Computer Vision for Detection of Tobacco Related Diseases
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: In February 2015 the Centers for Medicare and Medicaid Services (CMS) approved lung cancer screening with low dose computed tomography (LDCT). In a recent large lung cancer screening clinical trial, 35% of deaths were caused by cardiovascular events and respiratory illness. We will fully develop for clinical usage a computer system with machine intelligence that analyses these screening CTs and produces a new test panel that screens for a range of tobacco related diseases, including lung cancer, emphysema, interstitial lung disease (ILD), and heart disease (coronary artery and aortic calcification). This new panel will increase the value of the lung cancer screening exam for early detection and risk assessment of not only tobacco related cancer, but also cardiopulmonary disease. It will maximize the information content of LDCT screening, can provide motivation tension to quit smoking, and will inform timely referral of patients for diseases in which management strategies could reduce mortality. The research and development to bring this technology to practice will involve two key innovations: (1) the development of the new quantitative imaging panel that is unique is its focus and completeness for tobacco related disease risk assessment, and (2) making the computer system reliable for low dose CT acquisitions which are noisy and of variable quality in clinical screening. Computer vision systems based on machine learning can bring the expertise learned from thousands of exams annotated by experienced academic radiologists to the analysis of every screening CT. However, despite a substantial number of research papers on automated nodule detection, there is a distinct lack of such technology in routine clinical practice. The innovations in this proposal specifically address two key reasons for this, firstly, completeness of measurement capabilities needed to assess a patient, and secondly, reliability of automated detection and measurements in the clinical setting. Machine learning is typically performed on a local research database of diagnostic dose, low noise, CT scans and thus the systems fail when exposed to a range of acquisition protocols in clinical practice and especially those used in a low dose screening setting. We will use the multiple reconstructions from raw CT data to overcome this limitation. The two primary aims of this project are to: 1) Develop a computer system with machine intelligence that can reliably analyze low dose lung cancer screening CT scans for measurement of: (a) lung nodules suspicious for lung cancer, (b) the presence and extent of emphysema, (c) classification of ILD patterns, (d) aortic diameter (dilatation) and calcification, and (e) coronary artery calcium scoring to provide a measure of cardiac risk. 2) Evaluate the ability to automatically and robustly calculate this quantitative imaging panel across a wide range of patients and LDCT screening techniques, thus ensuring that the system can be deployed in any community clinic or large medical center. The investigative team at the UCLA Center for Computer Vision and Imaging Biomarkers (CVIB) team has extensive experience in translating computer vision algorithms from the laboratory and into clinical practice both in academic and commercial settings. The resulting computer vision system, capable of high throughput automation can be deployed in a secure cloud environment and thus has the ability to bring machine-learned quantitative diagnostic capabilities to the point of care at the screening exam to support decision making of the patient and their physician.