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<p>Lung cancer remains a critical global health challenge, with its prognosis heavily dependent on the timing of diagnosis. This literature review critically examines Artificial Intelligence and Computer-Aided Diagnosis (CADx) systems for lung cancer detection using Computed Tomography (CT) images, guided by seven pivotal research questions. Adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards and focusing on high-impact studies from 2013 to 2023, we provide an exhaustive assessment of current methodologies, underscore the variety and efficacy of algorithms and datasets, and evaluate preprocessing and performance evaluation strategies. Our findings reveal significant advancements in integrating machine learning and deep learning techniques, highlighting the importance of machine learning and deep learning methods and scrutinizing their goals, strengths, and limitations. Through a comprehensive meta-analysis, we offer insights into the state-of-the-art in lung cancer CADx, emphasizing data handling, model robustness, and avenues for enhancing diagnostic accuracy and reliability. This review not only critically relates varied methodologies and validates them against established metrics but also offers insights into future research trajectories aimed at enhancing early and accurate lung cancer diagnosis, thereby markedly improving patient outcomes. Targeting broad audiences, from experts in biomedical engineering to those across engineering and clinical sciences, we pave the way for future innovations in this vital domain.</p>

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Role of Artificial Intelligence in Early Assessment of Lung Nodules: A Brief Review

  • 期刊论文

  • 发表日期：

    2025年2月3日

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  • Amira Bouamrane,
  • Makhlouf Derdour,
  • Ahmed Alksas,
  • Sohail Contractor,
  • Mohamed Ghazal,
  • Ayman El-Baz

摘要

Lung cancer remains a critical global health challenge, with its prognosis heavily dependent on the timing of diagnosis. This literature review critically examines Artificial Intelligence and Computer-Aided Diagnosis (CADx) systems for lung cancer detection using Computed Tomography (CT) images, guided by seven pivotal research questions. Adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards and focusing on high-impact studies from 2013 to 2023, we provide an exhaustive assessment of current methodologies, underscore the variety and efficacy of algorithms and datasets, and evaluate preprocessing and performance evaluation strategies. Our findings reveal significant advancements in integrating machine learning and deep learning techniques, highlighting the importance of machine learning and deep learning methods and scrutinizing their goals, strengths, and limitations. Through a comprehensive meta-analysis, we offer insights into the state-of-the-art in lung cancer CADx, emphasizing data handling, model robustness, and avenues for enhancing diagnostic accuracy and reliability. This review not only critically relates varied methodologies and validates them against established metrics but also offers insights into future research trajectories aimed at enhancing early and accurate lung cancer diagnosis, thereby markedly improving patient outcomes. Targeting broad audiences, from experts in biomedical engineering to those across engineering and clinical sciences, we pave the way for future innovations in this vital domain.