Advanced diffusion weighted (DW) MRI of the brain in the fatal outbreak of Nipah viral encephalitis among pig workers in Malaysia and Singapore revealed a pattern similar to ischaemic infarction caused by obstruction of small cerebral blood vessels. However, relapse and late-onset cases in Malaysia, and other outbreaks of Nipah virus in Bangladesh and the Hendra virus infection in Australia, showed a different MRI pattern of predominantly confluent cortical lesions. MRI was useful in characterizing the disease in acute infection, as well as detection of spine abnormalities and subclinical infection.

Reeent advances in magnetic resonance imaging (MRI), in particular diffusion weighted imaging (DWI) and perfusion weighted imaging (PWI), have allowed clinicians to have the ability to differentiate between irreversible cerebral infarction and the potentially reversible ischemic penumbra. This article examines the principles and practice of DWI and PWI. With continued advances in thrombolysis and other therapy for acute cerebral ischemia, neuroimaging is poised to play an increasingly important role in decisionmaking in aeute stroke.

Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression of the discipline toward a vision of how clinical medicine, empowered by technology, can achieve our national healthcare objectives of delivering value-based and patient-centric care. In this article, we consider 3 core questions relating to AI in radiology, and review the barriers to the widespread adoption of AI in radiology. We propose solutions and describe a "Centaur" model as a promising avenue for enabling the interfacing between AI and radiologists. Finally, we introduce The Radiological AI, Data Science and Imaging Informatics (RADII) subsection of the Singapore Radiological Society. RADII is an enabling body, which together with key technological and institutional stakeholders, will champion research, development and evaluation of AI for radiology applications.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Neural Networks (Computer) ; Radiology ; Singapore ; Societies, Medical