儿童支气管管腔相对狭窄、肺部间质发育优于弹性组织、纤毛清除能力弱的生理结构使儿童更易发生肺部感染形成肺炎。人工智能(AI)的发展及其在医学领域的应用正在改变传统的疾病诊断、评估及治疗模式。以深度学习为核心的AI越来越多应用于儿童肺炎的诊断以及病情预后评估，有利于对患儿进行早期诊断、准确评估病情。除新型冠状病毒肺炎与急性呼吸窘迫综合征外，研究者很少关注其他病毒性肺炎、细菌性肺炎、支原体肺炎、真菌性肺炎，同时目前仍存在数据集少、样本量少、算法不完备等问题，且对于区分肺炎类型、亚型给予的关注度不够，有待改进。未来应建立儿童肺部感染的大样本数据集，开展医学生、医务人员对AI的学习，探索AI在儿童肺部感染中的更多价值，以推动其在诊疗相关临床决策时的辅助作用。 Children′s bronchial lumen is relatively narrow， pulmonary interstitial development is superior to elastic tissue， and ciliary clearance is weak， which makes children more prone to pulmonary infection and pneumonia。The development of artificial intelligence (AI) and its application in medicine is changing the traditional disease diagnosis， assessment and treatment。AI with deep learning as the core is increasingly used in the diagnosis and prognosis evaluation of pneumonia in children， which is conducive to the early diagnosis and accurate assessment of the disease。In addition to novel coronavirus pneumonia and acute respiratory distress syndrome， researchers rarely pay attention to other viral pneumonia， bacterial pneumonia， mycoplasmal pneumonia， and fungal pneumonia。Meanwhile， there are still problems， such as small datasets， small sample sizes， incomplete algorithms， and little attention paid to pneumonia types and subtypes。In the future， a large-sample dataset of children′s pulmonary infections should be established， and learning about AI should be promoted among medical students and medical staff， so as to explore the value of AI in children′s pulmonary infection and play its auxiliary role in clinical decision-making related to diagnosis and treatment。
Research progress of artificial intelligence imaging analysis technology in pediatric infectious pneumonia
Children′s bronchial lumen is relatively narrow, pulmonary interstitial development is superior to elastic tissue, and ciliary clearance is weak, which makes children more prone to pulmonary infection and pneumonia.The development of artificial intelligence (AI) and its application in medicine is changing the traditional disease diagnosis, assessment and treatment.AI with deep learning as the core is increasingly used in the diagnosis and prognosis evaluation of pneumonia in children, which is conducive to the early diagnosis and accurate assessment of the disease.In addition to novel coronavirus pneumonia and acute respiratory distress syndrome, researchers rarely pay attention to other viral pneumonia, bacterial pneumonia, mycoplasmal pneumonia, and fungal pneumonia.Meanwhile, there are still problems, such as small datasets, small sample sizes, incomplete algorithms, and little attention paid to pneumonia types and subtypes.In the future, a large-sample dataset of children′s pulmonary infections should be established, and learning about AI should be promoted among medical students and medical staff, so as to explore the value of AI in children′s pulmonary infection and play its auxiliary role in clinical decision-making related to diagnosis and treatment.
万方数据
