Jinhong Zhao, Bing-Long Wang, Xiaoping Qin, Yuanli Liu, Tingfang Liu
Core Elements of Excellent Hospital Leadership: Lessons from the 5 Top-performing Hospitals in China
International journal for quality in health care : journal of the International Society for Quality in Health Care
eus crucial diagnostic therapeutic method many anatomical regions especially evaluation mediastinal diseases related pathologies rapidly finding standard stations key achieving efficient complete mediastinal eus imaging however requires substantial technical skills extensive knowledge mediastinal anatomy constructed system named eusmps eusmediastinal position system realtime mediastinal eus station recognition standard scanning mediastinum eus divided 7 stations 33 010 images mediastinum eus examination collected construct station classification model used 151 videos clips video validation used 1212 eus images 2 hospitals external validation independent data set containing 230 eus images applied manmachine contest conducted crossover study evaluate effectiveness system reducing difficulty mediastinal ultrasound image interpretation station classification model achieved accuracy 9049 image validation 8380 video validation external validation models achieved 8985 accuracy manmachine contest model achieved accuracy 8478 comparable expert 8391 accuracy trainees station recognition significantly improved crossover study increase 1326 95 confidence interval 11041548 p 005 deep learningbased system shows great performance mediastinum station localization potential play important role shortening learning curve establishing standard mediastinal scanning future