AIMC Topic: Adult

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Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification.

Scientific reports
To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 inf...

Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments.

Neuron
Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations. However, it is unknown how the brain compactly represents the current state of the environment to guide this process. The deep Q-network ...

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Journal of neurotrauma
Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of patients with TBI-including the decision of whether or not to perform neurosurgery-is critical in op...

Machine-learning-driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis.

Proceedings of the National Academy of Sciences of the United States of America
Contact dermatitis tremendously impacts the quality of life of suffering patients. Currently, diagnostic regimes rely on allergy testing, exposure specification, and follow-up visits; however, distinguishing the clinical phenotype of irritant and all...

Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections.

Scientific reports
Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limi...

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.

BMC medical informatics and decision making
BACKGROUND: The study aimed to introduce a machine learning model that predicts in-hospital mortality in patients on mechanical ventilation (MV) following moderate to severe traumatic brain injury (TBI).

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices.

BMC bioinformatics
BACKGROUND: The aim of a recent research project was the investigation of the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology...

Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology - national survey.

Journal of medical imaging and radiation sciences
BACKGROUND: Artificial Intelligence (AI) is making a continuous progression into the field of Radiation Oncology in Canada and globally. While this field continues to evolve, there is no clear understanding about how radiation oncologists, radiation ...

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

The Lancet. Digital health
BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illnesses driving presentation to hospital. However, viral-specific PCR testing has limited sensitivity and results can take up to 72 h for operational reaso...