Summers Shane M, Chin Eric J, Long Brit J, Grisell Ronald D, Knight John G, Grathwohl Kurt W, Ritter John L, Morgan Jeffrey D, Salinas Jose, Blackbourne Lorne H
Brooke Army Medical Center, Department of Emergency Medicine, San Antonio, Texas.
United States Army Institute of Surgical Research, San Antonio, Texas.
West J Emerg Med. 2016 Mar;17(2):209-15. doi: 10.5811/westjem.2016.1.28087. Epub 2016 Mar 2.
Bedside thoracic ultrasound (US) can rapidly diagnose pneumothorax (PTX) with improved accuracy over the physical examination and without the need for chest radiography (CXR); however, US is highly operator dependent. A computerized diagnostic assistant was developed by the United States Army Institute of Surgical Research to detect PTX on standard thoracic US images. This computer algorithm is designed to automatically detect sonographic signs of PTX by systematically analyzing B-mode US video clips for pleural sliding and M-mode still images for the seashore sign. This was a pilot study to estimate the diagnostic accuracy of the PTX detection computer algorithm when compared to an expert panel of US trained physicians.
This was a retrospective study using archived thoracic US obtained on adult patients presenting to the emergency department (ED) between 5/23/2011 and 8/6/2014. Emergency medicine residents, fellows, attending physicians, physician assistants, and medical students performed the US examinations and stored the images in the picture archive and communications system (PACS). The PACS was queried for all ED bedside US examinations with reported positive PTX during the study period along with a random sample of negatives. The computer algorithm then interpreted the images, and we compared the results to an independent, blinded expert panel of three physicians, each with experience reviewing over 10,000 US examinations.
Query of the PACS system revealed 146 bedside thoracic US examinations for analysis. Thirteen examinations were indeterminate and were excluded. There were 79 true negatives, 33 true positives, 9 false negatives, and 12 false positives. The test characteristics of the algorithm when compared to the expert panel were sensitivity 79% (95 % CI [63-89]) and specificity 87% (95% CI [77-93]). For the 20 images scored as highest quality by the expert panel, the algorithm demonstrated 100% sensitivity (95% CI [56-100]) and 92% specificity (95% CI [62-100]).
This novel computer algorithm has potential to aid clinicians with the identification of the sonographic signs of PTX in the absence of expert physician sonographers. Further refinement and training of the algorithm is still needed, along with prospective validation, before it can be utilized in clinical practice.
床旁胸部超声（US）能够快速诊断气胸（PTX），与体格检查相比准确性更高，且无需进行胸部X线摄影（CXR）；然而，超声检查高度依赖操作者。美国陆军外科研究所开发了一种计算机化诊断助手，用于在标准胸部超声图像上检测气胸。该计算机算法旨在通过系统分析B超视频片段中的胸膜滑动以及M超静态图像中的海岸征，自动检测气胸的超声征象。这是一项初步研究，旨在评估与经超声培训的医师专家小组相比，气胸检测计算机算法的诊断准确性。
这是一项回顾性研究，使用2011年5月23日至2014年8月6日期间急诊科成年患者的存档胸部超声。急诊医学住院医师、研究员、主治医师、医师助理和医学生进行超声检查，并将图像存储在图像存档与通信系统（PACS）中。在研究期间，查询PACS中所有报告为气胸阳性的急诊科床旁超声检查以及随机抽取的阴性样本。然后计算机算法对图像进行解读，我们将结果与由三位医师组成的独立、盲法专家小组进行比较，每位医师都有超过10000次超声检查的阅片经验。
对PACS系统的查询显示有146次床旁胸部超声检查可供分析。13次检查结果不确定，予以排除。有79次真阴性、33次真阳性、9次假阴性和12次假阳性。与专家小组相比，该算法的检测特征为灵敏度79%（95%CI[63 - 89]），特异度87%（95%CI[77 - 93]）。对于专家小组评定为最高质量的20幅图像，该算法的灵敏度为100%（95%CI[56 - 100]），特异度为92%（95%CI[62 - 100]）。
这种新型计算机算法有潜力在没有专业超声医师的情况下帮助临床医生识别气胸的超声征象。在该算法能够应用于临床实践之前，仍需要对其进行进一步优化和培训，并进行前瞻性验证。
