Publications by authors named "Rohan Dhanakshirur"

Introduction: Moyamoya disease (MMD) is characterised by supraclinoid internal carotid artery (ICA) narrowing causing cerebral parenchyma to starve. Direct and indirect revascularisation techniques are the treatment norm. We provide a clinicoradiological comparison of single and double barrel superficial temporal artery to middle cerebral artery (STA-MCA) bypass for MMD.

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Background: Moyamoya disease (MMD) is a rare cerebrovascular disease characterized by progressive stenosis of the supraclinoid internal carotid artery. As a result of chronically decreased brain perfusion, eloquent areas of the brain become hypoperfused, leading to cognitive changes in patients. Repeated infarcts and bleeds produce clinically apparent neurologic deficits.

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Article Synopsis
  • The study aimed to enhance deep learning techniques for detecting isodense or obscure masses in dense breast tissue by incorporating core radiology principles into model development.
  • Using a multi-centre approach, the researchers trained and validated the deep learning model on both diagnostic and screening mammography datasets, which included thousands of images and confirmed cases of cancer.
  • Results showed improved sensitivity in detecting malignancies, especially in patients with dense breasts and isodense cancers, suggesting that integrating traditional radiology knowledge into deep learning can significantly boost cancer detection performance.
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With the rapid integration of artificial intelligence into medical practice, there has been an exponential increase in the number of scientific papers and industry players offering models designed for various tasks. Understanding these, however, is difficult for a radiologist in practice, given the core mathematical principles and complicated terminology involved. This review aims to elucidate the core mathematical concepts of both machine learning and deep learning models, explaining the various steps and common terminology in common layman language.

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Neurosurgery is a challenging surgical specialty that demands many technical and cognitive skills. The traditional surgical training approach of having a trainee coached in the operating room by the faculty is time-consuming, costly, and involves patient risk factors. Simulation-based training methods are suitable to impart the surgical skills outside the operating room.

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