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Artificial Intelligence Tools for Refining Lung Cancer Screening.

J Luis Espinoza1,2, Le Thanh Dong3

  • 1Global Health Unit, Faculty of Health Sciences, Kanazawa University, Kanazawa 920-0942, Ishikawa, Japan.

Journal of Clinical Medicine
|December 2, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) shows promise in improving lung cancer screening by analyzing low-dose computed tomography (LDCT) scans. AI models can enhance diagnostic accuracy and reduce false positives, aiding early lung cancer detection.

Keywords:
artificial intelligence and lung cancercomputers assisted diagnosisearly cancer diagnosislung cancer imaginglung cancer screening

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Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Lung cancer is the leading cause of cancer death globally, with early diagnosis crucial for survival.
  • Low-dose computed tomography (LDCT) screening improves early detection but suffers from a high false-positive rate.
  • Artificial intelligence (AI) offers advanced capabilities in data analysis and pattern recognition for medical imaging.

Purpose of the Study:

  • To review recent advancements in AI algorithms for lung cancer screening using chest CT scans.
  • To evaluate the potential of AI in improving the accuracy of lung nodule detection and reducing false positives in LDCT screening.
  • To discuss how AI can assist clinicians in interpreting LDCT images for lung cancer screening.

Main Methods:

  • Review of recent publications on AI algorithms applied to lung cancer screening.
  • Analysis of AI model performance in distinguishing benign from malignant lung nodules on CT scans.
  • Evaluation of AI's impact on diagnostic accuracy and false-positive rates in LDCT screening.

Main Results:

  • Several AI models have demonstrated performance comparable to or exceeding experienced radiologists in nodule characterization.
  • AI algorithms have shown potential to improve diagnostic accuracy in lung cancer screening.
  • Some AI models have successfully decreased the false-positive rate associated with LDCT screening.

Conclusions:

  • AI holds significant potential to enhance lung cancer screening by improving the interpretation of LDCT scans.
  • AI-powered tools can aid clinicians in making more accurate diagnoses and reducing unnecessary interventions.
  • Continued research and development of AI are crucial for optimizing lung cancer early detection strategies.