Abstract Over the years, Machine Learning models have been successfully employed on
neuroimaging data for accurately predicting brain age. Deviations from the healthy brain …

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical
trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance …

Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector
surveillance is needed. Survival of Anopheles mosquitoes is key for epidemiological …

Deep learning techniques have demonstrated great potential for accurately estimating brain
age by analyzing Magnetic Resonance Imaging (MRI) data from healthy individuals …

Deep neural networks have shown promise in predicting the chronological age of a healthy
brain using T1-weighted magnetic resonance images (T1 MRIs). This predicted brain age …

Brain age, most commonly inferred from T1-weighted magnetic resonance images (T1w
MRI), is a robust biomarker of brain health and related diseases. Superior accuracy in brain …

Developing low-carbon agriculture and alleviating the “carbon crisis” requires optimizing
strategies that fully leverage the carbon sink function of paddy systems. Accurate …

The brain presents age-related structural and functional changes in the human life, with
different extends between subjects and groups. Brain age prediction can be used to …

Brain age estimation involves predicting an individual's biological age from their brain
images. This process offers valuable insights into the aging process and the progression of …

The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance
image (MRI) into an effective “brain age” metric can provide a holistic, individualized, and …