Application of Artificial Intelligence in Lung Cancer Screening
Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However, several challenges must be overcome, to ensure the implementation of lung cancer screening, which inc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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The Korean Society of Radiology
2019-09-01
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| Series: | 대한영상의학회지 |
| Subjects: | |
| Online Access: | https://doi.org/10.3348/jksr.2019.80.5.872 |
| _version_ | 1818837538024980480 |
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| author | Sang Min Lee Chang Min Park |
| author_facet | Sang Min Lee Chang Min Park |
| author_sort | Sang Min Lee |
| collection | DOAJ |
| description | Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However, several challenges must be overcome, to ensure the implementation of lung cancer screening, which include a large number of expected low-dose CT examinations and relative
shortage of experienced radiologists for interpreting them. The use of artificial intelligence has
garnered attention in this regard. A deep learning technique, which is a subclass of machine
learning methods, involving the learning of data representations in an end-to-end manner, has
already demonstrated outstanding performance in medical image analysis. Several studies are
exploring the possibility of deep learning-based applications in medical domains, including radiology. In lung cancer screening, computer-aided detection, report generation, prediction of malignancy in the detected nodules, and prognosis prediction can be considered for the application
of artificial intelligence. This article will cover the current status of deep learning approaches,
their limitations, and their potential in lung cancer screening programs. |
| first_indexed | 2024-12-19T03:24:05Z |
| format | Article |
| id | doaj.art-89c8d57d2f3147e9a330007bb26701b5 |
| institution | Directory Open Access Journal |
| issn | 1738-2637 2288-2928 |
| language | English |
| last_indexed | 2024-12-19T03:24:05Z |
| publishDate | 2019-09-01 |
| publisher | The Korean Society of Radiology |
| record_format | Article |
| series | 대한영상의학회지 |
| spelling | doaj.art-89c8d57d2f3147e9a330007bb26701b52022-12-21T20:37:40ZengThe Korean Society of Radiology대한영상의학회지1738-26372288-29282019-09-01805872879https://doi.org/10.3348/jksr.2019.80.5.872Application of Artificial Intelligence in Lung Cancer ScreeningSang Min Lee0Chang Min Park1Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, KoreaDepartment of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, KoreaLung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However, several challenges must be overcome, to ensure the implementation of lung cancer screening, which include a large number of expected low-dose CT examinations and relative shortage of experienced radiologists for interpreting them. The use of artificial intelligence has garnered attention in this regard. A deep learning technique, which is a subclass of machine learning methods, involving the learning of data representations in an end-to-end manner, has already demonstrated outstanding performance in medical image analysis. Several studies are exploring the possibility of deep learning-based applications in medical domains, including radiology. In lung cancer screening, computer-aided detection, report generation, prediction of malignancy in the detected nodules, and prognosis prediction can be considered for the application of artificial intelligence. This article will cover the current status of deep learning approaches, their limitations, and their potential in lung cancer screening programs.https://doi.org/10.3348/jksr.2019.80.5.872lung neoplasmsscreeningcomputed tomographyx-rayartificial intelligence |
| spellingShingle | Sang Min Lee Chang Min Park Application of Artificial Intelligence in Lung Cancer Screening 대한영상의학회지 lung neoplasms screening computed tomography x-ray artificial intelligence |
| title | Application of Artificial Intelligence in Lung Cancer Screening |
| title_full | Application of Artificial Intelligence in Lung Cancer Screening |
| title_fullStr | Application of Artificial Intelligence in Lung Cancer Screening |
| title_full_unstemmed | Application of Artificial Intelligence in Lung Cancer Screening |
| title_short | Application of Artificial Intelligence in Lung Cancer Screening |
| title_sort | application of artificial intelligence in lung cancer screening |
| topic | lung neoplasms screening computed tomography x-ray artificial intelligence |
| url | https://doi.org/10.3348/jksr.2019.80.5.872 |
| work_keys_str_mv | AT sangminlee applicationofartificialintelligenceinlungcancerscreening AT changminpark applicationofartificialintelligenceinlungcancerscreening |