DENVER — A deep-learning system designed to read sinus CT scans could give researchers a more sensitive way to measure how chronic rhinosinusitis with nasal polyps responds to treatment, according to a new study led in part by investigators at National Jewish Health. The technology, evaluated using data from two randomized controlled trials, detects changes in sinus opacification that may be too subtle or inconsistent for conventional visual scoring. The findings suggest that artificial intelligence could make CT imaging a more precise tool for tracking disease activity and evaluating new therapies in clinical research.
The study, published in the International Forum of Allergy & Rhinology, examined an automated deep learning-based sinus severity score known as SSS. The score is designed to quantify the amount of material blocking or filling the sinus cavities on computed tomography images. Researchers compared the automated measurement with the Lund-Mackay score, the established clinical and research method in which radiologists visually grade each sinus according to the degree of opacification. In both treatment trials, the automated system was more responsive to changes observed after therapy.
Chronic rhinosinusitis with nasal polyps is a long-lasting inflammatory disease affecting the nose and the air-filled spaces surrounding it. In people with the condition, inflamed tissue and soft, noncancerous growths called polyps can obstruct the nasal passages and interfere with drainage from the sinuses. Symptoms may include persistent nasal congestion, loss of smell, facial pressure and recurring infections. Because inflammation can involve several sinus compartments at once, CT imaging is frequently used to characterize the extent of disease and to assess whether treatment has altered the underlying sinus abnormalities.
The Lund-Mackay system has helped standardize CT interpretation for decades, but it remains dependent on human judgment. Radiologists assign scores to individual sinuses based largely on how much of each cavity appears opaque on the scan. Although the method is practical and widely accepted, visual categories can compress complex imaging information into relatively broad grades. Small changes may not be reflected when a sinus remains within the same scoring category, and different readers may interpret borderline findings differently. These limitations are especially important in clinical trials, where researchers need reliable measurements capable of detecting treatment effects.
The deep-learning SSS approaches the problem differently. Rather than asking a reader to place a sinus into a discrete visual category, the system analyzes the CT data computationally and estimates the proportion of each sinus that is opacified. Deep-learning models are trained on large collections of medical images to recognize patterns associated with anatomy and disease. Once trained, such a model can process new scans using the same algorithmic criteria each time. This quantitative approach may preserve more of the continuous information contained in a CT image, allowing researchers to identify modest reductions or increases in sinus disease that might be overlooked by categorical scoring.
“CT imaging gives us important information about what is happening inside the sinuses, but traditional scoring methods are subjective and may not capture smaller changes over time,” said Stephen M. Humphries, PhD, a researcher in the Department of Radiology at National Jewish Health and senior author of the study. “By using deep learning to quantify disease objectively, we have the potential to measure treatment response with greater precision.” The researchers’ comparison focused on responsiveness to change, a key property for a clinical-trial endpoint. A measurement that changes consistently when disease improves can help distinguish a genuine therapeutic effect from reader variability or random fluctuations.
The analysis drew on CT data from two randomized clinical trials evaluating the same treatment in patients with chronic rhinosinusitis with nasal polyps. When scans obtained before and after treatment were assessed, the automated score detected treatment-related differences more effectively than the Lund-Mackay score. Greater sensitivity does not necessarily mean that the algorithm replaces clinical assessment or proves that a patient feels better; rather, it indicates that the system may be better able to quantify radiographic change. CT findings must still be interpreted alongside symptoms, nasal examination, smell testing, polyp measurements and other outcomes that reflect the patient’s experience.
The potential impact extends beyond a single disease or medication. Clinical trials often require endpoints that are reproducible, sensitive and capable of showing whether a therapy affects the biological process it is intended to treat. If an imaging measure misses small but meaningful changes, investigators may need larger studies or longer follow-up periods to demonstrate efficacy. A more precise automated score could reduce measurement noise, improve statistical power and help researchers compare treatment responses across study populations. It may also provide a consistent framework for examining therapies with different mechanisms, including anti-inflammatory medicines, biologic drugs and surgical interventions.
The investigators emphasized that the findings should be interpreted within the limits of the study design. The model was tested using data from two clinical trials involving one treatment, so its performance with other therapies, scanners, patient populations and disease patterns remains to be established. CT images can vary according to acquisition settings and equipment, and an algorithm trained on one dataset may not perform identically in another clinical environment. Additional validation will be needed to determine whether the score remains accurate across hospitals and whether changes detected by the system correlate with outcomes that matter most to patients.
The work builds on earlier research using automated CT analysis in chronic sinus disease, including studies of patients with cystic fibrosis in which computational methods detected longitudinal changes in sinus opacification. Together, these findings point toward a broader role for artificial intelligence in respiratory imaging: not simply identifying whether disease is present, but measuring how it evolves over time. For chronic rhinosinusitis with nasal polyps, a condition that can persist or recur despite treatment, that distinction could be valuable. More objective imaging may help researchers understand treatment biology, identify subtle responses and design more informative trials, while clinicians continue to use imaging as one component of a comprehensive evaluation.
Subject of Research: Deep-learning analysis of sinus CT scans and treatment response in chronic rhinosinusitis with nasal polyps.
Article Title: Deep Learning Improves Sensitivity to Change in Sinus Computed Tomography: Evidence From Two Randomized Controlled Trials
Web References: International Forum of Allergy & Rhinology article; Stephen M. Humphries, PhD; National Jewish Health media resources
References: International Forum of Allergy & Rhinology. DOI: 10.1002/alr.70218. Article publication date: 16 July 2026.
Keywords: deep learning, artificial intelligence, sinus CT, computed tomography, chronic rhinosinusitis, nasal polyps, sinus opacification, Lund-Mackay score, treatment response, medical imaging, clinical trials, National Jewish Health
Tags: AI compared to traditional sinus scoringAI-driven sinusitis treatment monitoringartificial intelligence for chronic sinus diseaseautomated sinus severity scoringclinical research in sinusitis using deep learningcomputer-aided diagnosis in rhinosinusitisCT scan disease progression measurementDeep Learning in Radiologydeep learning sinus CT analysisimaging analysis for nasal polypsinnovative approaches in sinus disease assessmentprecision imaging for sinus disease tracking
