The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.

The advent of three-dimensional printing (3DP) technology has enabled the creation of a tangible and complex 3D object that goes beyond a simple 3D-shaded visualization on a flat monitor. Since the early 2000s, 3DP machines have been used only in hard tissue applications. Recently developed multi-materials for 3DP have been used extensively for a variety of medical applications, such as personalized surgical planning and guidance, customized implants, biomedical research, and preclinical education. In this review article, we discuss the 3D reconstruction process, touching on medical imaging, and various 3DP systems applicable to medicine. In addition, the 3DP medical applications using multi-materials are introduced, as well as our recent results.