Interstitial Lung Diseases (ILDs) are a broad category of over 200 lung diseases that can cause scarring (fibrosis) to the lungs. The damage caused to the lungs from ILDs is often irreversible and can get worse over time. Early and accurate diagnosis is key to getting patients access to the right treatment and improving quality of life.

There is a growing need for more accurate and reproducible assessment and quantification of ILD in order to get patients access to the right treatment at an earlier timepoint and to improve the success of clinical trials.

Brainomix's proprietary lung imaging biomarkers have been trained using thousands of CT scans from leading academic institutions to accurately quantify pulmonary fibrosis and other characteristics.

Brainomix lung imaging biomarkers provide accurate and objective quantification of disease progress over time and can also identify patients that are at risk of progression.

Recent data presented at ATS and ERS demonstrated Brainomix lung imaging biomarkers' capability to predict both short- and long-term outcomes in Idiopathic Pulmonary Fibrosis (IPF) and non-IPF cohorts, including relative Forced Vital Capacity (FVC) decline and survival. When compared to current standard measures, including FVC and radiological assessment, Brainomix lung imaging biomarkers were a better predictor of survival when assessing change between two timepoints.

Brainomix 360 e-Lung is an FDA-cleared workflow enhancement and visualisation tool for the assessment of CT thoracic datasets, which saves time by providing automatic and objective quantifications of the lungs including lung segmentation, volumetric measurements, structural and coarse density evaluations.

Dr Deji Adegunsoye, Assistant Professor of Medicine and Scientific Director of the Interstitial Lung Disease Program at University of Chicago Medicine, said: "This is an exciting step for Brainomix, who have a demonstrated track record of developing novel AI-based solutions in stroke and are now applying that expertise to develop innovative tools in the lung space. The preliminary data for e-Lung is impressive and would indicate that we have a promising tool that could help to expedite healthcare delivery and improve clinically meaningful outcomes for patients with lung disease."