Software derives biomarkers designed to monitor respiratory disease.
Functional respiratory imaging company Fluidda secured clearance from the U.S. Food & Drug Administration (FDA) this week for its digital imaging platform, Broncholab. The product is designed to help providers visualize factors that can help diagnose respiratory disease.
“Functional respiratory imaging has been used in clinical trials for many years and has proven its value time and time again,” said Jan De Backer, Fluidda’s chief executive officer, in a statement. “Broncholab now extends these capabilities into clinical practice which is a tremendous step forward in our quest for better respiratory care.”
Used after a low-dose CT scan, Broncholab can derive several biomarkers, such as nodule and airway volume, trapped air pockets, and maps of ventilation. According to company information, it can also predict how drug particles are deposited throughout the lung when a patient breathes them in, helping to guide and maximize treatment options.
In addition to using high-resolution CT scans, the BronchoLab system incorporates digital image processing, 3D modeling, and computational airflow simulations to offer clinically relevant information about perfusion in various lung regions.
The hope, De Backer added in the company statement, is that BronchoLab will be able to improve the understanding of respiratory illnesses, better preparing the healthcare system for global outbreaks of lung diseases, much like the current COVID19 pandemic.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.