CT Lung Imaging Combined with Machine Learning Predicts Further COPD Care
By MedImaging International staff writers Posted on 24 Jun 2022 |
Healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is a growing concern. Patients with COPD are more likely to utilize healthcare services, have higher rates of hospitalizations and hospital readmissions, and higher rates of mortality. Hence, predicting increased risk of future healthcare utilization in COPD patients is important for improving patient management. Now, a new study has found that healthcare utilization could potentially be predicted in mild COPD patients using computed tomography (CT) lung imaging and machine learning.
The study by researchers at the Toronto Metropolitan University (Toronto, ON, Canada) aimed to determine the importance of CT lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. In the retrospective study, the researchers evaluated lung function measurements and chest CT images of 527 COPD participants from 2010 to 2017. Up to two follow-up visits (1.5- and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utilization was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits.
The researchers found that out of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant differences between the participants with or without healthcare utilization at follow-up for age, sex, BMI or pack-years. The accuracy for predicting subsequent healthcare utilization was 80% when all measurements were considered, 76% for CT measurements alone and 65% for demographic and lung function measurements alone. Based on these findings, the researchers concluded that a combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD.
Related Links:
Toronto Metropolitan University
Latest General/Advanced Imaging News
- PET Scans Reveal Hidden Inflammation in Multiple Sclerosis Patients
- Artificial Intelligence Evaluates Cardiovascular Risk from CT Scans
- New AI Method Captures Uncertainty in Medical Images
- CT Coronary Angiography Reduces Need for Invasive Tests to Diagnose Coronary Artery Disease
- Novel Blood Test Could Reduce Need for PET Imaging of Patients with Alzheimer’s
- CT-Based Deep Learning Algorithm Accurately Differentiates Benign From Malignant Vertebral Fractures
- Minimally Invasive Procedure Could Help Patients Avoid Thyroid Surgery
- Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery
- AR Application Turns Medical Scans Into Holograms for Assistance in Surgical Planning
- Imaging Technology Provides Ground-Breaking New Approach for Diagnosing and Treating Bowel Cancer
- CT Coronary Calcium Scoring Predicts Heart Attacks and Strokes
- AI Model Detects 90% of Lymphatic Cancer Cases from PET and CT Images
- Breakthrough Technology Revolutionizes Breast Imaging
- State-Of-The-Art System Enhances Accuracy of Image-Guided Diagnostic and Interventional Procedures
- Catheter-Based Device with New Cardiovascular Imaging Approach Offers Unprecedented View of Dangerous Plaques
- AI Model Draws Maps to Accurately Identify Tumors and Diseases in Medical Images