is well characterized in theory but must be estimated in practice. In this article, we investigate the use of machine learning methods to estimate the optimal augmentation. We consid...
Source: Statistics in Medicine - November 25, 2018 Category: Statistics Authors: Zhiwei Zhang, Shujie Ma Tags: RESEARCH ARTICLE Source Type: research

Machine Learning and Electrocardiography Signal-Based Minimum Calculation Time Detection for Blood Pressure Detection
CONCLUSION: According to the study results, it is evaluated that systolic and diastolic blood pressure values can be calculated with a high-performance ratio with 16-second ECG signals.PMID:35903432 | PMC:PMC9325348 | DOI:10.1155/2022/5714454
Source: Computational and Mathematical Methods in Medicine - July 29, 2022 Category: Statistics Authors: Majid Nour Derya Kandaz Muhammed Kursad Ucar Kemal Polat Adi Alhudhaif Source Type: research

Efficacy Analysis of Team-Based Nursing Compliance in Young and Middle-Aged Diabetes Mellitus Patients Based on Random Forest Algorithm and Logistic Regression
CONCLUSION: The team-based nursing model has a good effect on the blood glucose control level of middle-aged and young diabetic patients. Age, BMI, and glucose values are risk factors for diabetes. The SF algorithm has a good effect on predicting the risk of diabetes, which is worthy of further clinical application.PMID:35936376 | PMC:PMC9355774 | DOI:10.1155/2022/3882425
Source: Computational and Mathematical Methods in Medicine - August 8, 2022 Category: Statistics Authors: Dongni Qian Hong Gao Source Type: research

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