Machine Learning for Dental Image Analysis

The field of pathology diagnosis has steadily advanced with the development of microscopy, accompanied by the automation of the reduction of inter-observer reliability and intra-observer reproducibility. Within the field of mammography, computer vision, and artificial intelligence (AI) techniques have been successfully applied to detect and characterize abnormalities of medical images [Winsberg et al., 1967; Ravdin et al., 2001]. This has resulted in a situation such that automated detection techniques can now implement an entire medical procedure with a high degree of accuracy. In addition, advances in computer hardware and software have increased the performance and reliability of parallel computing. The advances in this technology have, in turn, provided hardware and software advancements that are sufficiently robust to support the large computational requirements of complex Artificial Intelligence (AI) algorithms and their application to machine learning.