Tsuneki Masayuki
Medmain Research, Medmain Inc., Fukuoka, Japan; Division of Anatomy and Cell Biology of the Hard Tissue, Department of Tissue Regeneration and Reconstruction, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan.
J Oral Biosci. 2022 Sep;64(3):312-320. doi: 10.1016/j.job.2022.03.003. Epub 2022 Mar 17.
Deep learning is a state-of-the-art technology that has rapidly become the method of choice for medical image analysis. Its fast and robust object detection, segmentation, tracking, and classification of pathophysiological anatomical structures can support medical practitioners during routine clinical workflow. Thus, deep learning-based applications for diseases diagnosis will empower physicians and allow fast decision-making in clinical practice.
Deep learning can be more robust with various features for differentiating classes, provided the training set is large and diverse for analysis. However, sufficient medical images for training sets are not always available from medical institutions, which is one of the major limitations of deep learning in medical image analysis. This review article presents some solutions for this issue and discusses efforts needed to develop robust deep learning-based computer-aided diagnosis applications for better clinical workflow in endoscopy, radiology, pathology, and dentistry.
The introduction of deep learning-based applications will enhance the traditional role of medical practitioners in ensuring accurate diagnoses and treatment in terms of precision, reproducibility, and scalability.
深度学习是一种先进技术，已迅速成为医学图像分析的首选方法。它对病理生理解剖结构进行快速且稳健的目标检测、分割、跟踪和分类，可在常规临床工作流程中为医生提供支持。因此，基于深度学习的疾病诊断应用将增强医生能力，并在临床实践中实现快速决策。
如果训练集规模大且多样以供分析，深度学习利用各种特征进行类别区分时会更稳健。然而，医疗机构并非总能提供足够的医学图像用于训练集，这是深度学习在医学图像分析中的主要局限之一。本文综述介绍了针对该问题的一些解决方案，并讨论了开发基于深度学习的稳健计算机辅助诊断应用所需的努力，以便在内镜检查、放射学、病理学和牙科领域实现更好的临床工作流程。
基于深度学习的应用的引入将提升医生在确保准确诊断和治疗方面的传统作用，在精准度、可重复性和可扩展性方面表现更佳。
