文献类型：期刊文献

英文题名：Enhanced pediatric pneumonia auxiliary diagnosis: Integrating optical fiber vibration sensing with machine learning

作者：Cao, Pengfei[1];Xu, Jiawei[1];Zhao, Yifan[1];Ni, Qian[2];Li, Yuxia[3];Chen, Hansen[1];Song, Ming[1];Shang, Jiqiang[1];Yu, Mengqiang[1];Ding, Xia[2];Ma, Zhanhua[2];Mao, Li[2];Tian, Wenxia[3];Zhang, Xiaofeng[2];Liang, Mengyun[1];Wen, Hao[1];Cao, Jie[1];Hu, Bin[1,4]

第一作者：Cao, Pengfei

通信作者：Cao, PF[1];Hu, B[1]

机构：[1]Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;[2]Lanzhou Univ, Dept Pediat, Hosp 2, Lanzhou 730030, Peoples R China;[3]Gansu Univ Chinese Med, Dept Pediat, Affiliated Hosp, Lanzhou 730000, Peoples R China;[4]Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China

第一机构：Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China.

年份：2025

卷号：160

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：;EI(收录号：20253419035354);Scopus(收录号：2-s2.0-105013740421);WOS:【SCI-EXPANDED(收录号:WOS:001561978800001)】；

基金：This work was supported in part by the Natural Science Foundation of Gansu Province (No. 20JR10RA614, 22YF7GA182, 22JR11RA042, 22JR5RA1006,24CXGA024) , the National Natural Science Foundation of China under Grant 61804071, the Open Fund of Key Laboratory of Time and Frequency Primary Standards, CAS, the Gansu Provincial University Industry Support Plan Project (2022CYZC-07 2022) , the Lanzhou Chengguan District Science and Technology Plan Project (2021RCCX0031) , 2023 Gansu Province health industry research project (GSWSHL2023-36) , Cuiying Scientific and Technological Innovation Program of the second hospital & clinical medical school (CY2022-BJ-08) .

语种：英文

外文关键词：Fiber optic vibration sensor; Machine learning; Auxiliary diagnosis of pediatric pneumonia; Respiratory vibration signals

摘要：Childhood pneumonia remains a primary cause of death in children under five. Early detection is arduous due to its inconspicuous symptoms, and the existing radiological diagnostic techniques carry the risk of radiation induced harm to young patients. To overcome these limitations, a groundbreaking study has proposed an innovative non - invasive diagnostic approach that integrates fiber optic vibration sensing technology with machine learning algorithms for pediatric pneumonia diagnosis. A novel fiber optic sensor is engineered to precisely capture respiratory vibration signals (RVS). These signals are then processed and analyzed using a Stacked - Grid Search Ensemble Learning Model (SGELM). In the experiment, respiratory vibration signals were gathered from 1649 pediatric patients aged between 3 and 14 who suffered from respiratory diseases. Through data balancing techniques, the dataset was expanded to 2184 samples. This dataset was partitioned into training, testing, and validation subsets. The developed system exhibited remarkable performance on the test dataset. It achieved high levels of accuracy, sensitivity, and specificity. Notably, it was also capable of classifying different pneumonia pathological types and statuses. This innovative method not only mitigates the radiation - related risks associated with traditional diagnostic methods but also holds great promise in revolutionizing the diagnosis of pediatric respiratory diseases. It could potentially improve the early - diagnosis rate and contribute to better treatment outcomes for children with pneumonia, thus playing a significant role in enhancing child health globally.