Putting artificial intelligence (AI) on the spot: Machine learning evaluation of pulmonary nodules

Yasmeen K. Tandon, Brian J. Bartholmai, Chi Wan Koo

Research output: Contribution to journalArticlepeer-review

Abstract

Lung cancer remains the leading cause of cancer related death world-wide despite advances in treatment. This largely relates to the fact that many of these patients already have advanced diseases at the time of initial diagnosis. As most lung cancers present as nodules initially, an accurate classification of pulmonary nodules as early lung cancers is critical to reducing lung cancer morbidity and mortality. There have been significant recent advances in artificial intelligence (AI) for lung nodule evaluation. Deep learning (DL) and convolutional neural networks (CNNs) have shown promising results in pulmonary nodule detection and have also excelled in segmentation and classification of pulmonary nodules. This review aims to provide an overview of progress that has been made in AI recently for pulmonary nodule detection and characterization with the ultimate goal of lung cancer prediction and classification while outlining some of the pitfalls and challenges that remain to bring such advancements to routine clinical use.

Original languageEnglish (US)
Pages (from-to)6954-6965
Number of pages12
JournalJournal of Thoracic Disease
Volume12
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Artificial intelligence (AI)
  • Machine learning (ML)
  • Pulmonary nodule

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

Fingerprint Dive into the research topics of 'Putting artificial intelligence (AI) on the spot: Machine learning evaluation of pulmonary nodules'. Together they form a unique fingerprint.

Cite this