Latest AI and machine learning research in autism for healthcare professionals.
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and ep...
Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed o...
To explore the minds of others, which is traditionally referred to as Theory of Mind (ToM), is perhaps the most fundamental ability of humans as socia...
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unif...
UNLABELLED: Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood...
This study focused on the development and initial psychometric evaluation of a set of online, webcam-collected, and artificial intelligence-derived pa...
Facial analysis technology in rare diseases has the potential to shorten the diagnostic odyssey by providing physicians with a valuable diagnostic too...
Deformable medical image registration is an essential preprocess step for several clinical applications. Even though the existing convolutional neural...
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human ...
BACKGROUND: It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imagin...
Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic vari...
The genetic etiology of brain disorders is highly heterogeneous, characterized by abnormalities in the development of the central nervous system that ...
Early disease detection and prevention methods based on effective interventions are gaining attention worldwide. Progress in precision medicine has re...
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and thei...
Genomic data and machine learning approaches have gained interest due to their potential to identify adaptive genetic variation across populations and...
This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic...
BACKGROUND: The diagnosis of rare genetic diseases is often challenging due to the complexity of the genetic underpinnings of these conditions and the...
BACKGROUND: Autism spectrum disorders (ASD) are a group of neurodevelopmental disorders characterized by difficulty communicating with society and oth...
The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise p...
The design of a metasurface array consisting of different unit cells with the objective of minimizing its radar cross-section is a popular research to...