Article
Biotechnology & Applied Microbiology
Guangming Zhang, Rong Liu, Min Pu, Xiaobo Zhou
Summary: This study utilized biomechanical stresses and machine learning methods to accurately predict the risk of AVB after TAVR, optimizing valve size and shape to improve clinical outcomes. The combination of biomechanical properties and machine learning significantly enhanced the prediction of surgical results.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2021)
Article
Biophysics
G. Rossini, A. Caimi, A. Redaelli, E. Votta
Summary: A Finite Element workflow was developed for multiscale analysis of aortic valve biomechanics, capturing the impact of anatomical features on the biomechanical environment from organ to cell scale. The study found that the dependency of leaflet biomechanics on leaflet-specific anatomy observed at the organ length-scale is reflected in the results obtained at lower length-scales.
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Nikrouz Bahadormanesh, Benjamin Tomka, Mason Kadem, Seyedvahid Khodaei, Zahra Keshavarz-Motamed
Summary: It provides an overview of aortic stenosis (AS) and its impact on patients, as well as the lack of effective tools for evaluating valve dynamics in clinical practice. The researchers developed a non-invasive computational framework using ultrasound technology, which can serve as a diagnostic tool to assess valve dynamics in patients with AS. The framework was validated using clinical data and demonstrated its accuracy and reliability in analyzing and interpreting clinical data.
MEDICAL IMAGE ANALYSIS
(2023)
Article
Engineering, Mechanical
Hossein Salahshoor, Hongsun Guo, Mikhail G. Shapiro, Michael Ortiz
Summary: Ultrasound neuromodulation (UNM) is a promising technique for excitation or inhibition of neural activity, but its off-target sensory effects and their dependence on stimulation frequency are still unclear. Research shows that the brain is largely insulated by the skull, and shear waves are carried away from the skull by the vertebral column, forming a frequency-dependent waveguide mechanism that may contribute to the frequency dependence of UNM effects.
EXTREME MECHANICS LETTERS
(2022)
Article
Cardiac & Cardiovascular Systems
Tianyang Yang, Haini Wen, Ismail El-Hamamsy, Qiming Ni, Yanbin Sun, Dan Zhu
Summary: By measuring the normal dimensions and relationship between aortic root and leaflets in the Chinese population, this 3DCT-based study aimed to establish a reference for aortic valve repair. The study found specific correlations and ratios that could be used to assess leaflet-root mismatch and post-repair outcomes.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2021)
Article
Biotechnology & Applied Microbiology
Roberta Scuoppo, Stefano Cannata, Giovanni Gentile, Caterina Gandolfo, Salvatore Pasta
Summary: This study used finite-element analysis to quantify coronary flow in a patient who underwent TAVR-in-TAVR and found that high implantation depth and device undersize of the second THV can significantly reduce coronary flow. Additionally, a positive correlation was observed between coronary flow and the valve-to-coronary distance.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Engineering, Biomedical
Jordan A. Brown, Jae H. Lee, Margaret Anne Smith, David R. Wells, Aaron Barrett, Charles Puelz, John P. Vavalle, Boyce E. Griffith
Summary: Transcatheter aortic valve replacement (TAVR) is a commonly used technique for aortic valve replacement, and computer modeling and simulation (CM&S) can assist in the design and approval process of TAVR devices. This study presents a computational fluid-structure interaction (FSI) model of TAVR using the immersed finite element-difference (IFED) method.
ANNALS OF BIOMEDICAL ENGINEERING
(2023)
Article
Polymer Science
Yuchen Liu, Ming Fang, Ruifeng Zhao, Hengyan Liu, Min Tian, Sheng Zhong, Shizhu Bai
Summary: This study evaluated the effect of periodontal splints made from different materials on stress distributions in compromised periodontal tissues and cement layers. Using a computer simulation of mastication, it was found that the use of splints effectively distributed loads and reduced stress. Splinting materials with low elastic moduli reduced stress concentration at the connectors, but increased tensile stress in the cement layer.
Article
Cardiac & Cardiovascular Systems
Axel Gomez, Zhongjie Wang, Yue Xuan, Michael D. Hope, David A. Saloner, Julius M. Guccione, Liang Ge, Elaine E. Tseng
Summary: This study compared wall stresses in tricuspid aortic valve-associated ascending thoracic aortic aneurysms of different diameters and found that both circumferential and longitudinal wall stresses increase as diameter increases. However, there is a large overlap of stress ranges between size groups, suggesting that wall stress thresholds based on aneurysm wall strength may be a better predictor of patient-specific risk of dissection in small ascending thoracic aortic aneurysms.
JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY
(2022)
Article
Mechanics
Hassan Mehboob
Summary: This study investigates the biomechanical performance of different implants made of various materials for spinal repair. The results demonstrate that metamaterial CPEEK rods and cages have better performance and can avoid some complications and issues.
COMPOSITE STRUCTURES
(2023)
Article
Biology
Yan Yao, Zhongjun Mo, Gang Wu, Junchao Guo, Jian Li, Lizhen Wang, Yubo Fan
Summary: Personalized plates (P-Plates) show improved clinical outcomes in joint fusion by achieving perfect geometric matching with irregular bones. Finite element method optimization demonstrated lower stress and tensile force in the P-Plate compared to a traditional plate (T-Plate). Implantation of the P-Plate in a patient showed no complications and achieved good clinical scores three years post operation.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Cardiac & Cardiovascular Systems
Siavash Zamirpour, Yue Xuan, Zhongjie Wang, Axel Gomez, Joseph Leach, Dimitrios Mitsouras, David A. Saloner, Julius M. Guccione, Liang Ge, Elaine E. Tseng
Summary: This study evaluated the association between aortic area/height and peak aneurysm wall stresses, valve morphology, and 3-year all-cause mortality. The results showed that the aortic area/height ratio was more predictive of high circumferential stresses in bicuspid valve aneurysms, while peak longitudinal stress independently predicted all-cause mortality.
JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY
(2023)
Article
Mathematical & Computational Biology
Li Cai, Yu Hao, Pengfei Ma, Guangyu Zhu, Xiaoyu Luo, Hao Gao
Summary: This paper studies the mechanics of aortic valve caused by calcification and uses a numerical model to investigate the dynamics and hemodynamic performance of the valve under normal and calcified conditions. The results show that calcification significantly affects the motion and blood flow of the valve, potentially leading to ventricular dysfunction.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Engineering, Biomedical
Ming Zhang, Haofei Liu, Zongxi Cai, Cuiru Sun, Wei Sun
Summary: This study presents a framework combining experiments, constitutive modeling, and computer simulation to quantify the subject-specific three-dimensional residual stress field of the aortic wall. The proposed framework was effective in quantifying the three-dimensional subject-specific residual stress field and yielded accurate results in terms of residual stress estimation for the aortic sample.
JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS
(2022)
Article
Computer Science, Interdisciplinary Applications
Sara Barati, Nasser Fatouraee, Malikeh Nabaei, Lorenza Petrini, Francesco Migliavacca, Giulia Luraghi, Jose Felix Rodriguez Matas
Summary: The design and optimization of transcatheter heart valve replacement devices are of great importance for improving performance quality and reducing the risk of malfunction. In this study, optimal models with larger distal diameters were found to perform better in the selected objective functions.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Hong Liu, Dong Wei, Donghuan Lu, Xiaoying Tang, Liansheng Wang, Yefeng Zheng
Summary: This study proposes a framework based on hybrid 2D-3D convolutional neural networks for obtaining continuous 3D retinal layer surfaces from OCT volumes. The framework works well with both full and sparse annotations and utilizes alignment displacement vectors and layer segmentation to align the B-scans and segment the layers. Experimental results show that the framework outperforms state-of-the-art 2D deep learning methods in terms of layer segmentation accuracy and cross-B-scan 3D continuity.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Simon Oxenford, Ana Sofia Rios, Barbara Hollunder, Clemens Neudorfer, Alexandre Boutet, Gavin J. B. Elias, Jurgen Germann, Aaron Loh, Wissam Deeb, Bryan Salvato, Leonardo Almeida, Kelly D. Foote, Robert Amaral, Paul B. Rosenberg, David F. Tang-Wai, David A. Wolk, Anna D. Burke, Marwan N. Sabbagh, Stephen Salloway, M. Mallar Chakravarty, Gwenn S. Smith, Constantine G. Lyketsos, Michael S. Okun, William S., Zoltan Mari, Francisco A. Ponce, Andres Lozano, Wolf-Julian Neumann, Bassam Al-Fatly, Andreas Horn
Summary: Spatial normalization is a method to map subject brain images to an average template brain, allowing comparison of brain imaging results. We introduce a novel tool called WarpDrive, which enables manual refinements of image alignment after automated registration. The tool improves accuracy of data representation and aids in understanding patient outcomes.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Ece Ozkan, Christian Knorr, Julia E. Vogt
Summary: This study presents interpretable machine learning models for predicting the diagnosis, management, and severity of suspected appendicitis using ultrasound images. The proposed models utilize concept bottleneck models (CBM) that facilitate interpretation and intervention by clinicians, without compromising performance or requiring time-consuming image annotation.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Jian-Qing Zheng, Ziyang Wang, Baoru Huang, Ngee Han Lim, Bartlomiej W. Papiez
Summary: This article introduces a new method for medical image registration, which utilizes a separable motion backbone and a residual aligner module to better handle the discontinuous motion of multiple neighboring objects. The proposed method achieves excellent registration results on abdominal CT scans and lung CT scans.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangqiong Wu, Guanghua Tan, Hongxia Luo, Zhilun Chen, Bin Pu, Shengli Li, Kenli Li
Summary: This study develops a user-friendly framework for the automated diagnosis of thyroid nodules in ultrasound videos, simulating the diagnostic workflow of radiologists. By interpreting image characteristics and modeling temporal contextual information, the efficiency and generalizability of the diagnosis can be improved.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Riddhish Bhalodia, Shireen Elhabian, Jadie Adams, Wenzheng Tao, Ladislav Kavan, Ross Whitaker
Summary: This paper introduces DeepSSM, a deep learning-based framework for image-to-shape modeling. By learning the functional mapping from images to low-dimensional shape descriptors, DeepSSM can directly infer statistical representation of anatomy from 3D images. Compared to traditional methods, DeepSSM eliminates the need for heavy manual preprocessing and segmentation, and significantly improves computational time.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Florentin Liebmann, Marco von Atzigen, Dominik Stutz, Julian Wolf, Lukas Zingg, Daniel Suter, Nicola A. Cavalcanti, Laura Leoty, Hooman Esfandiari, Jess G. Snedeker, Martin R. Oswald, Marc Pollefeys, Mazda Farshad, Philipp Furnstahl
Summary: This study presents a marker-less approach for automatic registration and real-time navigation of lumbar spinal fusion surgery using a deep neural network, avoiding radiation exposure and surgical errors. The method was validated on an ex-vivo surgery and a public dataset.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Piyush Tiwary, Kinjawl Bhattacharyya, A. P. Prathosh
Summary: Domain shift refers to the change of distributional characteristics between training and testing datasets, leading to performance drop. For medical image tasks, domain shift can be caused by changes in imaging modalities, devices, and staining mechanisms. Existing approaches based on generative models suffer from training difficulties and lack of diversity. In this paper, the authors propose the use of energy-based models (EBMs) for unpaired image-to-image translation in medical images. The proposed method, called Cycle Consistent Twin EBMs (CCT-EBM), employs a pair of EBMs in the latent space of an Auto-Encoder to ensure translation symmetry and coupling between domains.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Yutong Xie, Jianpeng Zhang, Lingqiao Liu, Hu Wang, Yiwen Ye, Johan Verjans, Yong Xia
Summary: This paper proposes a hybrid pre-training paradigm that combines self-supervised learning and supervised learning to improve the representation quality for medical image segmentation tasks. It introduces a reference task in self-supervised learning and optimizes the model using a gradient matching method. The experimental results demonstrate the effectiveness of this approach on multiple medical image segmentation benchmarks.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Youyi Song, Jing Zou, Kup-Sze Choi, Baiying Lei, Jing Qin
Summary: Cell classification is crucial for intelligent cervical cancer screening, but the variation in cells' appearance and shape poses challenges. A new learning algorithm, worse-case boosting, is proposed to improve classification accuracy for under-represented data. Experimental results demonstrate the effectiveness of this algorithm in two publicly available datasets, achieving a 4% improvement in accuracy.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Sangjoon Park, Eun Sun Lee, Kyung Sook Shin, Jeong Eun Lee, Jong Chul Ye
Summary: The increasing demand for AI systems to monitor human errors and abnormalities in healthcare presents challenges. This study presents a model called Medical X-VL, which is tailored for the medical domain and outperformed current state-of-the-art models in two medical image datasets. The model enables various zero-shot tasks for monitoring AI in the medical domain.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Anna Klimovskaia Susmelj, Berkan Lafci, Firat Ozdemir, Neda Davoudi, Xose Luis Dean-Ben, Fernando Perez-Cruz, Daniel Razansky
Summary: Optoacoustic imaging is a technique that uses optical excitation and ultrasound detection for biological tissue imaging. The quality of the images depends on the extent of tomographic coverage provided by the ultrasound detector arrays. However, full coverage is not always possible due to experimental constraints. The proposed signal domain adaptation network aims to reduce limited-view artifacts in the images.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Srijay Deshpande, Muhammad Dawood, Fayyaz Minhas, Nasir Rajpoot
Summary: In this work, a novel framework called SynCLay is proposed for automated synthesis of histology images based on user-defined cellular layouts. The framework can generate realistic and high-quality histology images with different cellular arrangements, which is helpful for studying the role of cells in the tumor microenvironment. The framework integrates a nuclear segmentation and classification model to refine nuclear structures and generate nuclear masks. Evaluation using quantitative metrics and feedback from pathologists shows that the synthetic images generated by SynCLay have high realism scores and can accurately differentiate between benign and malignant tumors.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber
Summary: Survival analysis is a valuable tool in healthcare for predicting the time to specific events. This paper introduces CenTime, a novel approach that directly estimates the time to event. The method performs well with censored data and can be easily integrated with deep learning models. Compared to standard methods, CenTime offers superior performance in predicting event time while maintaining comparable ranking performance.
MEDICAL IMAGE ANALYSIS
(2024)
Article
Computer Science, Artificial Intelligence
Bingyuan Liu, Jose Dolz, Adrian Galdran, Riadh Kobbi, Ismail Ben Ayed
Summary: Most segmentation losses, such as CE and Dice, are variants of the Cross-Entropy or Dice losses. This work provides a theoretical analysis that shows a deeper connection between CE and Dice than previously thought. From a constrained-optimization perspective, both CE and Dice decompose into similar ground-truth matching terms and region-size penalty terms. The analysis uncovers hidden region-size biases: Dice has an intrinsic bias towards extremely imbalanced solutions, while CE implicitly encourages the ground-truth region proportions. Based on this analysis, a principled and simple solution is proposed to explicitly control the region-size bias.
MEDICAL IMAGE ANALYSIS
(2024)