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Santos-Silva C, Ferreira-Cardoso H, Silva S, Vieira-Marques P, Valente JC, Almeida R, A Fonseca J, Santos C, Azevedo I, Jácome C. Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study. JMIR Pediatr Parent 2024; 7:e52540. [PMID: 38602309 PMCID: PMC11024396 DOI: 10.2196/52540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/23/2023] [Accepted: 01/02/2024] [Indexed: 04/12/2024] Open
Abstract
Background The use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians. Objective This cross-sectional study aims to assess the feasibility of this technology when used by parents-the real intended end users. Methods Physicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 ("totally disagree") to 5 ("totally agree"). Results Most recordings had quality (physicians' recordings: 253/297, 85.2%; parents' recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5). Conclusions Our results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life.
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Affiliation(s)
| | | | - Sónia Silva
- Department of Pediatrics, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Pedro Vieira-Marques
- CINTESIS - Center for Health Technology and Services Research, Faculty of Medicine, Universidade do Porto, Porto, Portugal
| | - José Carlos Valente
- MEDIDA – Serviços em Medicina, Educação, Investigação, Desenvolvimento e Avaliação, Porto, Portugal
| | - Rute Almeida
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - João A Fonseca
- MEDIDA – Serviços em Medicina, Educação, Investigação, Desenvolvimento e Avaliação, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Cristina Santos
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Inês Azevedo
- Department of Pediatrics, Centro Hospitalar Universitário de São João, Porto, Portugal
- Department of Obstetrics, Gynecology and Pediatrics, Faculty of Medicine, Universidade do Porto, Porto, Portugal
- EpiUnit, Institute of Public Health, Universidade do Porto, Porto, Portugal
| | - Cristina Jácome
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
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Zhou TH, Zhou XX, Ni J, Ma YQ, Xu FY, Fan B, Guan Y, Jiang XA, Lin XQ, Li J, Xia Y, Wang X, Wang Y, Huang WJ, Tu WT, Dong P, Li ZB, Liu SY, Fan L. CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD. Mil Med Res 2024; 11:14. [PMID: 38374260 PMCID: PMC10877876 DOI: 10.1186/s40779-024-00516-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. METHODS This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. RESULTS Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. CONCLUSIONS The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.
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Affiliation(s)
- Tao-Hu Zhou
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- School of Medical Imaging, Shandong Second Medical University, Weifang, 261053, Shandong, China
| | - Xiu-Xiu Zhou
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Jiong Ni
- Department of Radiology, School of Medicine, Tongji Hospital, Tongji University, Shanghai, 200065, China
| | - Yan-Qing Ma
- Department of Radiology, Zhejiang Province People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, 310014, China
| | - Fang-Yi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang, 310018, China
| | - Bing Fan
- Jiangxi Provincial People's Hospital, the First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, China
| | - Yu Guan
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Xin-Ang Jiang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Xiao-Qing Lin
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Jie Li
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- College of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
| | - Yi Xia
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Xiang Wang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Yun Wang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Wen-Jun Huang
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
- Department of Radiology, the Second People's Hospital of Deyang, Deyang, 618000, Sichuan, China
| | - Wen-Ting Tu
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Peng Dong
- School of Medical Imaging, Shandong Second Medical University, Weifang, 261053, Shandong, China
| | - Zhao-Bin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Shi-Yuan Liu
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China
| | - Li Fan
- Department of Radiology, the Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, China.
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Zhang H, Zhou QQ, Chen H, Hu XQ, Li WG, Bai Y, Han JX, Wang Y, Liang ZH, Chen D, Cong FY, Yan JQ, Li XL. The applied principles of EEG analysis methods in neuroscience and clinical neurology. Mil Med Res 2023; 10:67. [PMID: 38115158 PMCID: PMC10729551 DOI: 10.1186/s40779-023-00502-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/23/2023] [Indexed: 12/21/2023] Open
Abstract
Electroencephalography (EEG) is a non-invasive measurement method for brain activity. Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural signals, EEG has aroused much interest in scientific research and medical fields. This article reviews the types of EEG signals, multiple EEG signal analysis methods, and the application of relevant methods in the neuroscience field and for diagnosing neurological diseases. First, three types of EEG signals, including time-invariant EEG, accurate event-related EEG, and random event-related EEG, are introduced. Second, five main directions for the methods of EEG analysis, including power spectrum analysis, time-frequency analysis, connectivity analysis, source localization methods, and machine learning methods, are described in the main section, along with different sub-methods and effect evaluations for solving the same problem. Finally, the application scenarios of different EEG analysis methods are emphasized, and the advantages and disadvantages of similar methods are distinguished. This article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives, provide references for subsequent research, and summarize current issues and prospects for the future.
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Affiliation(s)
- Hao Zhang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Qing-Qi Zhou
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China
| | - He Chen
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Xiao-Qing Hu
- Department of Psychology, the State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, 999077, China
- HKU-Shenzhen Institute of Research and Innovation, Shenzhen, 518057, Guangdong, China
| | - Wei-Guang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, 999077, China
| | - Yang Bai
- Department of Rehabilitation Medicine, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, China
- Rehabilitation Medicine Clinical Research Center of Jiangxi Province, Nanchang, 330006, China
| | - Jun-Xia Han
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, 100048, China
| | - Yao Wang
- School of Communication Science, Beijing Language and Culture University, Beijing, 100083, China
| | - Zhen-Hu Liang
- Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, Hebei, China.
| | - Dan Chen
- School of Computer Science, Wuhan University, Wuhan, 430072, China.
| | - Feng-Yu Cong
- School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116081, Liaoning, China.
| | - Jia-Qing Yan
- College of Electrical and Control Engineering, North China University of Technology, Beijing, 100041, China.
| | - Xiao-Li Li
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China.
- Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, 510335, China.
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