AIMC Topic: Adult

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How much are we exposed to alcohol in electronic media? Development of the Alcoholic Beverage Identification Deep Learning Algorithm (ABIDLA).

Drug and alcohol dependence
BACKGROUND: Evidence demonstrates that seeing alcoholic beverages in electronic media increases alcohol initiation and frequent and excessive drinking, particularly among young people. To efficiently assess this exposure, the aim was to develop the A...

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning.

AJR. American journal of roentgenology
This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. This retrospective study included 1807 image sets from 168 pathological...

Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images.

Communications biology
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachmen...

Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data.

Transplantation proceedings
Prediction models of post-liver transplant mortality are crucial so that donor organs are not allocated to recipients with unreasonably high probabilities of mortality. Machine learning algorithms, particularly deep neural networks (DNNs), can often ...

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

Heart rhythm
BACKGROUND: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.

Gait characteristics and clinical relevance of hereditary spinocerebellar ataxia on deep learning.

Artificial intelligence in medicine
BACKGROUND: Deep learning has always been at the forefront of scientific research. It has also been applied to medical research. Hereditary spinocerebellar ataxia (SCA) is characterized by gait abnormalities and is usually evaluated semi-quantitative...

Assessing various sensorimotor and cognitive functions in people with epilepsy is feasible with robotics.

Epilepsy & behavior : E&B
BACKGROUND: Epilepsy is a common neurological disorder characterized by recurrent seizures, along with comorbid cognitive and psychosocial impairment. Current gold standards of assessment can quantify cognitive and motor performance, but may not capt...

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

Journal of healthcare engineering
Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human ...

Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

BMJ open
OBJECTIVE: We aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. ...