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Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preproc...

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Autores principales: Chiu, Hwa-Yen, Peng, Rita Huan-Ting, Lin, Yi-Chian, Wang, Ting-Wei, Yang, Ya-Xuan, Chen, Ying-Ying, Wu, Mei-Han, Shiao, Tsu-Hui, Chao, Heng-Sheng, Chen, Yuh-Min, Wu, Yu-Te
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687210/
https://www.ncbi.nlm.nih.gov/pubmed/36359360
http://dx.doi.org/10.3390/biomedicines10112839
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author Chiu, Hwa-Yen
Peng, Rita Huan-Ting
Lin, Yi-Chian
Wang, Ting-Wei
Yang, Ya-Xuan
Chen, Ying-Ying
Wu, Mei-Han
Shiao, Tsu-Hui
Chao, Heng-Sheng
Chen, Yuh-Min
Wu, Yu-Te
author_facet Chiu, Hwa-Yen
Peng, Rita Huan-Ting
Lin, Yi-Chian
Wang, Ting-Wei
Yang, Ya-Xuan
Chen, Ying-Ying
Wu, Mei-Han
Shiao, Tsu-Hui
Chao, Heng-Sheng
Chen, Yuh-Min
Wu, Yu-Te
author_sort Chiu, Hwa-Yen
collection PubMed
description Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3–523) days, longer than that for radiologists (8 (0–263) days). The AI model can assist radiologists in the early detection of lung nodules.
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spelling pubmed-96872102022-11-25 Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images Chiu, Hwa-Yen Peng, Rita Huan-Ting Lin, Yi-Chian Wang, Ting-Wei Yang, Ya-Xuan Chen, Ying-Ying Wu, Mei-Han Shiao, Tsu-Hui Chao, Heng-Sheng Chen, Yuh-Min Wu, Yu-Te Biomedicines Article Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3–523) days, longer than that for radiologists (8 (0–263) days). The AI model can assist radiologists in the early detection of lung nodules. MDPI 2022-11-07 /pmc/articles/PMC9687210/ /pubmed/36359360 http://dx.doi.org/10.3390/biomedicines10112839 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chiu, Hwa-Yen
Peng, Rita Huan-Ting
Lin, Yi-Chian
Wang, Ting-Wei
Yang, Ya-Xuan
Chen, Ying-Ying
Wu, Mei-Han
Shiao, Tsu-Hui
Chao, Heng-Sheng
Chen, Yuh-Min
Wu, Yu-Te
Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images
title Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images
title_full Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images
title_fullStr Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images
title_full_unstemmed Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images
title_short Artificial Intelligence for Early Detection of Chest Nodules in X-ray Images
title_sort artificial intelligence for early detection of chest nodules in x-ray images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687210/
https://www.ncbi.nlm.nih.gov/pubmed/36359360
http://dx.doi.org/10.3390/biomedicines10112839
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