BioMedical Engineering OnLine

Deep Learning in medicine is one of the most rapidly and new developing fields of science. Currently, almost every medical device intended for imaging has a more or less extended image and signal  analysis and processing module which can use deep learning. It provides quantitative data necessary to make a diagnosis with predicting diagnosis. The obtained quantitative features must be independent of the inter-subject variability and the type of medical device and, above all, must allow for reproducible results in the presence of high noise. The proposed deep learning  algorithms should also ensure the independence of the results obtained by the operator of the imaging device and, to be more exact, its position relative to the patient or the parameter settings in the device. In addition, the proposed deep learning  algorithms must be tailored for the diagnosis of a specific disease entity. On the other hand, they must allow for reproducible results for high inter-subject variability. These criteria make it difficult to propose a methodology for the deep learning algorithms. This special issue is dedicated to this area of knowledge.

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Medical robotics in the last few decades has achieved rapid development and has been widely applied in surgical field. The types of medical robotics include neurosurgery robotics, orthopedics robotics, laparoscopic robotics, vascular interventional robotics, prosthetics and exoskeleton robotics, assistive and rehabilitation robotics and capsule robotics. The robotics are important for surgery. Key technological research in medical robotics and automation is now very essential.

Guest edited by Prof Dhanjoo N. Ghista and Prof Kelvin K.L. Wong

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