Thickening of the corpus callosum is an important feature of development, whereas thinning of the corpus callosum can be the result of a number of diseases that affect development or cause destruction of the corpus callosum. Corpus callosum thickness reflects the volume of the hemispheres and responds to changes through direct effects or through Wallerian degeneration. It is therefore not only important to evaluate the morphology of the corpus callosum for congenital anomalies but also to evaluate the thickness of specific components or the whole corpus callosum in association with other findings. The goal of this pictorial review is raise awareness that the thickness of the corpus callosum can be a useful feature of pathology in pediatric central nervous system disease and must be considered in the context of the stage of development of a child. Thinning of the corpus callosum can be primary or secondary, and generalized or focal. Primary thinning is caused by abnormal or failed myelination related to the hypomyelinating leukoencephalopathies, metabolic disorders affecting white matter, and microcephaly. Secondary thinning of the corpus callosum can be caused by diffuse injury such as hypoxic-ischemic encephalopathy, human immunodeficiency virus (HIV) encephalopathy, hydrocephalus, dysmyelinating conditions and demyelinating conditions. Focal disturbance of formation or focal injury also causes localized thinning, e.g., callosal dysgenesis, metabolic disorders with localized effects, hypoglycemia, white matter injury of prematurity, HIV-related atrophy, infarction and vasculitis, trauma and toxins. The corpus callosum might be too thick because of a primary disorder in which the corpus callosum finding is essential to diagnosis; abnormal thickening can also be secondary to inflammation, infection and trauma.

Heightened clinicopathological awareness of the expanding anatomical distribution of myopericytoma is critical to its diagnosis when it presents in unusual and novel locations. Myopericytoma should be added to the range of external auditory canal neoplasms, especially those characterised by an admixture

A conceptual artificial intelligence (AI)-enabled framework is presented in this study involving triangulation of various diagnostic methods for management of coronavirus disease 2019 (COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AI-enabled framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based molecular diagnostics, radiological image-based assessments, and end-user provided information for the detection of COVID-19 cases and management of symptomatic patients. It will support self-data capture, clinical risk stratification, explanation-based intelligent recommendations for patient triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable communication with end-users in local languages through cheap and accessible means, such as WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19 cases and general support for pandemic recovery. We intend to test the feasibility of implementing the proposed framework through community engagement in sub-Saharan African (SSA) countries where many people are living with pre-existing comorbidities. A multimodal approach to disease diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential services across the continuum of COVID-19 care.

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