Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Anna Jenul, Henning Langen Stokmo, Stefan Schrunner, Geir Olav Hjortland, Mona-Elisabeth Revheim, Oliver Tomic
Summary: This study evaluates the application of modern ensemble feature selection techniques for predicting overall survival in patients with high-grade gastroenteropancreatic neuroendocrine neoplasms. The results demonstrate that these feature selectors provide accurate predictions and integrating expert knowledge can improve the stability of the feature set. WHO Performance Status, Albumin, Platelets, Ki-67, Tumor Morphology, Total MTV, Total TLG, and SUVmax are identified as the most stable and predictive features in this study.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Information Systems
Jahanpour Alipour, Roxana Sharifian, Javid Dehghan Haghighi, Mehrnaz Hashemzehi, Afsaneh Karimi
Summary: This study investigated patients' perceptions of the e-prescribing system. The majority of patients were aware of e-prescribing, and preferred it over traditional prescriptions. Patients reported overall positive satisfaction and relatively positive perceptions and experiences with the e-prescribing system.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Bo Sheng, Xiaohui Chen, Jian Cheng, Yanxin Zhang, Shane (Sheng Quan) Xie, Jing Tao, Chaoqun Duan
Summary: In this study, a novel scoring approach for motor function assessment was proposed, which utilized a motion tracking system and an intelligent scoring system to objectively and accurately evaluate upper-limb motor function in stroke survivors, aiding physiotherapists in making individualized treatment decisions.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Information Systems
Matthew Ho, Todd J. Levy, Ioannis Koulas, Kyriaki Founta, Kevin Coppa, Jamie S. Hirsch, Karina W. Davidson, Alex C. Spyropoulos, Theodoros P. Zanos
Summary: This study identified clinical phenotypes of hospitalized COVID-19 patients and investigated their longitudinal dynamics throughout the pandemic. Four distinct clinical phenotypes were associated with different mortality rates and showed variability across different viral variants. Factors such as sex, race/ethnicity, and treatment modalities revealed significant differences between the observed phenotypes. This methodology has the potential to guide evidence-based treatment strategies in a dynamic fashion.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Information Systems
Savina Mannarino, Valeria Calcaterra, Giulia Fini, Andrea Foppiani, Antonio Sanzo, Martina Pisarra, Gabriele Infante, Marta Marsilio, Irene Raso, Sara Santacesaria, Gianvincenzo Zuccotti
Summary: This study introduces an innovative pediatric telecardiology system, seamlessly integrated with a hospital telemedicine platform, which enhances patient management in the community. The results demonstrate the system's value as a diagnostic tool to facilitate the execution, transmission, and reporting of ECG data between primary care pediatrician clinics and the hospital.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Nikolaos M. Dimitriou, Ece Demirag, Katerina Strati, Georgios D. Mitsis
Summary: This study investigates the performance of mathematical models of tumour growth through integrating multiscale measurements. The results demonstrate that incorporating multiscale measurements can improve model performance, particularly when high-dose treatment is involved.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chaofan Song, Tongqiang Liu, Haifeng Shi, Zhuqing Jiao
Summary: This study proposed a multi-modal feature selection framework for classifying end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) patients and identifying discriminative brain regions. Brain networks were constructed using structural and functional imaging data, and node efficiency and clustering coefficient were extracted as features. The results showed that this framework outperformed existing methods in terms of diagnostic accuracy.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Caiwen Xu, Jie Feng, Yong Yue, Wanjun Cheng, Dianning He, Shouliang Qi, Guojun Zhang
Summary: A hybrid few-shot multiple-instance learning model was developed to successfully predict lymphoma aggressiveness in PET/CT images, showcasing the potential of artificial intelligence in medical applications with limited samples.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Aziguli Wulamu, Jichang Luo, Saian Chen, Han Zheng, Tao Wang, Renjie Yang, Liqun Jiao, Taohong Zhang
Summary: In this paper, a quantitative method for predicting morphological indices of carotid stenosis is proposed. The method adopts a two-stage pipeline, first using an object detection model to locate suitable regions for predicting the indices, and then using a regression model to make the predictions. Experimental results show that the proposed method improves the detection performance of normal vascular segments and achieves high accuracy in predicting the morphological indices.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Review
Computer Science, Information Systems
Louis Agha-Mir-Salim, Lucas McCullum, Enrico Dahnert, Yanick-Daniel Scheel, Ainsley Wilson, Marianne Carpio, Carmen Chan, Claudia Lo, Lindsay Maher, Corinna Dressler, Felix Balzer, Leo Anthony Celi, Akira-Sebastian Poncette, Michele M. Pelter
Summary: Alarm fatigue is a significant issue in the intensive care unit, and collaboration between nurses and engineers is crucial for finding solutions. However, the current research lacks sufficient involvement of nurses, leading to a lack of successful real-world solutions.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Changxu Dong, Dengdi Sun
Summary: This article introduces a novel framework called the dynamic graph attention information bottleneck (DGAIB) to improve information exchange in brain network computations. By optimizing the brain graph structure and enhancing the feature aggregation of brain states, this framework achieves good performance in brain network classification tasks.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Haifeng Wang, Lei Fan, Jenny S. Choy, Ghassan S. Kassab, Lik Chuan Lee
Summary: Capillary transit time is an important factor in gas exchange between the heart and other organs. Researchers have developed a computational framework that can predict the hemodynamics of the entire coronary network and calculate capillary transit time using virtual tracers. The model predictions align with experimental measurements and indicate that coronary artery stenosis leads to longer capillary transit time.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
