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

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....

scholarly journals Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Cécile Nabet ◽  
Aurélien Chaline ◽  
Jean-François Franetich ◽  
Jean-Yves Brossas ◽  
Noémie Shahmirian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mass Spectrometry ◽  
Malaria Transmission ◽  
Maldi Tof Mass Spectrometry ◽  
Anopheles Mosquitoes ◽  
Maldi Tof

scholarly journals Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence

2020 ◽  
Vol 3 (1) ◽  
pp. 61-87 ◽  
Author(s):  
Theodore Alexandrov
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mass Spectrometry ◽  
Image Analysis ◽  
Imaging Mass Spectrometry ◽  
Rapid Evolution ◽  
Life Sciences ◽  
Imaging Data ◽  
Enabling Technology ◽  
Tissue Sections

Spatial metabolomics is an emerging field of omics research that has enabled localizing metabolites, lipids, and drugs in tissue sections, a feat considered impossible just two decades ago. Spatial metabolomics and its enabling technology—imaging mass spectrometry—generate big hyperspectral imaging data that have motivated the development of tailored computational methods at the intersection of computational metabolomics and image analysis. Experimental and computational developments have recently opened doors to applications of spatial metabolomics in life sciences and biomedicine. At the same time, these advances have coincided with a rapid evolution in machine learning, deep learning, and artificial intelligence, which are transforming our everyday life and promise to revolutionize biology and healthcare. Here, we introduce spatial metabolomics through the eyes of a computational scientist, review the outstanding challenges, provide a look into the future, and discuss opportunities granted by the ongoing convergence of human and artificial intelligence.

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

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.

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