Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review - Takeaways - MDSpire

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review

  • By

  • Ting-Wei Wang

  • Jia-Sheng Hong

  • Hwa-Yen Chiu

  • Heng-Sheng Chao

  • Yuh-Min Chen

  • Yu-Te Wu

  • May 22, 2024

  • 0 min

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  • 1

    Lung cancer is the leading cause of cancer death globally, with 2.38 million new cases and 1.27 million deaths estimated in 2023.

  • 2

    Deep learning models, particularly convolutional neural networks, can enhance the accuracy of lung cancer diagnoses from chest CT scans.

  • 3

    The systematic review aimed to compare the diagnostic accuracy of deep learning models with expert radiologists in lung cancer detection.

  • 4

    AI integration in radiology can reduce diagnostic errors and alleviate radiologists' workload, improving patient care.

  • 5

    The study adhered to PRISMA guidelines and utilized various statistical analyses to evaluate the performance of DL algorithms and human experts.

Original Source(s)

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review

  • by Ting-Wei Wang, Jia-Sheng Hong, Hwa-Yen Chiu, Heng-Sheng Chao, Yuh-Min Chen, Yu-Te Wu

  • May 22, 2024

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