A cross-attention-based deep learning approach for predicting functional stroke outcomes using 4D CTP imaging and clinical metadata.

Acute ischemic stroke (AIS) remains a global health challenge, leading to long-term functional disabilities without timely intervention. Spatio-temporal (4D) Computed Tomography Perfusion (CTP) imaging is crucial for diagnosing and treating AIS due to its ability to rapidly assess the ischemic core and penumbra. Although traditionally used to assess acute tissue status in clinical settings, 4D CTP has also been explored in research for predicting stroke tissue outcomes.

TRANscranial direct current stimulation for FOcal Refractory epilepsy in mitochondrial disease (TRANSFORM): delayed-start, randomised, double-blinded, placebo-controlled study.

Background: Focal epilepsy is common in children and adults with mitochondrial disease. Seizures are often refractory to pharmacological treatment and, in this patient group, frequently evolve to refractory focal status epilepticus (also known as epilepsia partialis continua). Where this occurs, the long-term prognosis is poor.

Digitizing Survivorship Care Plans Through the POST-Treatment Health Outcomes of Cancer Survivors (POSTHOC) Mobile App: Protocol for a Phase II Randomized Controlled Trial.

Background: Survivorship care plans (SCPs) are provided at the completion of cancer treatment to aid in the transition from active treatment to long-term survivorship. They describe the details of a patient's diagnosis and treatment and offer recommendations for follow-up appointments, referrals, and healthy behaviors. The plans are currently paper-based and become outdated as soon as a patient's health status changes.

Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

Objective: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models.

EEG and fNIRS are associated with situation awareness (hazard) prediction during a driving task.

Situation awareness (SA) is important in many demanding tasks (e.g. driving).