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

SUBMITTER: Gompelmann D 

PROVIDER: S-EPMC12322453 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature