# Computer-aided diagnosis for lung cancer screening

DevFeed: [Computer-aided diagnosis for lung cancer screening](<https://devfeed.tech/articles/computer-aided-diagnosis-for-lung-cancer-screening-28555.md>)

Original publisher: [Read original article](<http://blog.research.google/2024/03/computer-aided-diagnosis-for-lung.html>)

Author: Google AI (noreply@blogger.com)

Published: 2024-03-20T20:54:00Z

Content type: article

Language: en

Sources: [Google Research](<https://devfeed.tech/sources/google-research.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Google](<https://devfeed.tech/topics/google.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Image](<https://devfeed.tech/topics/image.md>)

Tags: [false-positives](<https://devfeed.tech/tags/false-positives.md>), [google](<https://devfeed.tech/tags/google.md>), [health](<https://devfeed.tech/tags/health.md>), [healthcare](<https://devfeed.tech/tags/healthcare.md>), [japan](<https://devfeed.tech/tags/japan.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [reporting](<https://devfeed.tech/tags/reporting.md>), [user-experience](<https://devfeed.tech/tags/user-experience.md>)

## AI overview

Google Research describes a machine-learning system for lung cancer screening that analyzes CT scans, assigns one of four cancer-suspicion ratings, and highlights corresponding regions of interest. The system was evaluated in randomized reader studies in the US and Japan using local scoring systems and realistic image viewers.

## Source excerpt

Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product Manager, Google Research Lung cancer is the leading cause of cancer-related deaths globally with 1.8 million deaths reported in 2020. Late diagnosis dramatically reduces the chances of survival. Lung cancer screening via computed tomography (CT), which provides a detailed 3D image of the lungs, has been shown to reduce mortality in high-risk populations by at least 20% by detecting potential signs of cancers earlier. In the US, screening involves annual scans, with some countries or cases recommending more or less frequent scans. The United States Preventive Services Task Force recently expanded lung cancer screening recommendations by roughly 80%, which is expected to increase screening access for women and racial and ethnic minority groups. However, false positives (i.e., incorrectly reporting a potential cancer in a cancer-free patient) can cause anxiety and lead to unnecessary procedures for patients while increasing costs for the healthcare system. Moreover, efficiency in screening a large number of individuals can be challenging depending on healthcare infrastructure and radiologist availability. At Google we have previously developed machine learning (ML) models for lung cancer detection, and have evaluated their ability to automatically detect and classify regions that show signs of potential cancer. Performance has been shown to be comparable to that of specialists in detecting possible cancer. While they have achieved high performance, effectively communicating findings in realistic environments is necessary to realize their full potential. To that end, in "Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the US and Japan", published in Radiology AI, we investigate how ML models can effectively communicate findings to radiologists. We also introduce a generalizable user-centric interface to help radiologists leverage such models for lung cancer screening. The