Results 111 to 120 of about 35,242 (295)
ABSTRACT Objective To explore how cerebral hypoxia and Normal‐Appearing White Matter (NAWM) integrity affect MS lesion burden and clinical course. Methods Seventy‐nine MS patients, including 13 clinically isolated syndrome (CIS) patients and 66 relapsing–remitting multiple sclerosis (RRMS) patients, and 44 healthy controls (HCs) were recruited from ...
Xinli Wang +8 more
wiley +1 more source
ABSTRACT Objective Intravenous thrombolysis (IVT) before thrombectomy for ischemic stroke may alter clot structure and procedural performance. We investigated how IVT relates to thrombectomy metrics across stroke etiologies. Methods We performed a time‐to‐event analysis of consecutive patients with anterior circulation large vessel occlusion (acLVO ...
Annahita Sedghi +8 more
wiley +1 more source
Added Prognostic Value of EEG Reactivity in Comatose Patients Following Cardiac Arrest
ABSTRACT Objectives To evaluate the added prognostic value of EEG reactivity for favorable outcome compared with background analysis during and after targeted temperature management (TTM). Methods Prospective observational cohort study of comatose post–cardiac arrest patients admitted to a single academic center between 2017 and 2022, all undergoing ...
Sarah Caroyer +11 more
wiley +1 more source
An explainable AI model for power plant NOx emission control
In recent years, developing Artificial Intelligence (AI) models for complex system has become a popular research area. There have been several successful AI models for predicting the Selective Non-Catalytic Reduction (SNCR) system in power plants and ...
Kyprianidis, Konstantinos, +7 more
core +1 more source
Glaucoma is a leading global cause of blindness, making early detection essential. This paper introduces GlaucoXAI (Glaucoma Explainable AI), an advanced computer-aided diagnosis (CAD) model that integrates machine learning and explainable AI for ...
Debendra Muduli +4 more
doaj +1 more source
Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analyzing
Neeraj Anand Sharma +5 more
doaj +1 more source
Screening Routine Clinical Notes for Epilepsy Surgery Candidates Using Large Language Models
ABSTRACT Objective Epilepsy surgery is severely underutilized despite proven efficacy, with substantial under‐referral of eligible patients in routine clinical practice. This study evaluated the potential role of large language models (LLMs) as decision‐support tools for screening unstructured clinical notes to identify epilepsy surgery candidates and ...
Uriel Fennig +9 more
wiley +1 more source
Explainable AI for Mixed Data Clustering
4262Clustering, an unsupervised machine learning approach, aims to find groups of similar instances. Mixed data clustering is of particular interest since real-life data often consists of diverse data types.
Amling, Jonas +4 more
core +1 more source
Explainable AI and Music [PDF]
The field of eXplainable Artificial Intelligence (XAI) has become a hot topic examining how machine learning models such as neural nets and deep learning techniques can be made more understandable to humans.
Reed, Courtney N. +5 more
core +1 more source
A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi +17 more
wiley +1 more source

