Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case–control study

BMC Pulmonary Medicine

Study Protocol

Johan N. Siebert¹  Alain Gervaix¹  Constance Barazzone-Argiroffo²  Pierre-Olivier Bridevaux³  Marlène Salamin³  Laura Robotham³  Jonathan Doenz⁴  Mary-Anne Hartley⁴  Delphine S. Courvoisier⁵ 

[1] Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland;Faculty of Medicine, University of Geneva, Geneva, Switzerland;Division of Paediatric Pulmonology, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva, Switzerland;Faculty of Medicine, University of Geneva, Geneva, Switzerland;Division of Pulmonology, Hospital of Valais, Sion, Switzerland;Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;Quality of Care Unit, Geneva University Hospitals, Geneva, Switzerland;Faculty of Medicine, University of Geneva, Geneva, Switzerland;

关键词: Lung diseases, Interstitial;    Idiopathic pulmonary fibrosis;    Idiopathic interstitial pneumonias;    Pulmonary disease, Chronic obstructive;    Deep learning;    Artificial intelligence;    Respiratory sounds;    Auscultation;    Ultrasonography;   

DOI  :  10.1186/s12890-022-02255-w

 received in 2022-09-03, accepted in 2022-11-20,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundInterstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach.MethodsA total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls.DiscussionThis approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity.Trial registration Registration: August 8, 2022. ClinicalTrials.gov Identifier: NCT05318599.

【 授权许可】

CC BY   
© The Author(s) 2023

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