近日,幸运快3网站登录郑强老师及其合作者的论文“Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs”在影像学领域国际顶级期刊Radiology发表(SCI一区,IF=7.608)。该研究通过人工智能算法,帮助实现影像科常规X线平片图像儿童双下肢不等长检查过程中的自动快速测量,比具有二十多年经验的儿科影像科医生的手动测量速度快将近100倍,能极大提高影像科医生的工作效率,并使他们更多关注于肢体异常的影像学表现,具有重要的产业化价值。
Radiology期刊副主编,来自阿姆斯特丹大学的Rick van Rijn教授,在Radiology期刊发表评论文章“Three Reasons Why Artificial Intelligence Might Be the Radiologist’s Best Friend”,对于郑强老师开展的工作给予极大肯定,并指出AI can free up a lot of time for radiologists to spend on value-adding duties that only humans can perform。
该研究是幸运快3网站登录郑强老师在美国宾夕法尼亚大学和费城儿童医院进修期间取得的研究成果。论文在正式发表后,被国外HealthImaging、AuntMinnie、Doctor Penguin等多家媒体报道。
(1) HealthImaging媒体报道:https://www.healthimaging.com/topics/artificial-intelligence/algorithm-measures-childrens-leg-1-second
(2) AuntMinnie媒体报道:https://www.auntminnie.com/index.aspx?sec=log&itemID=128802
(3) Doctor Penguin媒体报道: http://doctorpenguin.com/categories
Qiang Zheng, Sphoorti Shellikeri, Hao Huang, Misun Hwang, Raymond W. Sze*, “Deep Learning Measurement of Leg Length Discrepancy in Children Based on Radiographs”, Radiology, 2020.