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Mao Y, Shi Y, Qiao W, Zhang Z, Yang W, Liu H, Li E, Fan H, Liu Q. Symptom clusters and unplanned hospital readmission in Chinese patients with acute myocardial infarction on admission. Front Cardiovasc Med 2024; 11:1388648. [PMID: 38832319 PMCID: PMC11144855 DOI: 10.3389/fcvm.2024.1388648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/06/2024] [Indexed: 06/05/2024] Open
Abstract
Backgroud Acute myocardial infarction (AMI) has a high morbidity rate, high mortality rate, high readmission rate, high health care costs, and a high symptomatic, psychological, and economic burden on patients. Patients with AMI usually present with multiple symptoms simultaneously, which are manifested as symptom clusters. Symptom clusters have a profound impact on the quality of survival and clinical outcomes of AMI patients. Objective The purpose of this study was to analyze unplanned hospital readmissions among cluster groups within a 1-year follow-up period, as well as to identify clusters of acute symptoms and the characteristics associated with them that appeared in patients with AMI. Methods Between October 2021 and October 2022, 261 AMI patients in China were individually questioned for symptoms using a structured questionnaire. Mplus 8.3 software was used to conduct latent class analysis in order to find symptom clusters. Univariate analysis is used to examine characteristics associated with each cluster, and multinomial logistic regression is used to analyze a cluster membership as an independent predictor of hospital readmission after 1-year. Results Three unique clusters were found among the 11 acute symptoms: the typical chest symptom cluster (64.4%), the multiple symptom cluster (29.5%), and the atypical symptom cluster (6.1%). The cluster of atypical symptoms was more likely to have anemia and the worse values of Killip class compared with other clusters. The results of multiple logistic regression indicated that, in comparison to the typical chest cluster, the atypical symptom cluster substantially predicted a greater probability of 1-year hospital readmission (odd ratio 8.303, 95% confidence interval 2.550-27.031, P < 0.001). Conclusion Out of the 11 acute symptoms, we have found three clusters: the typical chest symptom, multiple symptom, and atypical symptom clusters. Compared to patients in the other two clusters, those in the atypical symptom cluster-which included anemia and a large percentage of Killip class patients-had worse clinical indicators at hospital readmission during the duration of the 1-year follow-up. Both anemia and high Killip classification suggest that the patient's clinical presentation is poor and therefore the prognosis is worse. Intensive treatment should be considered for anemia and high level of Killip class patients with atypical presentation. Clinicians should focus on patients with atypical symptom clusters, enhance early recognition of symptoms, and develop targeted symptom management strategies to alleviate their discomfort in order to improve symptomatic outcomes.
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Affiliation(s)
- Yijun Mao
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Yuqiong Shi
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Wenfang Qiao
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Zhuo Zhang
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Wei Yang
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Haili Liu
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Erqing Li
- Department of Cardiology, Xianyang Central Hospital, Shaanxi, China
| | - Hui Fan
- Department of Nursing, Xianyang Central Hospital, Shaanxi, China
| | - Qiang Liu
- Department of Orthopedic, Xianyang Central Hospital, Shaanxi, China
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Cao H, Zeng H, Lv L, Wang Q, Ouyang H, Gui L, Hua P, Yang S. Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model. Front Physiol 2024; 15:1293380. [PMID: 38426204 PMCID: PMC10901972 DOI: 10.3389/fphys.2024.1293380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing. Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors. Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67. Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model's performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.
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Affiliation(s)
- Heshan Cao
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hui Zeng
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lei Lv
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qi Wang
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hua Ouyang
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Long Gui
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Ping Hua
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Songran Yang
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
- Department of Biobank and Bioinformatics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
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MacRaild M, Sarrami-Foroushani A, Lassila T, Frangi AF. Accelerated simulation methodologies for computational vascular flow modelling. J R Soc Interface 2024; 21:20230565. [PMID: 38350616 PMCID: PMC10864099 DOI: 10.1098/rsif.2023.0565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/12/2024] [Indexed: 02/15/2024] Open
Abstract
Vascular flow modelling can improve our understanding of vascular pathologies and aid in developing safe and effective medical devices. Vascular flow models typically involve solving the nonlinear Navier-Stokes equations in complex anatomies and using physiological boundary conditions, often presenting a multi-physics and multi-scale computational problem to be solved. This leads to highly complex and expensive models that require excessive computational time. This review explores accelerated simulation methodologies, specifically focusing on computational vascular flow modelling. We review reduced order modelling (ROM) techniques like zero-/one-dimensional and modal decomposition-based ROMs and machine learning (ML) methods including ML-augmented ROMs, ML-based ROMs and physics-informed ML models. We discuss the applicability of each method to vascular flow acceleration and the effectiveness of the method in addressing domain-specific challenges. When available, we provide statistics on accuracy and speed-up factors for various applications related to vascular flow simulation acceleration. Our findings indicate that each type of model has strengths and limitations depending on the context. To accelerate real-world vascular flow problems, we propose future research on developing multi-scale acceleration methods capable of handling the significant geometric variability inherent to such problems.
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Affiliation(s)
- Michael MacRaild
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- EPSRC Centre for Doctoral Training in Fluid Dynamics, University of Leeds, Leeds, UK
| | - Ali Sarrami-Foroushani
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Health Science, University of Manchester, Manchester, UK
| | - Toni Lassila
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Computing, University of Leeds, Leeds, UK
| | - Alejandro F. Frangi
- Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), University of Leeds, Leeds, UK
- School of Computer Science, University of Manchester, Manchester, UK
- School of Health Science, University of Manchester, Manchester, UK
- Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
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Wang S, Ono R, Wu D, Aoki K, Kato H, Iwahana T, Okada S, Kobayashi Y, Liu H. Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning. Biomed Eng Online 2024; 23:7. [PMID: 38243221 PMCID: PMC10797936 DOI: 10.1186/s12938-024-01201-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024] Open
Abstract
Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.
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Affiliation(s)
- Sirui Wang
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Ryohei Ono
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Dandan Wu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Kaoruko Aoki
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hirotoshi Kato
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Togo Iwahana
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sho Okada
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yoshio Kobayashi
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hao Liu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
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Lauric A, Ludwig CG, Malek AM. Topological Data Analysis and Use of Mapper for Cerebral Aneurysm Rupture Status Discrimination Based on 3-Dimensional Shape Analysis. Neurosurgery 2023; 93:1285-1295. [PMID: 37387576 DOI: 10.1227/neu.0000000000002570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/26/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Topological data analysis (TDA), which identifies patterns in data through simplified topological signatures, has yet to be applied to aneurysm research. We investigate TDA Mapper graphs (Mapper) for aneurysm rupture discrimination. METHODS Two hundred sixteen bifurcation aneurysms (90 ruptured) from 3-dimensional rotational angiography were segmented from vasculature and evaluated for 12 size/shape and 18 enhanced radiomics features. Using Mapper, uniformly dense aneurysm models were represented as graph structures and described by graph shape metrics. Mapper dissimilarity scores (MDS) were computed between pairs of aneurysms based on shape metrics. Lower MDS described similar shapes, whereas high MDS represented shapes that do not share common characteristics. Ruptured/unruptured average MDS scores (how "far" an aneurysm is shape-wise to ruptured/unruptured data sets, respectively) were evaluated for each aneurysm. Rupture status discrimination univariate and multivariate statistics were reported for all features. RESULTS The average MDS for pairs of ruptured aneurysms were significantly larger compared with unruptured pairs (0.055 ± 0.027 vs 0.039 ± 0.015, P < .0001). Low MDS suggest that, in contrast to ruptured aneurysms, unruptured aneurysms have similar shape characteristics. An MDS threshold value of 0.0417 (area under the curve [AUC] = 0.73, 80% specificity, 60% sensitivity) was identified for rupture status classification. Under this predictive model, MDS scores <0.0417 would identify unruptured status. MDS statistical performance in discriminating rupture status was similar to that of nonsphericity and radiomics Flatness (AUC = 0.73), outperforming other features. Ruptured aneurysms were more elongated ( P < .0001), flatter ( P < .0001), and showed higher nonsphericity ( P < .0001) compared with unruptured. Including MDS in multivariate analysis resulted in AUC = 0.82, outperforming multivariate analysis on size/shape (AUC = 0.76) and enhanced radiomics (AUC = 0.78) alone. CONCLUSION A novel application of Mapper TDA was proposed for aneurysm evaluation, with promising results for rupture status classification. Multivariate analysis incorporating Mapper resulted in high accuracy, which is particularly important given that bifurcation aneurysms are challenging to classify morphologically. This proof-of-concept study warrants future investigation into optimizing Mapper functionality for aneurysm research.
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Affiliation(s)
- Alexandra Lauric
- Cerebrovascular Hemodynamics Laboratory, Department of Neurosurgery, Tufts Medical Center and Tufts University School of Medicine, Boston , Massachusetts , USA
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Zhang X, Mao B, Che Y, Kang J, Luo M, Qiao A, Liu Y, Anzai H, Ohta M, Guo Y, Li G. Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology. Comput Biol Med 2023; 164:107287. [PMID: 37536096 DOI: 10.1016/j.compbiomed.2023.107287] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/06/2023] [Accepted: 07/28/2023] [Indexed: 08/05/2023]
Abstract
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of hemodynamics remains a challenge for current invasive detection and simulation algorithms. Here, we integrate computational fluid dynamics with our customized analysis framework based on a multi-attribute point cloud dataset and physics-informed neural networks (PINNs)-aided deep learning modules. This combination is implemented by our workflow that generates flow field datasets within two types of patient personalized models - aorta with fine coronary branches and abdominal aorta. Deep learning modules with or without an antecedent hierarchical structure model the flow field development and complete the mapping from spatial and temporal dimensions to 4D hemodynamics. 88,000 cases on 4 randomized partitions in 16 controlled trials reveal the hemodynamic landscape of spatio-temporal anisotropy within two types of personalized models, which demonstrates the effectiveness of PINN in predicting the space-time behavior of flow fields and gives the optimal deep learning framework for different blood vessels in terms of balancing the training cost and accuracy dimensions. The proposed framework shows intentional performance in computational cost, accuracy and visualization compared to currently prevalent methods, and has the potential for generalization to model flow fields and corresponding clinical metrics within vessels at different locations. We expect our framework to push the 4D hemodynamic predictions to the real-time level, and in statistically significant fashion, applicable to morphologically variable vessels.
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Affiliation(s)
- Xuelan Zhang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Baoyan Mao
- Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yue Che
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Jiaheng Kang
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China
| | - Mingyao Luo
- Department of Vascular Surgery, Fuwai Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100037, China; Department of Vascular Surgery, Fuwai Yunnan Cardiovascular Hospital, Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming, 650102, China
| | - Aike Qiao
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Youjun Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Hitomi Anzai
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Makoto Ohta
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan
| | - Yuting Guo
- Department of Mechanical Engineering and Science, Kyoto University, Kyoto, 615-8540, Japan
| | - Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577, Japan.
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Li G, Zhu Y, Guo Y, Mabuchi T, Li D, Huang S, Wang S, Sun H, Tokumasu T. Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers. ACS APPLIED MATERIALS & INTERFACES 2023; 15:5099-5108. [PMID: 36652634 DOI: 10.1021/acsami.2c17198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.
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Affiliation(s)
- Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Yonghong Zhu
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Yuting Guo
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takuya Mabuchi
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
- Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi980-8577, Japan
| | - Dong Li
- School of Chemical Engineering, Northwest University, Xi'an710069Shaanxi, China
| | - Shengfeng Huang
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Sirui Wang
- Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba263-8522, Japan
| | - Haiyi Sun
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
| | - Takashi Tokumasu
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai980-8577, Japan
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Wang S, Wu D, Li G, Zhang Z, Xiao W, Li R, Qiao A, Jin L, Liu H. Deep learning-based hemodynamic prediction of carotid artery stenosis before and after surgical treatments. Front Physiol 2023; 13:1094743. [PMID: 36703930 PMCID: PMC9872942 DOI: 10.3389/fphys.2022.1094743] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/09/2022] [Indexed: 01/11/2023] Open
Abstract
Hemodynamic prediction of carotid artery stenosis (CAS) is of great clinical significance in the diagnosis, prevention, and treatment prognosis of ischemic strokes. While computational fluid dynamics (CFD) is recognized as a useful tool, it shows a crucial issue that the high computational costs are usually required for real-time simulations of complex blood flows. Given the powerful feature-extraction capabilities, the deep learning (DL) methodology has a high potential to implement the mapping of anatomic geometries and CFD-driven flow fields, which enables accomplishing fast and accurate hemodynamic prediction for clinical applications. Based on a brain/neck CT angiography database of 280 subjects, image based three-dimensional CFD models of CAS were constructed through blood vessel extraction, computational domain meshing and setting of the pulsatile flow boundary conditions; a series of CFD simulations were undertaken. A DL strategy was proposed and accomplished in terms of point cloud datasets and a DL network with dual sampling-analysis channels. This enables multimode mapping to construct the image-based geometries of CAS while predicting CFD-based hemodynamics based on training and testing datasets. The CFD simulation was validated with the mass flow rates at two outlets reasonably agreed with the published results. Comprehensive analysis and error evaluation revealed that the DL strategy enables uncovering the association between transient blood flow characteristics and artery cavity geometric information before and after surgical treatments of CAS. Compared with other methods, our DL-based model trained with more clinical data can reduce the computational cost by 7,200 times, while still demonstrating good accuracy (error<12.5%) and flow visualization in predicting the two hemodynamic parameters. In addition, the DL-based predictions were in good agreement with CFD simulations in terms of mean velocity in the stenotic region for both the preoperative and postoperative datasets. This study points to the capability and significance of the DL-based fast and accurate hemodynamic prediction of preoperative and postoperative CAS. For accomplishing real-time monitoring of surgical treatments, further improvements in the prediction accuracy and flexibility may be conducted by utilizing larger datasets with specific real surgical events such as stent intervention, adopting personalized boundary conditions, and optimizing the DL network.
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Affiliation(s)
- Sirui Wang
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Dandan Wu
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Gaoyang Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Zhiyuan Zhang
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Weizhong Xiao
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ruichen Li
- Graduate School of Engineering, Chiba University, Chiba, Japan
| | - Aike Qiao
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China
| | - Long Jin
- Department of Interventional Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China,*Correspondence: Hao Liu, ; Long Jin,
| | - Hao Liu
- Graduate School of Engineering, Chiba University, Chiba, Japan,*Correspondence: Hao Liu, ; Long Jin,
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Lv L, Li H, Wu Z, Zeng W, Hua P, Yang S. An artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:525-534. [PMID: 36710907 PMCID: PMC9779925 DOI: 10.1093/ehjdh/ztac058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/01/2022] [Accepted: 09/06/2022] [Indexed: 11/07/2022]
Abstract
Aims Aortopathies are a series of disorders requiring multiple indicators to assess risk. Time-averaged wall shear stress (TAWSS) is currently considered as the primary indicator of aortopathies progression, which can only be calculated by Computational Fluid Dynamics (CFD). However, CFD's complexity and high computational cost, greatly limit its application. The study aimed to construct a deep learning platform which could accurately estimate TAWSS in ascending aorta. Methods and results A total of 154 patients who had thoracic computed tomography angiography were included and randomly divided into two parts: training set (90%, n = 139) and testing set (10%, n = 15). TAWSS were calculated via CFD. The artificial intelligence (AI)-based model was trained and assessed using the dice coefficient (DC), normalized mean absolute error (NMAE), and root mean square error (RMSE). Our AI platform brought into correspondence with the manual segmentation (DC = 0.86) and the CFD findings (NMAE, 7.8773% ± 4.7144%; RMSE, 0.0098 ± 0.0097), while saving 12000-fold computational cost. Conclusion The high-efficiency and robust AI platform can automatically estimate value and distribution of TAWSS in ascending aorta, which may be suitable for clinical applications and provide potential ideas for CFD-based problem solving.
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Affiliation(s)
| | | | - Zonglv Wu
- Department of Cardio-Vascular Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yan Jiang West Road, 510120 Guangzhou, China,Department of Cardiac Surgery, Guangzhou Women and Children's Medical Center, No. 9 Jinsui Road, 510623 Guangzhou, China
| | - Weike Zeng
- Department of Radiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, No. 107 Yanjiang West Road, 510120 Guangzhou, China
| | - Ping Hua
- Corresponding author. Tel: +86 13 609716875, Fax: +86 20 81332199, (P.H.); Tel: +86 13926168990, Fax: +86 20 81332199, (S.R.)
| | - Songran Yang
- Corresponding author. Tel: +86 13 609716875, Fax: +86 20 81332199, (P.H.); Tel: +86 13926168990, Fax: +86 20 81332199, (S.R.)
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Abstract
Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better understanding various medical conditions, designing more effective drug delivery systems, and developing novel diagnostic methods and treatments. However, despite significant advances in computational technology and resources, the expensive computational cost of these simulations still hinders their transformation from a research interest to a clinical tool. This bottleneck is even more severe for image-based, patient-specific CFD simulations with realistic boundary conditions and complex computational domains, which make such simulations excessively expensive. To address this issue, deep learning approaches have been recently explored to accelerate computational hemodynamics simulations. In this study, we review recent efforts to integrate deep learning with CFD and discuss the applications of this approach in solving hemodynamics problems, such as blood flow behavior in aorta and cerebral arteries. We also discuss potential future directions in the field. In this review, we suggest that incorporating physiologic understandings and underlying fluid mechanics laws in deep learning models will soon lead to a paradigm shift in the development novel non-invasive computational medical decisions.
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Guo Y, Li G, Mabuchi T, Surblys D, Ohara T, Tokumasu T. Prediction of nanoscale thermal transport and adsorption of liquid containing surfactant at solid–liquid interface via deep learning. J Colloid Interface Sci 2022; 613:587-596. [DOI: 10.1016/j.jcis.2022.01.037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 10/19/2022]
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Li G, Ji J, Ni J, Wang S, Guo Y, Hu Y, Liu S, Huang SF, Li YY. Application of deep learning for predicting the treatment performance of real municipal wastewater based on one-year operation of two anaerobic membrane bioreactors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 813:151920. [PMID: 34838555 DOI: 10.1016/j.scitotenv.2021.151920] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 11/05/2021] [Accepted: 11/19/2021] [Indexed: 06/13/2023]
Abstract
In this study, data-driven deep learning methods were applied in order to model and predict the treatment of real municipal wastewater using anaerobic membrane bioreactors (AnMBRs). Based on the one-year operating data of two AnMBRs, six parameters related to the experimental conditions (temperature of reactor, temperature of environment, temperature of influent, influent pH, influent COD, and flux) and eight parameters for wastewater treatment evaluation (effluent pH, effluent COD, COD removal efficiency, biogas composition (CH4, N2, and CO2), biogas production rate, and oxidation-reduction potential) were selected to establish the data sets. Three deep learning network structures were proposed to analyze and reproduce the relationship between the input parameters and output evaluation parameters. The statistical analysis showed that deep learning closely agrees with the AnMBR experimental results. The prediction accuracy rate of the proposed densely connected convolutional network (DenseNet) can reach up to 97.44%, and the single calculation time can be reduced to within 1 s, suggesting the high performance of AnMBR treatment prediction with deep learning methods.
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Affiliation(s)
- Gaoyang Li
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Jiayuan Ji
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.
| | - Jialing Ni
- Department of Chemical Engineering, Graduate School of Engineering, Tohoku University, 6-6-07 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan
| | - Sirui Wang
- Graduate School of Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
| | - Yuting Guo
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Yisong Hu
- Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan; Key Lab of Environmental Engineering, Shaanxi Province, Xi'an University of Architecture and Technology, No. 13 Yanta Road, Xi'an 710055, PR China
| | - Siwei Liu
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Sheng-Feng Huang
- Institute of Fluid Science, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Yu-You Li
- Department of Civil and Environmental Engineering, Graduate School of Engineering, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba-ku, Sendai 980-8579, Japan
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