Latest AI and machine learning research in urinary incontinence for healthcare professionals.
The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predic...
The Food & Drug Administration (FDA) is considering the permanent exemption of premarket notification requirements for several Class I and II medical device products, including several artificial Intelligence (AI)-driven devices. The exemption is based on the need to rapidly more quickly disseminate devices to the public, estimated cost-savings, a lack of documented adverse events reported to the ...
In the present study, the phenolic content by using LC MS/MS method, anticholinergic, antioxidant (metal reduction, radical and removal of lipid perox...
This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intellig...
OBJECTIVE: To develop and externally validate a prediction model for anticholinergic response in patients with overactive bladder (OAB).
In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more dif...