scholarly journals Application of Pre-Trained Deep Learning Models for Clinical ECGs

2021 ◽  
Author(s):  
Theresa Bender ◽  
Tim Seidler ◽  
Philipp Bengel ◽  
Ulrich Sax ◽  
Dagmar Krefting
Keyword(s):  
Deep Learning ◽  
Clinical Relevance ◽  
Added Value ◽  
Computer Assisted ◽  
Rounding Errors ◽  
Ecg Analysis ◽  
Runtime Environment ◽  
Assisted Diagnosis ◽  
Electrocardiogram Ecg ◽  
Complex Cases

Automatic electrocardiogram (ECG) analysis has been one of the very early use cases for computer assisted diagnosis (CAD). Most ECG devices provide some level of automatic ECG analysis. In the recent years, Deep Learning (DL) is increasingly used for this task, with the first models that claim to perform better than human physicians. In this manuscript, a pilot study is conducted to evaluate the added value of such a DL model to existing built-in analysis with respect to clinical relevance. 29 12-lead ECGs have been analyzed with a published DL model and results are compared to build-in analysis and clinical diagnosis. We could not reproduce the results of the test data exactly, presumably due to a different runtime environment. However, the errors were in the order of rounding errors and did not affect the final classification. The excellent performance in detection of left bundle branch block and atrial fibrillation that was reported in the publication could be reproduced. The DL method and the built-in method performed similarly good for the chosen cases regarding clinical relevance. While benefit of the DL method for research can be attested and usage in training can be envisioned, evaluation of added value in clinical practice would require a more comprehensive study with further and more complex cases.

Clinical relevance of model based computer-assisted diagnosis and therapy

2008 ◽  
Author(s):  
Andrea Schenk ◽  
Stephan Zidowitz ◽  
Holger Bourquain ◽  
Milo Hindennach ◽  
Christian Hansen ◽  
...  
Keyword(s):  
Clinical Relevance ◽  
Computer Assisted ◽  
Model Based ◽  
Computer Assisted Diagnosis ◽  
Diagnosis And Therapy ◽  
Assisted Diagnosis

scholarly journals Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

2022 ◽  
Vol 12 (1) ◽  
pp. 738
Author(s):  
Akella S. Narasimha Raju ◽  
Kayalvizhi Jayavel ◽  
Tulasi Rajalakshmi
Keyword(s):  
Colorectal Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Premalignant Lesions ◽  
Computer Assisted ◽  
Screening Methods ◽  
Learning Approaches ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>

scholarly journals A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images

2021 ◽  
Vol 18 (5) ◽  
pp. 6978-3994
Author(s):  
Zijian Wang ◽  
◽  
Yaqin Zhu ◽  
Haibo Shi ◽  
Yanting Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Mental Disease ◽  
Computer Assisted ◽  
Multi Scale ◽  
Mental Diseases ◽  
Assisted Diagnosis ◽  
3D Cnn

<abstract> <p>Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.</p> </abstract>

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