Idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive, irreversibly incapacitating pulmonary disorders with modest response to therapeutic interventions and 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 (AI)-assisted digital lung auscultation could constitute an alternative to conventional subjective operator-related auscultation to accurately and earlier diagnose these diseases. Moreover, lung ultrasound (LUS), a relevant gold standard for lung pathology, could also benefit from automation by deep learning.
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
To differentiate ILD from control subjects based on digital lung sounds recordings and LUS.
Timeframe: During lung auscultation (10 minutes). Each patient will provide 10 recordings of 30 seconds. LUS images and 5 second video clips of each anatomic region (10 regions represented).
Predictive performance of the DeepBreath algorithm to stratify ILD severity based on human digital lung sounds recordings and LUS (i.e. physiological parameters) compared to grading scales.
Timeframe: During lung auscultation (10 minutes). Each patient will provide 10 recordings of 30 seconds. LUS images and 5 second video clips of each anatomic region (10 regions represented).
Performance of the DeepBreath algorithm to subcategorize ILD by discriminating digital lung sounds recordings and LUS (i.e. physiological parameters).
Timeframe: During lung auscultation (10 minutes). Each patient will provide 10 recordings of 30 seconds. LUS images and 5 second video clips of each anatomic region (10 regions represented).