scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

scholarly journals Prediction of Cerebral Aneurysm Hemodynamics With Porous-Medium Models of Flow-Diverting Stents via Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Gaoyang Li ◽  
Xiaorui Song ◽  
Haoran Wang ◽  
Siwei Liu ◽  
Jiayuan Ji ◽  
...  
Keyword(s):  
Porous Medium ◽  
Deep Learning ◽  
Cerebral Aneurysm ◽  
Stent Placement ◽  
Point Cloud ◽  
Computational Cost ◽  
Learning Method ◽  
Method Error ◽  
Complex Operation ◽  
Before And After

The interventional treatment of cerebral aneurysm requires hemodynamics to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in calculating cerebral aneurysm hemodynamics before and after flow-diverting (FD) stent placement. However, the complex operation (such as the construction and placement simulation of fully resolved or porous-medium FD stent) and high computational cost of CFD hinder its application. To solve these problems, we applied aneurysm hemodynamics point cloud data sets and a deep learning network with double input and sampling channels. The flexible point cloud format can represent the geometry and flow distribution of different aneurysms before and after FD stent (represented by porous medium layer) placement with high resolution. The proposed network can directly analyze the relationship between aneurysm geometry and internal hemodynamics, to further realize the flow field prediction and avoid the complex operation of CFD. Statistical analysis shows that the prediction results of hemodynamics by our deep learning method are consistent with the CFD method (error function <13%), but the calculation time is significantly reduced 1,800 times. This study develops a novel deep learning method that can accurately predict the hemodynamics of different cerebral aneurysms before and after FD stent placement with low computational cost and simple operation processes.

scholarly journals Use of a 3D Virtually Reconstructed Patient-Specific Model to Examine the Effect of Acetabular Labral Interference on Hip Range of Motion

2020 ◽  
Vol 8 (11) ◽  
pp. 232596712096446
Author(s):  
Shota Higashihira ◽  
Naomi Kobayashi ◽  
Hyonmin Choe ◽  
Kosuke Sumi ◽  
Yutaka Inaba
Keyword(s):  
Range Of Motion ◽  
Internal Rotation ◽  
Dynamic Simulation ◽  
Rotation Angle ◽  
Specific Model ◽  
Developmental Dysplasia ◽  
Patient Specific ◽  
Before And After ◽  
Internal Rotation Angle ◽  
Mri Scans

Background: The labrum is likely to influence impingement, which may also depend on acetabular coverage. Simulating impingement using 3-dimensional (3D) computed tomography (CT) is a potential solution to evaluating range of motion (ROM); however, it is based on bony structures rather than on soft tissue. Purpose: To examine ROM when the labrum is considered in a 3D dynamic simulation. A particular focus was evaluation of maximum flexion and internal rotation angles before occurrence of impingement, comparing them in cases of cam-type femoroacetabular impingement (FAI) and borderline developmental dysplasia of the hip (BDDH). Study Design: Descriptive laboratory study. Methods: Magnetic resonance imaging (MRI) and CT scans of 40 hips (20 with cam-type FAI and 20 with BDDH) were reviewed retrospectively. The thickness and width of the labrum were measured on MRI scans. A virtual labrum was reconstructed based on patient-specific sizes measured on MRI scans. The impingement point was identified using 3D dynamic simulation and was compared with the internal rotation angle before and after labral reconstruction. Results: The thickness and width of the labrum were significantly larger in BDDH than in FAI ( P < .001). In FAI, the maximum internal rotation angles without the labrum were 30.3° at 90° of flexion and 56.9° at 45° of flexion, with these values decreasing to 18.7° and 41.4°, respectively, after labral reconstruction ( P < .001). In BDDH, the maximum internal rotation angles were 48.0° at 90° of flexion and 76.7° at 45° of flexion without the labrum, decreasing to 31.1° and 55.3°, respectively, after labral reconstruction ( P < .001). The differences in the angles before and after labral reconstruction were larger in BDDH than in FAI (90° of flexion, P = .03; 45° of flexion, P = .01). Conclusion: As the labrum was significantly more hypertrophic in BDDH than in FAI, the virtual labral model revealed that the labrum’s interference with the maximum internal rotation angle was also significantly larger in BDDH. Clinical Relevance: The labrum has a significant effect on impingement; this is more significant for BDDH than for FAI.

scholarly journals A Deep Learning Approach for Single Shot C-Arm Pose Estimation

10.29007/6mkk ◽  
2020 ◽  
Author(s):  
Hooman Esfandiari ◽  
Sebastian Andreß ◽  
Maternus Herold ◽  
Wolfgang Böcker ◽  
Simon Weidert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Operation Time ◽  
Learning System ◽  
Patient Specific ◽  
Single Shot ◽  
X Rays ◽  
Similar Range ◽  
Deep Learning Network ◽  
Ionizing Radiation Exposure

During a typical fluoroscopic guided surgery, it is common to acquire multiple x-ray images to correctly position the C-arm. This can be a long process resulting in an in- crease in operation time and ionizing radiation exposure. Our purpose in this study is to implement a machine learning system for predicting the position of the C-arm based on the intraoperative radiographs. The prediction is achieved by training a Deep Learning Network based on Digitally Reconstructed Radiographs. We first showed a high prediction accuracy (4.5 mm and 1.1o) when patient-specific training was implemented. Additionally, we demonstrated a similar range of accuracy by applying transfer-learning on the last lay- ers of the network while reducing the processing time by 83%. In conclusion, in this study, we propose a C-arm position prediction system based on machine learning that can po- tentially reduce the number of intraoperatively acquired X-rays in a common orthopaedic surgical procedure.

scholarly journals Emulating complex simulations by machine learning methods

2021 ◽  
Vol 22 (S14) ◽  
Author(s):  
Paola Stolfi ◽  
Filippo Castiglione
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Mobile Devices ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Specific Model ◽  
Patient Specific ◽  
Complex Biological System ◽  
Self Monitoring

Abstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. Results Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. Conclusion The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

scholarly journals UAVs in rail damage image diagnostics supported by deep-learning networks

2021 ◽  
Vol 11 (1) ◽  
pp. 339-348
Author(s):  
Piotr Bojarczak ◽  
Piotr Lesiak
Keyword(s):  
Deep Learning ◽  
Head Width ◽  
Learning Networks ◽  
Image Brightness ◽  
Learning Network ◽  
Rail Head ◽  
Image Recording ◽  
Python Language ◽  
Deep Learning Network ◽  
Efficiency Rate

Abstract The article uses images from Unmanned Aerial Vehicles (UAVs) for rail diagnostics. The main advantage of such a solution compared to traditional surveys performed with measuring vehicles is the elimination of decreased train traffic. The authors, in the study, limited themselves to the diagnosis of hazardous split defects in rails. An algorithm has been proposed to detect them with an efficiency rate of about 81% for defects not less than 6.9% of the rail head width. It uses the FCN-8 deep-learning network, implemented in the Tensorflow environment, to extract the rail head by image segmentation. Using this type of network for segmentation increases the resistance of the algorithm to changes in the recorded rail image brightness. This is of fundamental importance in the case of variable conditions for image recording by UAVs. The detection of these defects in the rail head is performed using an algorithm in the Python language and the OpenCV library. To locate the defect, it uses the contour of a separate rail head together with a rectangle circumscribed around it. The use of UAVs together with artificial intelligence to detect split defects is an important element of novelty presented in this work.

scholarly journals BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets

2021 ◽  
Vol 11 (13) ◽  
pp. 5880
Author(s):  
Paloma Tirado-Martin ◽  
Raul Sanchez-Reillo
Keyword(s):  
Deep Learning ◽  
Temporal Variations ◽  
Physical Activities ◽  
Learning Tools ◽  
Biometric Recognition ◽  
Learning Network ◽  
Deep Learning Network ◽  
Electrocardiogram Ecg

Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.

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