AI Transforms the Early Detection of Lung Cancer
When you think of lung cancer prevention, your first thought is probably to reduce cigarette smoking. While anti-smoking initiatives are highly successful (67% reduction since 1965), lung cancer is still among the most common cancers and results in the most cancer-related deaths in the US. There is still work to be done to prevent severe lung cancer cases, and much of that work revolves around improving the early screening process to catch cancer before it spreads. This week’s Domain Knowledge focuses on recent AI developments in lung cancer detection.
The American Cancer Society’s National Consortium for Cancer Screening and Care recommends 9 ways to boost cancer screening rates in a recent report, many of which align with AI designed to improve efficiency, access, and quality control. The first objective, which is the foundation for all of the points listed, says “accelerating collective action of partnerships will influence the adoption of new screening interventions and policies”. As AI technology improves, it is equally important to work with healthcare professionals to integrate them smoothly into existing screening practices.
One significant way AI impacts lung cancer screening is by enhancing the peer review process used to assess diagnostic images for lung nodules. In a recent case study using AI in screenings for an 8-month period, thousands of scans were assessed for lung nodules and 118 scans were found to have clinically significant nodules that directly impacted patient care. Not only does AI allow for more scans to be screened, but in this case, it does so without disrupting the workflow of radiologists.
Other recent advancements in lung cancer detection utilize deep learning algorithms to detect nodules through various imaging methods. This recently published study shows how a deep learning model using the segmentation method can detect lung cancer on chest radiographs.
Additionally, image reconstruction can detect lung cancer nodules on ultra-low-dose CT scans. This could allow larger scale screening while minimizing radiation exposure for patients.
That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an AI Hub to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog.
If you have AI tips, suggestions, or resources you’d like to share leave us a note below, and please feel free to suggest topics you would like to see covered in future posts.