Sa1707 Artificial Intelligence for Endocytoscopy Provides Fully Automated Diagnosis of Histological Remission in Ulcerativ E.Coli Tis

2017 ◽  
Vol 85 (5) ◽  
pp. AB248
Author(s):  
Maeda Yasuharu ◽  
Shinei Kudo ◽  
Yuichi Mori ◽  
Masashi Misawa ◽  
Kunihiko Wakamura ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Automated Diagnosis ◽  
Histological Remission

Epilogue

2018 ◽  
pp. 261-264
Author(s):  
Ingmar Weber
Keyword(s):  
Artificial Intelligence ◽  
Augmented Reality ◽  
Good Reason ◽  
Medical Doctors ◽  
Automated Diagnosis ◽  
Profound Impact ◽  
Customer Support ◽  
The Past ◽  
The Future ◽  
Digital Landscape

Changes in the global digital landscape over the past decade or so have transformed many aspects of society, including how people communicate, socialize, and organize. These transformations have also reconfigured how companies conduct their businesses and altered how states think about security and interact with their citizens. Glancing into the future, there is good reason to believe that nascent technologies such as augmented reality will continue to change how people connect, blurring the lines between our online and offline worlds. Recent breakthroughs in the field of artificial intelligence will also have a profound impact on many aspects of our lives, ranging from the mundane—chat bots as convenient, always available customer support—to the disruptive—replacing medical doctors with automated diagnosis tools....

Automated diagnosis of prostate cancer location by artificial intelligence in multiparametric MRI

2018 ◽  
Vol 17 (2) ◽  
pp. e888-e889 ◽  
Author(s):  
Y. Oishi ◽  
T. Kitta ◽  
N. Shinohara ◽  
H. Nosato ◽  
H. Sakanashi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Multiparametric Mri ◽  
Automated Diagnosis

Artificial Intelligence Analysis of Bio-signals for Automated Detection and Automated Diagnosis of ADHD and CD

교정담론 ◽  
2021 ◽  
Vol 15 (3) ◽  
pp. 87-118
Author(s):  
Chang-Hyun Ryu ◽  
◽  
Young-Sup Hwang
Keyword(s):  
Artificial Intelligence ◽  
Automated Detection ◽  
Intelligence Analysis ◽  
Automated Diagnosis

scholarly journals Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1078
Author(s):  
Stefan L. Popa ◽  
Abdulrahman Ismaiel ◽  
Pop Cristina ◽  
Mogosan Cristina ◽  
Giuseppe Chiarioni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Liver Disease ◽  
Electronic Health Records ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Digital Pathology ◽  
Automated Diagnosis ◽  
Health Records ◽  
Alcoholic Fatty Liver ◽  
Alcoholic Fatty Liver Disease

Background: Non-alcoholic fatty liver disease (NAFLD) is a fast-growing pathology around the world, being considered the most common chronic liver disease. It is diagnosed based on the presence of steatosis in more than 5% of hepatocytes without significant alcohol consumption. This review aims to provide a comprehensive overview of current studies of artificial intelligence (AI) applications that may help physicians in implementing a complete automated NAFLD diagnosis and staging. Methods: PubMed, EMBASE, Cochrane Library, and WILEY databases were screened for relevant publications in relation to AI applications in NAFLD. The search terms included: (non-alcoholic fatty liver disease OR NAFLD) AND (artificial intelligence OR machine learning OR neural networks OR deep learning OR automated diagnosis OR computer-aided diagnosis OR digital pathology OR automated ultrasound OR automated computer tomography OR automated magnetic imaging OR electronic health records). Results: Our search identified 37 articles about automated NAFLD diagnosis, out of which 15 articles analyzed imagistic techniques, 15 articles analyzed digital pathology, and 7 articles analyzed electronic health records (EHC). All studies included in this review show an accurate capacity of automated diagnosis and staging in NAFLD using AI-based software. Conclusions: We found significant evidence demonstrating that implementing a complete automated system for NAFLD diagnosis, staging, and risk stratification is currently possible, considering the accuracy, sensibility, and specificity of available AI-based tools.

An ensemble artificial intelligence ‐enabled MIoT for automated diagnosis of malaria parasite

2021 ◽  
Author(s):  
Soumya Ranjan Nayak ◽  
Janmenjoy Nayak ◽  
S. Vimal ◽  
Vaibhav Arora ◽  
Utkarsh Sinha
Keyword(s):  
Artificial Intelligence ◽  
Malaria Parasite ◽  
Automated Diagnosis

scholarly journals Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence

2020 ◽  
Vol 40 (12) ◽  
pp. 3117-3124
Author(s):  
Silvia Giordano ◽  
Sen Takeda ◽  
Matteo Donadon ◽  
Hidekazu Saiki ◽  
Laura Brunelli ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mass Spectrometry ◽  
Automated Diagnosis ◽  
Hepatic Tumour

scholarly journals Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders

2019 ◽  
Vol 82 (1-3) ◽  
pp. 41-64 ◽  
Author(s):  
U. Raghavendra ◽  
U. Rajendra Acharya ◽  
Hojjat Adeli
Keyword(s):  
Artificial Intelligence ◽  
Signal Processing ◽  
Neurological Disorders ◽  
Extraction Methods ◽  
Physiological Signals ◽  
Machine Learning Techniques ◽  
Automated Diagnosis ◽  
Cad System ◽  
Reduction Techniques ◽  
Processing Techniques

Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions. Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson’s disease, Alzheimer’s disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles on different feature extraction methods, dimensionality reduction techniques, feature selection, and classification techniques are reviewed. Key Message: CAD systems using AI and advanced signal processing techniques can assist clinicians in analyzing and interpreting physiological signals and images more effectively.

scholarly journals On modern methods of automated diagnosis of skin tumors in clinical practice

2020 ◽  
pp. 76-78
Author(s):  
V. Yu. Sergeev ◽  
Yu. Yu. Sergeev ◽  
O. B. Tamrazova ◽  
V. G. Nikitaev ◽  
A. N. Pronichev
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Public Health Service ◽  
Automated Diagnosis ◽  
Skin Cancers ◽  
Modern Methods ◽  
Number Of Publications ◽  
New Algorithms ◽  
Artificial Intelligence Systems

Despite the existence of many algorithms for automated diagnosis of melanoma and other skin cancers, these remain almost inaccessible to public health service. A small number of publications on the efficacy of existing artificial intelligence systems marks the problems of their implementation into current examination routines in dermatology and oncology. New algorithms and software solutions as well as studies demonstrating their diagnostic accuracy on compatible and verifiable clinical material are still in demand.

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