Results 171 to 180 of about 133,461 (264)

Nanophotonic Biosensors and the Paradigm of Trans‐Scale Autonomous Bio‐Integrated Diagnostics for Addressing Health Complexities

Electron, EarlyView.
Physics‐driven advances in optical nanobiosensors for rapid, miniaturized, and point‐of‐care diagnostics for next‐generation decentralized and personalized healthcare based on sensor intelligence. ABSTRACT Public health emergencies and the escalating burden of chronic diseases necessitate a paradigm shift from centralized laboratory testing to rapid ...
Vishal Chaudhary, Harsh Sable, Sonu Sonu, Lakshita Phor, Reddicherla Umapathi, Pradeep Bhadola   +5 more
wiley   +1 more source

Advanced machine learning-based screening for primary aldosteronism with plasma steroids, potassium, and renin. [PDF]

NPJ Digit Med
Zhang W, Pamporaki C, Jäkel R, Constantinescu G, Peitzsch M, Schulze M, Yang J, Horvath AR, Müller-Pfefferkorn R, Williams TA, Reincke M, Beuschlein F, Fuss CT, Hahner S, Eisenhofer G.   +14 more
europepmc   +1 more source

Can epilepsy be predicted after the first febrile seizure? Insights from machine learning of postictal EEG

Epileptic Disorders, EarlyView.
Abstract Objective Febrile seizures (FS) are the most common seizures in childhood, yet identifying children at risk of developing epilepsy after the first FS remains challenging. We aimed to evaluate the prognostic potential of machine learning (ML) algorithms applied to post‐febrile seizure electroencephalography (EEG) recordings.
Boran Şekeroğlu, Hüseyin Öztoprak, Özlem Yayıcı Köken, Hatice Demir, Mehpare Sarı Yanartaş, Deniz Yılmaz, Burçin Şanlıdağ, Ayşegül Neşe Çıtak Kurt   +7 more
wiley   +1 more source

Discovery of a preliminary urinary metabolite panel for Parkinson's disease: a pilot study using paired patient-spouse samples and machine learning consensus. [PDF]

Front Neurol
Chen QQ, Gou DH, Huang JY, Mo ZH, Guan XY, Xu JN.   +5 more
europepmc   +1 more source

Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta‐analysis of four clinical applications

Epilepsia, EarlyView.
Abstract Objective The application of artificial intelligence/machine learning (AI/ML) to magnetic resonance imaging (MRI) promises to enhance and support clinical decision‐making in epilepsy. However, there currently lacks an appropriate assessment of clinical utility and study rigor of current AI/ML‐driven models that are targeted toward supporting ...
Judy Chen, Ella Sahlas, Yigu Zhou, Natalie Chen, Jim Xie, Farhan Wadia, Lorenzo Caciagli, Aristides Hadjinicolaou, Alexander G. Weil, Roy W. Dudley, Dewi V. Schrader, Andrea Bernasconi, Neda Bernasconi, Boris C. Bernhardt   +13 more
wiley   +1 more source

Development and validation of a machine learning-based risk prediction model for non-suicidal self-injury in adolescents. [PDF]

Front Psychiatry
Zhao Y, Wang Q, Liu W.
europepmc   +1 more source

Effects of fenfluramine and sigma‐1‐dependent pharmacological and genetic modulation in a mouse kindling model

Epilepsia, EarlyView.
Abstract Objective Sigma‐1 is a chaperone protein that serves as a key homeostatic regulator, implicated in neuronal excitability and seizure control. Positive allosteric modulators offer a use‐dependent means to enhance Sigma‐1 activity, potentially with favorable tolerability compared to direct agonists.
Eva‐Lotta von Rüden, Verena Buchecker, Amelie Wagner, Victoria M. Stocker, Liga Zvejniece, Heidrun Potschka   +5 more
wiley   +1 more source

Artificial intelligence approaches for schizophrenia prediction and its biomarkers using medical imaging data. [PDF]

Front Psychiatry
Palpandi SB, Palanigurupackiam N, Almatar H, Alduhayan R, Alsomaie B, Almazroa A.   +5 more
europepmc   +1 more source

AI‐based localization of the epileptogenic zone using intracranial EEG

Epilepsia Open, EarlyView.
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida, Yipeng Zhang, Sotaro Kanai, Richard Staba, Vwani Roychowdhury, Hiroki Nariai   +5 more
wiley   +1 more source