Brainomix Study Validated Quantitative CT Lung Measures
New research confirms AI-powered imaging tools accurately track pulmonary fibrosis progression and treatment outcomes.
Updated on Oct. 1, 2026 in Asthma
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Brainomix has released phase III INBUILD study results showing its e-Lung software can effectively quantify lung disease. The study demonstrates that these quantitative CT measurements correlate with patient disease progression and treatment responses.
Why it matters
Quantitative CT imaging provides a more precise way to stratify patients and measure treatment efficacy, potentially serving as a vital complement to traditional lung function tests. This approach could significantly improve how clinicians monitor pulmonary fibrosis in real-world practice.
The study analyzed data from 474 patients across 24-week and 52-week clinical assessment intervals. Researchers utilized Brainomix e-Lung software to establish CT benchmarks, which were compared against established UCLA research algorithms.
The players
Brainomix
This medical technology company specializes in AI-powered software solutions for neurological and respiratory imaging.
UCLA
The University of California, Los Angeles provided the established research algorithms used to benchmark the new software.
Royal Brompton Hospital
This institution is a leading specialist center for heart and lung disease and was the home of the study's lead author.
The details
By utilizing Brainomix e-Lung software, researchers tracked changes in total disease extent to assess the impact of nintedanib on pulmonary fibrosis. Findings published in the American Journal of Respiratory & Critical Care Medicine suggest these AI metrics offer a reliable method for evaluating disease trajectory and therapeutic effectiveness.
Timeline
The clinical assessment of Nintedanib effects occurred at the 24-week mark.
Researchers tracked FVC decline and treatment response through a 52-week period.
The Big Picture
This research follows the patient cohort methodology established by the INBUILD trial to validate novel diagnostic markers. The findings extend the original study by successfully applying advanced AI-driven CT measurement tools to the existing data set.
Patients with pulmonary fibrosis may benefit from more personalized treatment plans enabled by these precise imaging benchmarks. These AI-driven tools could ultimately lead to earlier identification of therapy failure and more accurate disease monitoring.
The takeaway
The integration of AI-powered CT analysis into respiratory medicine represents a shift toward more objective, standardized data in patient care. Clinicians should monitor for future software updates as these automated tools transition from research environments to standard clinical settings.
Further reading
For broader context on diagnostic advancements for lung conditions, visit the Asthma section.
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