Hemopneumothorax detection through the process of artificial evolution-a feasibility study  

作　　者：Adir Sommer Noy MarkGavriel D.Kohlberg Rafi Gerasi Linn Wagnert Avraham Ruth Fan-Marko Arik Eisenkraft Dean Nachman 

机构地区：[1]The Medical Corps,Israel Defense Forces(IDF),Tel Hashomer,5262000 Ramat Gan,Israel [2]Department of Otolaryngology-Head and Neck Surgery,University of Washington,Seattle,WA 98195,USA [3]The Institute for Research in Military Medicine,the Faculty of Medicine,the Hebrew University of Jerusalem and the IDF Medical Corps,9112102 Jerusalem,Israel [4]The Hebrew University School of Medicine,9112102 Jerusalem,Israel [5]Department of Medicine,Hadassah Medical Center,9112102 Jerusalem,Israel.

出　　处：《Military Medical Research》2022年第1期12-20,共9页军事医学研究（英文版）

基　　金：supported by the Israel Defense Forces(IDF)Medical Corps and Directorate of Defense Research&Development,Israeli Ministry of Defense(IMOD DDR&D)。

摘　　要：Background:Tension pneumothorax is one of the leading causes of preventable death on the battlefield.Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination.These techniques are not fully applicable in field conditions and on the battlefield,where situational and environmental factors may impair clinical capabilities.We aimed to assemble a device able to sample,analyze,and classify the unique acoustic signatures of pneumothorax and hemothorax.Methods:Acoustic data was obtained with simultaneous use of two sensitive digital stethoscopes from the chest wall of an ex-vivo porcine model.Twelve second samples of acoustic data were obtained from the in-house assembled digital stethoscope system during mechanical ventilation.The thoracic cavity was injected with increasing volumes of 200,400,600,800,and 1000 ml of air or saline to simulate pneumothorax and hemothorax,respectively.The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution,a cutting-edge approach in the artificial intelligence discipline.Results:The in-house assembled dual digital stethoscope system and developed genetic algorithm achieved an accuracy,sensitivity and specificity ranging from 64%to 100%,63%to 100%,and 63%to 100%,respectively,in classifying acoustic signal as associated with pneumothorax or hemothorax at fluid injection levels of 400 ml or more,and regardless of background noise.Conclusions:We present a novel,objective device for rapid diagnosis of potentially lethal thoracic injuries.With further optimization,such a device could provide real-time detection and monitoring of pneumothorax and hemothorax in battlefield conditions.

关 键 词：PNEUMOTHORAX HEMOTHORAX TRAUMA BATTLEFIELD Artificial evolution Machine learning 

分 类 号：R826.63[医药卫生—临床医学]