AI Tool Combines Lung Screening CT with Clinical Data to Predict Lung Cancer without Radiologist Assistance
|
By HospiMedica International staff writers Posted on 28 Oct 2021 |
|
A new artificial intelligence (AI)-based model that combines lung screening computed tomography (CT) information with clinical data has been shown to better predict lung cancer without the need for manual reading.
The deep learning tool built by scientists at the Vanderbilt University (Nashville, TN, USA) integrates CT features such as nodule size and risk factors including age, pack-years smoked, cancer history, among others. The scientists developed and tested the co-learning model on exams of more than 23,000 patients and found that it outperformed risk models utilizing clinical or imaging data alone, including the popular Brock model.
The scientists developed the deep learning tool by applying a five-fold cross-validation approach to data of 23,505 patients from the National Lung Screening Trial. The team used screening data from close to 150 patients in an in-house program for external testing. The scientists found that the deep learning tool notched an area under the receiver operating characteristic curve score of 0.88, which was higher than published models dependent completely on imaging data (0.86) and clinical risk factors (0.69).
More importantly, the deep learning tool automatically pulls high-risk regions from CT exams without the need for any effort by the radiologist. However, gathering clinical data does involve manual effort from radiologists and physicians. The scientists believe that their co-learning approach could be particularly beneficial as more patients start to qualify for screening exams and the insights from the tool could find low-risk individuals to be actually high-risk.
“Risk estimation among lung screening participants will become even more important with the impending expansion of screening guidelines to include those patients who are considered lower risk only based on age and history of tobacco use,” stated Riqiang Gao, a PhD student in computer science at Vanderbilt University. “The role of [the] radiologist is still irreplaceable in terms of looking for and reporting clinically significant findings (emphysema, pulmonary fibrosis, atelectasis, etc.).”
Related Links:
Vanderbilt University
Channels
Artificial Intelligence
view channel
Machine Learning Approach Enhances Liver Cancer Risk Stratification
Hepatocellular carcinoma, the most common form of primary liver cancer, is often detected late despite targeted surveillance programs. Current screening guidelines emphasize patients with known cirrhosis,... Read more
New AI Approach Monitors Brain Health Using Passive Wearable Data
Brain health spans cognitive and emotional functions and can fluctuate even in adults without diagnosed disease. Detecting early changes remains difficult in routine care and burdens specialty services... Read more
Critical Care
view channel
Automated IV Labeling Solution Improves Infusion Safety and Efficiency
Medication administration in high-acuity settings is often complicated by multiple concurrent infusions, making accurate line identification essential. In a 10-hospital intensive care unit study, 60% of... Read more
First-Of-Its-Kind AI Tool Detects Pulmonary Hypertension from Standard ECGs
Pulmonary hypertension is a progressive, life‑threatening disease that is frequently missed early because symptoms such as dyspnea are nonspecific and diagnostic delays can exceed two years.... Read more
Surgical Techniques
view channel
Continuous Monitoring with Wearables Enhances Postoperative Patient Safety
Postoperative hypoxemia on general surgical wards is common and often missed by intermittent vital sign checks. Undetected low oxygen levels can delay recovery and raise the risk of complications that... Read more
New Approach Enables Customized Muscle Tissue Without Biomaterial Scaffolds
Volumetric muscle loss is a traumatic loss of skeletal muscle that often leads to permanent functional impairment and limited reconstructive options. Current experimental strategies struggle to deliver... Read more
Patient Care
view channel
Wearable Sleep Data Predict Adherence to Pulmonary Rehabilitation
Chronic obstructive pulmonary disease (COPD) is a long-term lung disorder that makes breathing difficult and often disturbs sleep, reducing energy for daily activities. Limited engagement in pulmonary... Read more
Revolutionary Automatic IV-Line Flushing Device to Enhance Infusion Care
More than 80% of in-hospital patients receive intravenous (IV) therapy. Every dose of IV medicine delivered in a small volume (<250 mL) infusion bag should be followed by subsequent flushing to ensure... Read more
Health IT
view channel
EMR-Based Tool Predicts Graft Failure After Kidney Transplant
Kidney transplantation offers patients with end-stage kidney disease longer survival and better quality of life than dialysis, yet graft failure remains a major challenge. Although a successful transplant... Read more
Printable Molecule-Selective Nanoparticles Enable Mass Production of Wearable Biosensors
The future of medicine is likely to focus on the personalization of healthcare—understanding exactly what an individual requires and delivering the appropriate combination of nutrients, metabolites, and... Read more
Business
view channel
GE HealthCare Leads Major European Initiative to Advance Cardio-Oncology Care
Cardiovascular complications are increasingly recognized among people undergoing or recovering from cancer treatment, driven by pre‑existing disease and the cardiotoxic effects of chemotherapy, radiotherapy,... Read more
