Virtual imaging trials in medicine: A brief takeaway of the lessons from the first international summit.

Background: The rapid advancement of medical technologies presents significant challenges for researchers and practitioners. While traditional clinical trials remain the gold standard, they are often limited by high costs, lengthy durations, and ethical constraints. In contrast, in-silico trials and digital twins have emerged not only as efficient and ethical alternatives but also as a complementary technology that can extend beyond classical trials to predict and design new strategies.

Off-label in-silico flow diverter performance assessment in posterior communicating artery aneurysms.

Background: The posterior communicating artery (PComA) is among the most common intracranial aneurysm locations, but flow diverter (FD) treatment with the widely used pipeline embolization device (PED) remains an off-label treatment that is not well understood. PComA aneurysm flow diversion is complicated by the presence of fetal posterior circulation (FPC), which has an estimated prevalence of 4-29% and is more common in people of black (11.5%) than white (4.

SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI.

Intravoxel incoherent motion (IVIM) imaging is increasingly recognised as an important tool in clinical MRI, where tissue perfusion and diffusion information can aid disease diagnosis, monitoring of patient recovery, and treatment outcome assessment. Currently, the discovery of biomarkers based on IVIM imaging, similar to other medical imaging modalities, is dependent on long preclinical and clinical validation pathways to link observable markers derived from images with the underlying pathophysiological mechanisms. To speed up this process, virtual IVIM imaging is proposed.

Early detection of dementia through retinal imaging and trustworthy AI.

Alzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers.