Su Che-Yu, Tsai Tsung-Yu, Tseng Cheng-Yen, Liu Keng-Hao, Lee Chi-Wei
Department of Emergency Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.
J Clin Med. 2021 Jan 12;10(2):254. doi: 10.3390/jcm10020254.
Hollow organ perforation can precipitate a life-threatening emergency due to peritonitis followed by fulminant sepsis and fatal circulatory collapse. Pneumoperitoneum is typically detected as subphrenic free air on frontal chest X-ray images; however, treatment is reliant on accurate interpretation of radiographs in a timely manner. Unfortunately, it is not uncommon to have misdiagnoses made by emergency physicians who have insufficient experience or who are too busy and overloaded by multitasking. It is essential to develop an automated method for reviewing frontal chest X-ray images to alert emergency physicians in a timely manner about the life-threatening condition of hollow organ perforation that mandates an immediate second look. In this study, a deep learning-based approach making use of convolutional neural networks for the detection of subphrenic free air is proposed. A total of 667 chest X-ray images were collected at a local hospital, where 587 images (positive/negative: 267/400) were used for training and 80 images (40/40) for testing. This method achieved 0.875, 0.825, and 0.889 in sensitivity, specificity, and AUC score, respectively. It may provide a sensitive adjunctive screening tool to detect pneumoperitoneum on images read by emergency physicians who have insufficient clinical experience or who are too busy and overloaded by multitasking.
中空器官穿孔可因腹膜炎引发危及生命的紧急情况，随后导致暴发性脓毒症和致命的循环衰竭。气腹通常在胸部正位X线图像上表现为膈下游离气体；然而，治疗依赖于及时对X线片进行准确解读。不幸的是，经验不足或因任务过多而过于忙碌、负担过重的急诊医生做出误诊的情况并不少见。开发一种自动审查胸部正位X线图像的方法至关重要，以便及时提醒急诊医生注意中空器官穿孔这一危及生命的状况，这需要立即进行再次检查。在本研究中，提出了一种基于深度学习的方法，利用卷积神经网络检测膈下游离气体。在当地一家医院共收集了667张胸部X线图像，其中587张图像（阳性/阴性：267/400）用于训练，80张图像（40/40）用于测试。该方法的灵敏度、特异度和AUC评分分别达到0.875、0.825和0.889。它可能为临床经验不足或因任务过多而过于忙碌、负担过重的急诊医生在解读图像时检测气腹提供一种敏感的辅助筛查工具。
