Clinical Evidence: e-Lung

Explore peer-reviewed studies validating e-Lung’s performance and value, published by international academic groups. Brainomix products have been rigorously tested across diverse clinical settings—find the evidence below.

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Evaluation of e-Lung automated quantitative computed tomography biomarkers in idiopathic pulmonary fibrosis

WRVS is an automated CT biomarker which can identify patients with IPF at increased risk of progression and is able to reliably capture disease progression over time

Dec 2, 2024

e-Lung CT Biomarkers Can Stratify Patients at Risk of IPF Progression at 52 Weeks; Post-hoc Analysis From a Randomised Control Trial

This study evaluated the ability for baseline WRVS to identify patients at risk of FVC decline of ≥10% over 52 weeks

May 17, 2024

e-Lung Computed Tomography Biomarker Stratifies Patients at Risk of Idiopathic Pulmonary Fibrosis Progression in a 52-Week Clinical Trial

This study evaluated the ability for baseline WRVS to identify patients at risk of FVC decline of ≥10% over 52 weeks

Feb 16, 2024

The automated e-Lung CT algorithm is more prognostic than lung function in patients with non IPF fibrotic ILD; a validation study

3D image data from CT scans in non-IPF fibrotic ILD patients with contemporaneous lung function tests were used. Derived outputs include a novel imaging biomarker, the weighted reticulovascular score (WRVS), which incorporates fibrotic features and abnormal vascular structures.

Sept 9, 2023

Novel e-Lung CT biomarkers combine to provide higher prognostic discrimination than FVC in patients with non-IPF fibrotic interstitial lung disease.

To establish the relationship between e-Lung ground glass opacity score at baseline and at serial CT with outcome in patients with non-IPF pulmonary fibrotic lung diseases

Sept 9, 2023

In the Serial Assessment of Patients With Idiopathic Pulmonary Fibrosis, the Automated E-ILD CT Algorithm Outperforms Lung Function; a Validation Study

Using the Open-Source Imaging Consortium database, associations between e-ILD biomarkers and clinical outcomes were explored and validated.

May 19, 2023

Performance and Reproducibility of a Deep-learning Derived Automated Algorithm to Detect, Segment and Measure Lung Cancer Lesions on Computed Tomography Scans

The performance and reproducibility of the e-ACT algorithm in detecting, segmenting, and measuring lung cancer lesions on thoracic CT scans were assessed.

Jan 1, 2023