机器学习在辅助检测神经退行性疾病中的研究进展

Research Progress of Machine Learning in Assistant Detection of Neurodegenerative Diseases

人口老龄化问题日益加重,神经退行性疾病的发病率逐年递增,已成为重要的全球医疗、公共卫生和社会问题。然而,神经退行性疾病的病灶识别、分类、分级是神经外科医师进行诊断和治疗的关键。近年来,人工智能和医学影像技术的快速发展为其提供了良好的助力,明显规避了传统人工阅片的主观性问题。本文针对影像组学的具体步骤,对两类有代表性的神经退行性疾病研究中机器学习和深度学习的应用成果进行综述,最后总结机器学习所面临的挑战并展望未来研究方向,为后续“医”“工”深入结合辅助神经退行性疾病的相关治疗提供依据。

The aging of the population is becoming more and more serious,and the incidence of neurodegenerative diseases is increasing year by year,which has become an important global medical,public health and social problem.However,the identification,classification and grading of neurodegenerative diseases are the key to the diagnosis and treatment of neurosurgeons.In recent years,the rapid development of artificial intelligence and medical imaging technology has provided a good auxiliary role for it,which has greatly improved the subjectivity of traditional manual film reading.Aiming at the concrete steps of radiomic,the application results of machine learning and deep learning in two representative neurodegenerative diseases were summarized.The last part summarized the challenges faced by machine learning and looking forward to the future research direction.It provides a basis for the further combination of“clinic”and“engineering”in the treatment of auxiliary neurodegenerative diseases.