Objective To establish a deep learning model for the detection of hypoxic–ischemic
encephalopathy (HIE) features on CT scans and to compare various networks to determine …

Background Establishing whether a patient who survived a cardiac arrest has suffered
hypoxic-ischemic brain injury (HIBI) shortly after return of spontaneous circulation (ROSC) …

Introduction: Heterogeneous MRI manifestations restrict the efficiency and consistency of
neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex …

Introduction Guidelines recommend use of computerized tomography (CT) and
electroencephalography (EEG) in post-arrest prognostication. Strong associations between …

Abstract Background/Aim Signs of hypoxic ischaemic encephalopathy (HIE) on head
computed tomography (CT) predicts poor neurological outcome after cardiac arrest. We …

With great interest, we have read the article by Mansour et al.[1], reporting on the use of
deep transfer learning to identify early signs of hypoxic-ischemic brain injury (HIBI) on head …

Predicting poor neurological outcomes after resuscitation is important for planning treatment
strategies. We constructed an explainable artificial intelligence-based prognostic model …

Objective: Most cardiac arrest patients who are successfully resuscitated are initially
comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) …

Outcome prognostication in comatose patients after cardiac arrest (CA) remains to date a
challenge. The major determinant of clinical outcome is the post-hypoxic/ischemic …

Background and Objectives In light of limited intensive care capacities and a lack of accurate
prognostic tools to advise caregivers and family members responsibly, this study aims to …