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Deep Neural Networks for ECG-free Cardiac Phase and End-Diastolic Frame Detection on Coronary Angiographies [article]

Costin Ciusdel, Alexandru Turcea, Andrei Puiu, Lucian Itu, Lucian Calmac, Emma Weiss, Cornelia Margineanu, Elisabeta Badila, Martin Berger, Thomas Redel, Tiziano Passerini, Mehmet Gulsun, Puneet Sharma
2018 arXiv   pre-print
Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive  ...  We evaluate the performance of a purely image based workflow based on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiographies.  ...  We conclude that the proposed image based workflow, employing deep neural networks, demonstrated good performance, thus potentially obviating the need for manual frame selection and ECG acquisition, representing  ... 
arXiv:1811.02797v1 fatcat:sxlwtygldfgpbda3aed7myw7hi

TCT-233 Machine-Learned Algorithms Utilizing Novel Tomography for Evaluating Coronary Artery Disease

Tom Stuckey, Frederick Meine, Narendra Singh, Prashant Kaul, Jeremiah Depta, Roger Gammon, John Steuter, Horace Gillins, Tim Burton, Ali Khosousi, Ian Shadforth, Shyam Ramchandani (+1 others)
2018 Journal of the American College of Cardiology  
We evaluate the performance of a workflow based on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiograms without the need for ECG measurements.  ...  A first deep neural network (DNN), trained to detect coronary arteries, is employed to preselect a subset of frames in which coronary arteries are well visible.  ...  We evaluate the performance of a workflow based on deep neural networks for fully automated cardiac phase and EDF detection on coronary angiograms without the need for ECG measurements.  ... 
doi:10.1016/j.jacc.2018.08.1358 fatcat:uzaxdcgwvna6nmkdby3quy56mi

End-to-End Deep Learning Model for Cardiac Cycle Synchronization from Multi-View Angiographic Sequences

Raphael Royer-Rivard, Fantin Girard, Nagib Dahdah, Farida Cheriet
2020 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)  
In this paper, we show how a neural network can be trained from angiographic sequences to synchronize different views during the cardiac cycle using raw x-ray angiography videos exclusively.  ...  First, we train a neural network model with angiographic sequences to extract features describing the progression of the cardiac cycle.  ...  Another recent work used a Deep Convolutional Neural Network (DCNN) to train a model to identify cardiac phases using a training dataset of several thousand coronary angiographies [7] .  ... 
doi:10.1109/embc44109.2020.9175453 pmid:33018200 fatcat:kctmlj35krcsxg3pbtxwjvfj4a

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images

Retesh Bajaj, Xingru Huang, Yakup Kilic, Ajay Jain, Anantharaman Ramasamy, Ryo Torii, James Moon, Tat Koh, Tom Crake, Maurizio K Parker, Vincenzo Tufaro, Patrick W Serruys (+6 others)
2021 The International Journal of Cardiovascular Imaging  
We developed a novel deep learning (DL)-methodology for end-diastolic frame detection in IVUS and compared its efficacy against expert analysts and a previously established methodology using electrocardiographic  ...  A window of ± 100 ms from the ECG estimations was used to define accurate end-diastolic frames detection. The ECG-signal identified 3,167 end-diastolic frames.  ...  However, accurate reconstruction of coronary anatomy requires fusion of angiography with intravascular images at the end-diastolic phase of the cardiac cycle.  ... 
doi:10.1007/s10554-021-02162-x pmid:33590430 pmcid:PMC8255253 fatcat:lnmh5g4l3fctzfcs23bxkt3cwa

Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos [article]

Yinghui Meng, Minghao Dong, Xumin Dai, Haipeng Tang, Chen Zhao, Jingfeng Jiang, Shun Xu, Ying Zhou, Fubao Zhu1, Zhihui Xu, Weihua Zhou
2021 arXiv   pre-print
Automatic identification of proper image frames at the end-diastolic (ED) and end-systolic (ES) frames during the review of invasive coronary angiograms (ICA) is important to assess blood flow during a  ...  In this paper, we propose a new method to automatically identify angiographic image frames associated with the ED and ES cardiac phases by using the trajectories of key vessel points (i.e. landmarks).  ...  [24] proposed a fully image-based method based on two deep neural networks to detect the cardiac phase and ED frames in ICA videos.  ... 
arXiv:2110.02844v1 fatcat:4e25xe4ntfbybezzmmz4gkeluu

Non-contrast coronary magnetic resonance angiography: current frontiers and future horizons

Yoko Kato, Bharath Ambale-Venkatesh, Yoshimori Kassai, Larry Kasuboski, Joanne Schuijf, Karan Kapoor, Shelton Caruthers, Joao A. C. Lima
2020 Magnetic Resonance Materials in Physics, Biology and Medicine  
Coronary magnetic resonance angiography (coronary MRA) is advantageous in its ability to assess coronary artery morphology and function without ionizing radiation or contrast media.  ...  In this paper, we first review the current clinical use and motivations for non-contrast coronary MRA, discuss currently available coronary MRA techniques, and highlight current technical developments  ...  JS and KK helped in critical revision. All authors contributed to the idea for the article. All authors read and approved the final manuscript.  ... 
doi:10.1007/s10334-020-00834-8 pmid:32242282 fatcat:cu4vrtjuwvatneey7gg4jxxbla

Artificial intelligence and automation in valvular heart diseases

Qiang Long, Xiaofeng Ye, Qiang Zhao
2020 Cardiology Journal  
This review also describes the state-of-the-art autonomous surgical robots and their roles in cardiac surgery and intervention.  ...  Thirdly, it introduces using AI algorithms to identify risk factors and predict mortality of cardiac surgery.  ...  Artificial neural network and deep learning Artificial neural network (ANN) is composed of multiple interconnected artificial neurons which mimic the biological brain.  ... 
doi:10.5603/cj.a2020.0087 pmid:32567669 pmcid:PMC8016001 fatcat:yzdeast6q5eurjwe7vsirs6rw4

Subject index: Abstracts

1991 Journal of the American College of Cardiology  
ventricular end-diastolic pressure on action potential duration, 385A Left ventricular end diastolic pressure may not be uniquely related to end diastolic volume in heart failure, 376A End-systolic  ...  an ECG and enzymes, 12 OiMS~gr Clinical assessment of a rapid sensitive enzyme immunoassay specific for human cardiac troponin-I, 330A Detection of coronary clot lysis with an improved ELISA for  ... 
doi:10.1016/0735-1097(91)92530-y fatcat:gzpj3vn6knhb3kyh2r3odxyi6q

Reconstruction techniques for cardiac cine MRI

Rosa-María Menchón-Lara, Federico Simmross-Wattenberg, Pablo Casaseca-de-la-Higuera, Marcos Martín-Fernández, Carlos Alberola-López
2019 Insights into Imaging  
The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic resonance image reconstruction.  ...  Additionally, clinical relevance, main challenges, and future trends of this image modality are outlined.  ...  Coronary MR angiography (MRA) is a promising imaging technique for detection of coronary artery disease (CAD).  ... 
doi:10.1186/s13244-019-0754-2 pmid:31549235 pmcid:PMC6757088 fatcat:s5574wj5pjadhbq5rah7k3h6lu

Cardiac MR: From Theory to Practice

Tevfik F. Ismail, Wendy Strugnell, Chiara Coletti, Maša Božić-Iven, Sebastian Weingärtner, Kerstin Hammernik, Teresa Correia, Thomas Küstner
2022 Frontiers in Cardiovascular Medicine  
The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this  ...  Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance  ...  imaging technique such as invasive coronary angiography or CT coronary angiography.  ... 
doi:10.3389/fcvm.2022.826283 pmid:35310962 pmcid:PMC8927633 fatcat:j3vd446nxnajvgajkhazwbvgia

A Comprehensive Review on Seismocardiogram: Current Advancements on Acquisition, Annotation, and Applications

Deepak Rai, Hiren Kumar Thakkar, Shyam Singh Rajput, Jose Santamaria, Chintan Bhatt, Francisco Roca
2021 Mathematics  
, deep learning, artificial neural networks, and fuzzy logic.  ...  Cardiac vibrations yield a wide and rich spectrum of essential information regarding the functioning of the heart, and thus it is necessary to take advantage of this data to better monitor cardiac health  ...  In [134] , a Deep Convolutional Neural Network (D-CNN)-based approach was proposed for robustly monitoring the cardiac activity from SCG signals.  ... 
doi:10.3390/math9182243 fatcat:uiwrndfe3jdgvebrvxdclrxg3q

Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

K. R. Siegersma, T. Leiner, D. P. Chew, Y. Appelman, L. Hofstra, J. W. Verjans
2019 Netherlands Heart Journal  
In the long term, AI will not only assist doctors, it has the potential to significantly improve access to health and well-being data for patients and their caretakers. This empowers patients.  ...  In the short term, it will assist physicians with easy tasks, such as automating measurements, making predictions based on big data, and putting clinical findings into an evidence-based context.  ...  A similar approach with a convolutional neural network was used to determine the calcium score from regular coronary CT angiography (CTA).  ... 
doi:10.1007/s12471-019-01311-1 pmid:31399886 pmcid:PMC6712136 fatcat:xclaar73wvexfmy4l47k67e2oe

Subject index: Abstracts

1992 Journal of the American College of Cardiology  
Hardware-Software Applications for Quantitative Angiography (985-119). llA MS Windows Program for Quantitation of Left Ventricular Systolic Function From EndDiastolic and End-Systolic Angiographic  ...  • 8A Computerized Interpretation of the ECG Using Expert System and Neural Network Techniques (839-8)·5A Development of a Client-Server, Relational Patient and Cardiac Catheterization Database Management  ... 
doi:10.1016/s0735-1097(10)80306-7 fatcat:ratpmc45enaenk6zjitbrkhq4q

Full Issue PDF

2020 JACC Cardiovascular Imaging  
0.38 AE 0.13 s -1 ), independent of changes in left ventricular global longitudinal strain, LA end-diastolic volume, and mitral regurgitation severity (p < 0.001).  ...  OBJECTIVES The aim of this study was to assess the effect of congestion and decongestive therapy on left atrial (LA) mechanics and to determine the relationship between LA improvement after decongestive  ...  The authors are solely responsible for the design and conduct of this study; all study analyses; and the drafting and editing of the paper and its final contents.  ... 
doi:10.1016/s1936-878x(20)30301-6 fatcat:w765zpyw4neohdsuytjzyy4gtm

The Role of AI in Characterizing the DCM Phenotype

Clint Asher, Esther Puyol-Antón, Maleeha Rizvi, Bram Ruijsink, Amedeo Chiribiri, Reza Razavi, Gerry Carr-White
2021 Frontiers in Cardiovascular Medicine  
Cardiac MRI (CMR) is well-placed in this respect, not only for its diagnostic utility, but the wealth of information captured in global and regional function assessment with the addition of unique tissue  ...  Dilated Cardiomyopathy is conventionally defined by left ventricular dilatation and dysfunction in the absence of coronary disease.  ...  FIGURE 2 | 2 FIGURE 2 | LV volume (LVV) curve for a cardiac cycle, in blue end diastole (ED) and end systole (ES) frames, in red peak ejection rate (PER), peak filling rate (PFR), atrial contribution (  ... 
doi:10.3389/fcvm.2021.787614 pmid:34993240 pmcid:PMC8724536 fatcat:v63dlo5mkzatfcy3bhsf3zkucq
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