scholarly journals Disease Classification from ECG Signal Using R-Peak Analysis with Artificial Intelligence

2018 ◽  
Vol 11 (3) ◽  
pp. 29-40
Author(s):  
Pooja Sharma ◽  
D.V Gupta
Keyword(s):  
Artificial Intelligence ◽  
Disease Classification ◽  
Ecg Signal

scholarly journals 3D Image Analysis and Artificial Intelligence for Bone Disease Classification

2009 ◽  
Vol 34 (5) ◽  
pp. 815-828 ◽  
Author(s):  
Abdurrahim Akgundogdu ◽  
Rachid Jennane ◽  
Gabriel Aufort ◽  
Claude Laurent Benhamou ◽  
Osman Nuri Ucan
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Bone Disease ◽  
Disease Classification ◽  
3D Image ◽  
3D Image Analysis

Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

2022 ◽  
pp. 80-127
Author(s):  
Viswanathan Rajagopalan ◽  
Houwei Cao
Keyword(s):  
Artificial Intelligence ◽  
Disease Management ◽  
Heart Attack ◽  
Congenital Heart ◽  
The United States ◽  
Disease Classification ◽  
Gradient Boosting ◽  
Support Vector ◽  
Gradient Boosting Machine ◽  
Infarction Heart

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc.

scholarly journals Opportunities for Artificial Intelligence in Advancing Precision Medicine

2019 ◽  
Vol 7 (4) ◽  
pp. 208-213 ◽  
Author(s):  
Fabian V. Filipp
Keyword(s):  
Artificial Intelligence ◽  
Precision Medicine ◽  
Large Scale ◽  
Digital Health ◽  
Disease Classification ◽  
Biomedical Data ◽  
Cell Clustering ◽  
Genome Wide ◽  
Definition Of ◽  
Disease Signatures

Abstract Purpose of Review We critically evaluate the future potential of machine learning (ML), deep learning (DL), and artificial intelligence (AI) in precision medicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. Recent Findings High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays; libraries of medical images; or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single-cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Summary Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.

scholarly journals An Artificial Intelligence Framework for Plant Leaf Disease Detection and Classification Using AMBF with GKFCM and GLCM

2021 ◽  
Vol 36 (1) ◽  
pp. 443-450
Author(s):  
Mounika Jammula
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Plant Disease ◽  
Bilateral Filter ◽  
Noise Removal ◽  
Classification Performance ◽  
Disease Classification ◽  
Gaussian Kernel ◽  
Shape Features

As of 2020, the total area planted with crops in India overtook 125.78 million hectares. India is the second biggest organic product maker in the world. Thus, an Indian economy greatly depends on farming products. Nowadays, farmers suffer a drop in production due to a lot of diseases and pests. Thus, to overcome this problem, this article presents the artificial intelligence based deep learning approach for plant disease classification. Initially, the adaptive mean bilateral filter (AMBF) for noise removal and enhancement operations. Then, Gaussian kernel fuzzy C-means (GKFCM) approach is used to segment the effected disease regions. The optimal features from color, texture and shape features are extracted by using GLCM. Finally, Deep learning convolutional neural network (DLCNN) is used for the classification of five class diseases. The segmentation and classification performance of proposed method outperforms as compared with the state of art approaches.

scholarly journals AI4AD: Artificial intelligence analysis for Alzheimer's disease classification based on a multisite DTI database

2021 ◽  
Vol 1 ◽  
pp. 100005 ◽  
Author(s):  
Yida Qu ◽  
Pan Wang ◽  
Bing Liu ◽  
Chengyuan Song ◽  
Dawei Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Classification ◽  
Intelligence Analysis

Artificial intelligence and inflammatory bowel disease: practicalities and future prospects

2021 ◽  
pp. flgastro-2021-102003
Author(s):  
Johanne Brooks-Warburton ◽  
James Ashton ◽  
Anjan Dhar ◽  
Tony Tham ◽  
Patrick B Allen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Inflammatory Bowel Disease ◽  
Risk Stratification ◽  
Bowel Disease ◽  
Emerging Technology ◽  
Disease Classification ◽  
Future Prospects ◽  
Patient Journey ◽  
Self Monitoring ◽  
Inflammatory Bowel

Artificial intelligence (AI) is an emerging technology predicted to have significant applications in healthcare. This review highlights AI applications that impact the patient journey in inflammatory bowel disease (IBD), from genomics to endoscopic applications in disease classification, stratification and self-monitoring to risk stratification for personalised management. We discuss the practical AI applications currently in use while giving a balanced view of concerns and pitfalls and look to the future with the potential of where AI can provide significant value to the care of the patient with IBD.

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