AI-powered evaluation of lung function for diagnosis of interstitial lung disease

Abstract

Background The diagnosis of interstitial lung disease (ILD) can pose a challenge as the pulmonary function test (PFT) is only minimally affected at the onset. To improve early diagnosis, this study aims to explore the potential of artificial intelligence (AI) software in assisting pulmonologists with PFT interpretation for ILD diagnosis. The software provides an automated description of PFT and disease probabilities computed from an AI model. Study methods In study phase 1, a cohort of 60 patients, 30 of whom had ILD, were retrospectively diagnosed by 25 pulmonologists (8 junior physicians and 17 experienced pneumologists) by evaluating a PFT (body plethysmography and diffusion capacity) and a short medical history. The experts screened the cohort twice, without and with the aid of AI (ArtiQ.PFT, V.1.4.0, ArtiQ, BE) software and provided a primary diagnosis and up to three differential diagnoses for each case. In study phase 2, 19 pulmonologists repeated the protocol after using ArtiQ.PFT for 4–6 months. Results Overall, AI increased the diagnostic accuracy for various lung diseases from 41.8% to 62.3% in study phase 1. Focusing on ILD, AI improved the detection of lung fibrosis as the primary diagnosis from 42.8% without AI to 72.1% with AI (p<0.0001). Phase 2 yielded a similar outcome: using AI increased ILD diagnosis based on primary diagnosis (53.2% to 75.1%; p<0.0001). ILD detections without AI support significantly increased between phase 1 and phase 2 (p=0.028) but not with AI (p=0.24). Interpretation This study shows that AI-based decision support on PFT interpretation improves accurate and early ILD diagnosis. All data relevant to the study are included in the article or uploaded as supplementary information.