[Medical Application of Artificial Intelligence/Deep Learning].

Deep learning is a subset of the medical application of artificial intelligence. Its significant results are garnering attention, particularly in radiographic image interpretation, pathological diagnosis, gene analysis, and prediction of cancer recurrence. In this study, we summarize the concept of deep learning. The human body structure, from the molecule to physical functions, is a complex system. Deep learning is a new way to analyze its complex systems. An essential point of the analysis is the categorization of obstacles. To a certain extent, deep learning approximates a doctor's cognition.

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.