AIMC Topic: Humans

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Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is as...

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier.

Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially availab...

Endonasal endoscopic transsphenoidal approach robot prototype: A cadaveric trial.

Asian journal of surgery
BACKGROUND: The Endonasal Endoscopic Transsphenoidal Surgery (EETS) is a minimally invasive procedure to approach and remove pituitary tumors and other sellar lesions. The process causes less pain, faster recovery, and provides further minimal invasi...

Machine learning methods accurately predict host specificity of coronaviruses based on spike sequences alone.

Biochemical and biophysical research communications
Coronaviruses infect many animals, including humans, due to interspecies transmission. Three of the known human coronaviruses: MERS, SARS-CoV-1, and SARS-CoV-2, the pathogen for the COVID-19 pandemic, cause severe disease. Improved methods to predict...

Artificial intelligence in COVID-19 drug repurposing.

The Lancet. Digital health
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timel...

Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI.

Neural computation
Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connectio...

Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Neuroimaging clinics of North America
Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogene...

Artificial Intelligence and Stroke Imaging: A West Coast Perspective.

Neuroimaging clinics of North America
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth leading cause of death in the United States. AI applications for stroke imaging are a topic of intens...

Overview of Machine Learning: Part 2: Deep Learning for Medical Image Analysis.

Neuroimaging clinics of North America
Deep learning has contributed to solving complex problems in science and engineering. This article provides the fundamental background required to understand and develop deep learning models for medical imaging applications. The authors review the ma...

Brief History of Artificial Intelligence.

Neuroimaging clinics of North America
This article reviews the history of artificial intelligence and introduces the reader to major events that prompted interest in the field, as well as pitfalls and challenges that have slowed its development. The purpose of this article is to provide ...