Artificial Intelligence and Machine Learning in Lung Cancer Screening

Scott J Adams¹, Peter Mikhael², Jeremy Wohlwend², Regina Barzilay², Lecia V Sequist³, Florian J Fintelmann⁴

Affiliations

¹ Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
² Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, Boston, MA, USA. Electronic address: lvsequist@partners.org.
⁴ Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA. Electronic address: fintelmann@mgh.harvard.edu.

PMID: 37806742
DOI: 10.1016/j.thorsurg.2023.03.001

Review

Artificial Intelligence and Machine Learning in Lung Cancer Screening

Scott J Adams et al. Thorac Surg Clin. 2023 Nov.

. 2023 Nov;33(4):401-409.

doi: 10.1016/j.thorsurg.2023.03.001. Epub 2023 May 12.

Authors

Scott J Adams¹, Peter Mikhael², Jeremy Wohlwend², Regina Barzilay², Lecia V Sequist³, Florian J Fintelmann⁴

Affiliations

¹ Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
² Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA, USA.
³ Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA; Harvard Medical School, Boston, MA, USA. Electronic address: lvsequist@partners.org.
⁴ Harvard Medical School, Boston, MA, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA. Electronic address: fintelmann@mgh.harvard.edu.

PMID: 37806742
DOI: 10.1016/j.thorsurg.2023.03.001

Abstract

Recent advances in artificial intelligence and machine learning (AI/ML) hold substantial promise to address some of the current challenges in lung cancer screening and improve health equity. This article reviews the status and future directions of AI/ML tools in the lung cancer screening workflow, focusing on determining screening eligibility, radiation dose reduction and image denoising for low-dose chest computed tomography (CT), lung nodule detection, lung nodule classification, and determining optimal screening intervals. AI/ML tools can assess for chronic diseases on CT, which creates opportunities to improve population health through opportunistic screening.

Keywords: Artificial intelligence; Lung cancer screening; Lung nodule detection; Machine learning; Opportunistic screening; Risk prediction.

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Artificial Intelligence and Machine Learning in Lung Cancer Screening

Affiliations

Artificial Intelligence and Machine Learning in Lung Cancer Screening

Authors

Affiliations

Abstract

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MeSH terms

LinkOut - more resources

Full Text Sources