Article
Computer Science, Interdisciplinary Applications
Vinicius Ribeiro, Karyna Isaieva, Justine Leclere, Jacques Felblinger, Pierre-Andre Vuissoz, Yves Laprie
Summary: This research presents a method for individually segmenting nine non-rigid vocal tract articulators in real-time MRI movies. The software is openly available as an installable package and is designed to develop speech applications and clinical and non-clinical research in fields that require vocal tract geometry, such as speech, singing, and human beatboxing.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Onintze Zaballa, Aritz Perez, Elisa Gomez-Inhiesto, Teresa Acaiturri-Ayesta, Jose A. Lozano
Summary: A novel generative model is proposed in this paper to handle the incompleteness of electronic health records (EHRs) and describe the different joint evolution of coexisting diseases. The model shows good accuracy and effectiveness in experiments on synthetic and real medical data.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Information Systems
Liss Hernandez, Estefania Estevez-Priego, Laura Lopez-Perez, Maria Fernanda Cabrera-Umpierrez, Maria Teresa Arredondo, Giuseppe Fico
Summary: HeNeCOn is a reusable, extendible and standardized ontology that provides a clinically reliable data model for Head and Neck Cancer. It consists of 502 classes and 283 medical terms with detailed relations between them, allowing for information extraction and knowledge management.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Liang Wang, Hongge Han, Jie Wang, Yueting Zhu, Zhanli Liu, Yongtao Sun, Lele Wang, Shuyi Xiang, Huibin Shi, Qian Ding
Summary: This study improved the finite element model of repairing perforated tympanic membranes (TM) and investigated the effect of autologous graft materials and a bionic spider web tympanic scaffold on middle ear sound transmission. The results showed that there was still a degree of high-frequency hearing loss after repairing with autologous graft materials, but the use of a magnesium alloy scaffold effectively prevented high-frequency hearing loss and made the sound transmission closer to normal. These findings provide valuable references for clinical trial protocols and follow-up repair ideas for tympanoplasty.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Santosh Kumar, Vijesh Bhagat, Prakash Sahu, Mithliesh Kumar Chaube, Ajoy Kumar Behera, Mohsen Guizani, Raffaele Gravina, Michele Di Dio, Giancarlo Fortino, Edward Curry, Saeed Hamood Alsamhi
Summary: This study aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients using machine learning techniques. The proposed framework extracts features from CT scan images and lung sound/cough samples, and utilizes unsupervised ML techniques and customized ensemble learning techniques for early classification and severity assessment. The results show high accuracy for early diagnosis of COPD patients based on the proposed framework.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Neslihan Gokmen Inan, Ozan Kocadagli, Duzgun Yildirim, Ismail Mese, Ozge Kovan
Summary: In this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of thyroid nodules. The system achieved excellent segmentation and classification outcomes, outperforming existing approaches in terms of ACC, Jaccard, and DICE losses.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Xuan Tang, Chaojie Wu
Summary: This study investigates the application of a Predictive Surrogate Model (PSM) for the prediction of fluid and solid variables in the abdominal aorta. The results show promising results for the prediction of velocity components and wall-related indices using PSM, despite the inherent complexities of physiological conditions in the aorta.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yuefei Wang, Xi Yu, Yixi Yang, Xiang Zhang, Yutong Zhang, Li Zhang, Ronghui Feng, Jiajing Xue
Summary: This study proposes a novel approach called TP-MNet for medical image semantic segmentation. TP-MNet addresses the accuracy bottleneck by facilitating feature transfer among neighboring branches and conducting secondary feature mining. The experiments demonstrate the superiority of TP-MNet in medical image segmentation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Information Systems
Danya Arif Siddiqi, Fatima Miraj, Humdiya Raza, Owais Ahmed Hussain, Mehr Munir, Vijay Kumar Dharma, Mubarak Taighoon Shah, Ali Habib, Subhash Chandir
Summary: This study developed and evaluated an AI chatbot in local language for providing immunization information in low-resource, low-literacy settings in Pakistan. The results showed that the chatbot was feasible and acceptable, meeting the needs of caregivers and reducing the workload of helpline operators.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Computer Science, Interdisciplinary Applications
Rasmus Ryyppo, Sergei Hayrynen, Henry Joutsijoki, Martti Juhola, Mikko R. J. Seppanen
Summary: Machine learning can successfully predict the occurrence of rare diseases in patients at least 30 days before the initial diagnosis, and a performative custom deep learning model can be built.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Review
Computer Science, Information Systems
Ana Raquel Costa-Brito, Antonio Bovolini, Maria Rua-Alonso, Claudia Vaz, Juan Francisco Ortega-Moran, J. Blas Pagador, Carolina Vila-Cha
Summary: This scoping review investigates the use of home-based technological tools to improve physical function in older adults. The majority of studies suggest high levels of technology usage and positive health outcomes. However, the lack of international consensus on technology usage measures and the exclusion of older adults without technology ownership or experience may limit the findings.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Review
Computer Science, Information Systems
Xinyi Chen, Xiang Liu, Yuke Wu, Zhenglei Wang, Shuo Hong Wang
Summary: The current research on the diagnosis and staging of prostate cancer using machine learning and deep learning is in its infancy, with low accuracy in diagnosis and classification. There is a lack of studies on CT images and ultrasound images.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2024)
Article
Mathematical & Computational Biology
Jia Wang, Jingjing Yin, Lili Tian
Summary: This article examines both parametric and non-parametric approaches for estimating the confidence region of HUMK and J(K) for a single biomarker. The performance of the proposed methods is evaluated through extensive simulation studies and application to a real data set.
STATISTICS IN MEDICINE
(2023)
Article
Mathematical & Computational Biology
Fan Li, Xinyuan Chen, Zizhong Tian, Rui Wang, Patrick J. Heagerty
Summary: This article proposes novel variance formulas for analyzing heterogeneity of treatment effects in stepped wedge designs, applicable to both average treatment effect and subgroup treatment effects. The study also investigates optimal design allocations of clusters to maximize precision for evaluating both average and heterogeneous treatment effects.
STATISTICS IN MEDICINE
(2023)
Article
Mathematical & Computational Biology
Erik W. van Zwet, Lu Tian, Robert Tibshirani
Summary: The main objective of clinical trials is to evaluate the effect of treatments compared to control conditions. The signal-to-noise ratio (SNR), which is the ratio of the true treatment effect to its estimate's standard error (SE), is often found to be low. This indicates that many trials have low statistical power against the true effect. In this study, using the Cochrane Database of Systematic Reviews (CDSR), we quantitatively assess the consequences and find considerable overoptimism of unbiased estimators and under-coverage of associated confidence intervals. We propose a novel shrinkage estimator to address this issue and demonstrate its superior performance compared to the usual unbiased estimator.
STATISTICS IN MEDICINE
(2023)
Article
Mathematical & Computational Biology
Patarawan Sangnawakij, Dankmar Bohning, Heinz Holling, Katrin Jansen
Summary: This article investigates the estimation of effect heterogeneity for the risk-ratio parameter in meta-analysis of rare events studies, and proposes two likelihood-based nonparametric mixture approaches. Simulation results suggest that the contrast-based approach is more appropriate in terms of model selection criteria.
STATISTICS IN MEDICINE
(2023)
