AIMC Topic: Humans

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A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.

Journal of medical Internet research
BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system...

Evaluating eligibility criteria of oncology trials using real-world data and AI.

Nature
There is a growing focus on making clinical trials more inclusive but the design of trial eligibility criteria remains challenging. Here we systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes ...

Combining natural language processing and metabarcoding to reveal pathogen-environment associations.

PLoS neglected tropical diseases
Cryptococcus neoformans is responsible for life-threatening infections that primarily affect immunocompromised individuals and has an estimated worldwide burden of 220,000 new cases each year-with 180,000 resulting deaths-mostly in sub-Saharan Africa...

A Framework for Automatic Burn Image Segmentation and Burn Depth Diagnosis Using Deep Learning.

Computational and mathematical methods in medicine
Burn is a common traumatic disease with high morbidity and mortality. The treatment of burns requires accurate and reliable diagnosis of burn wounds and burn depth, which can save lives in some cases. However, due to the complexity of burn wounds, th...

Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics
Epidemiological sleep research strives to identify the interactions and causal mechanisms by which sleep affects human health, and to design intervention strategies for improving sleep throughout the lifespan. These goals can be advanced by further f...

Learning curve of robot-assisted transabdominal preperitoneal (rTAPP) inguinal hernia repair: a cumulative sum (CUSUM) analysis.

Surgical endoscopy
BACKGROUND: Robot-assisted transabdominal preperitoneal inguinal hernia repair (rTAPP-IHR) is a safe and feasible approach for hernias of varying etiology. We aim to present a single surgeon's learning curve (LC) of this technique based on operative ...

Deep learning-based automatic surgical step recognition in intraoperative videos for transanal total mesorectal excision.

Surgical endoscopy
BACKGROUND: Dividing a surgical procedure into a sequence of identifiable and meaningful steps facilitates intraoperative video data acquisition and storage. These efforts are especially valuable for technically challenging procedures that require in...

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks.

Current eye research
: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity.: After cross-validation of three glaucoma specialists, the final dataset comprised of 3,460 fundus photograp...

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

The Milbank quarterly
Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractic...

Deep-learning approach in the study of skin lesions.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Medical technology is far from reaching its full potential. An area that is currently expanding is that of precision medicine. The aim of this article is to present an application of precision medicine-a deep-learning approach to computer...