scholarly journals Artificial Intelligence Applications in the Imaging of Epilepsy and Its Comorbidities: Present and Future

2022 ◽  
pp. 153575972110686
Author(s):  
Fernando Cendes ◽  
Carrie R. McDonald
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Treatment Outcomes ◽  
Medical Image ◽  
Clinical Utility ◽  
Medical Image Analysis ◽  
Large Datasets ◽  
Future Studies ◽  
Patients With Epilepsy

Artificial intelligence (AI) is increasingly used in medical image analysis and has accelerated scientific discoveries across fields of medicine. In this review, we highlight how AI has been applied to neuroimaging in patients with epilepsy to enhance classification of clinical diagnosis, prediction of treatment outcomes, and the understanding of cognitive comorbidities. We outline the strengths and shortcomings of current AI research and the need for future studies using large datasets that test the reproducibility and generalizability of current findings, as well as studies that test the clinical utility of AI approaches.

scholarly journals Medical Image Analysis Using Artificial Intelligence

2019 ◽  
Vol 30 (2) ◽  
pp. 49 ◽  
Author(s):  
Hyun Jin Yoon ◽  
Young Jin Jeong ◽  
Hyun Kang ◽  
Ji Eun Jeong ◽  
Do-Young Kang
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

scholarly journals What is new in computer vision and artificial intelligence in medical image analysis applications

2021 ◽  
Vol 11 (8) ◽  
pp. 3830-3853
Author(s):  
Jimena Olveres ◽  
Germán González ◽  
Fabian Torres ◽  
José Carlos Moreno-Tagle ◽  
Erik Carbajal-Degante ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

scholarly journals Utilization of Artificial Intelligence in Medical Image Analysis for COVID-19 Patients Detection

2021 ◽  
Vol 29 (3) ◽  
pp. 97-111
Author(s):  
Mohammed Baz ◽  
Hatem Zaini ◽  
Hala S. El-sayed ◽  
Matokah AbuAlNaja ◽  
Heba M. El-Hoseny ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis

Artificial Intelligence and Big Data

2020 ◽  
Vol 237 (12) ◽  
pp. 1438-1441
Author(s):  
Soenke Langner ◽  
Ebba Beller ◽  
Felix Streckenbach
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Big Data ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Clinical Applications ◽  
Radiological Imaging ◽  
Learning Techniques

AbstractMedical images play an important role in ophthalmology and radiology. Medical image analysis has greatly benefited from the application of “deep learning” techniques in clinical and experimental radiology. Clinical applications and their relevance for radiological imaging in ophthalmology are presented.

scholarly journals A Critical Insight into Pragmatic Manifestation of Diabetic Retinopathy Grading and Detection

2019 ◽  
Vol 9 (2) ◽  
pp. 2040-2049
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Medical Image ◽  
Vision Loss ◽  
Medical Image Analysis ◽  
Research Trend ◽  
Automated Systems ◽  
First Choice

Nowadays, artificial intelligence applications invade all of the fields including medical applications field. Deep learning, a subfield of artificial intelligence, in particular, Convolutional Neural Networks (CNN), have quickly become the first choice for processing and analyzing medical images due to its performance and effectiveness. Diabetic retinopathy is a vision loss disease that infects people with diabetes. This disease damages the blood vessels in the retina, hence, leads to blindness. Due to the sensitivity and complications involved in managing diabetics, designing and developing automated systems to detect and grade diabetic retinopathy is considered one of the recent research areas in the world of medical image applications. In this paper, the aspects of deep learning field related to diabetic retinopathy have been discussed. Various concepts in deep learning including traditional Artificial Neural Network (ANN) algorithm, ANN drawbacks in context of computer vision and image processing applications, and the best algorithm to overcome ANN drawbacks, CNN, have been elucidated along with the architecture. The paper also reviews an extensive summary of some works in the current research trend and future applications of the DL algorithms in medical image analysis for DR detection and grading. Furthermore, various research gabs related to building such automated systems for medical image analysis have been conferred – such as imbalance dataset which is considered one of the main performance issues that should be handled, the need of high performance computational resources to train deep and efficient models and others. This is quite beneficial for researchers working in the domain of medical image analysis to handle DR.

scholarly journals Editorial: Artificial Intelligence for Medical Image Analysis of Neuroimaging Data

2020 ◽  
Vol 14 ◽  
Author(s):  
Nianyin Zeng ◽  
Siyang Zuo ◽  
Guoyan Zheng ◽  
Yangming Ou ◽  
Tong Tong
Keyword(s):  
Artificial Intelligence ◽  
Image Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Neuroimaging Data
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