The added value of using artificial intelligence in adult chest X-rays for nodules and masses detection in daily radiology practice

Background Pulmonary nodule detection in CXR is challenging. Recently, the use of artificial intelligence (AI) has been a major attraction. The current study aimed to evaluate the diagnostic performance of the AI in the detection of pulmonary nodules or masses on CXR compared to the radiologist’s in...

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Veröffentlicht in:Egyptian Journal of Radiology and Nuclear Medicine 2023-12, Vol.54 (1), p.142-11, Article 142
Hauptverfasser: Farouk, Suzan, Osman, Ahmed M., Awadallah, Shrouk M., Abdelrahman, Ahmed S.
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container_issue 1
container_start_page 142
container_title Egyptian Journal of Radiology and Nuclear Medicine
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creator Farouk, Suzan
Osman, Ahmed M.
Awadallah, Shrouk M.
Abdelrahman, Ahmed S.
description Background Pulmonary nodule detection in CXR is challenging. Recently, the use of artificial intelligence (AI) has been a major attraction. The current study aimed to evaluate the diagnostic performance of the AI in the detection of pulmonary nodules or masses on CXR compared to the radiologist’s interpretation and to assess its impact on the reporting process. The current study included 150 patients who had CXR interpreted by radiologists and by AI software. Results CT detected pulmonary nodules in 99 cases (66%) while the visual model of analysis, as well as AI, detected nodules among 92 cases (61.3%) compared to 93 (62%) cases detected by combined visual/AI model. A total of 216 nodules were detected by CT (64.4% solid and 31.5% GG). Only 188 nodules were detected by the AI while 170 nodules were detected by visual analysis. As per case classification or nodule analysis, the AI showed the highest area under curve (AUC) (0.890, 95% CI) and (0.875, 95% CI), respectively, followed by the combined visual/AI model. Regarding the nodules’ texture, the AI model’s sensitivity for solid nodules was 91.4% which was greater than the combined visual/AI and visual models alone, while in GG nodules, the combined visual/AI model’s sensitivity was higher than the AI and visual models. The probability score using the combined visual/AI model was significantly higher than using the visual model alone ( P value = 0.001). Conclusions The use of the AI model in CXR interpretation regarding nodules and masses detection helps in more accurate decision-making and increases the diagnostic performance affecting the patient’s morbidity and mortality.
doi_str_mv 10.1186/s43055-023-01093-y
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Recently, the use of artificial intelligence (AI) has been a major attraction. The current study aimed to evaluate the diagnostic performance of the AI in the detection of pulmonary nodules or masses on CXR compared to the radiologist’s interpretation and to assess its impact on the reporting process. The current study included 150 patients who had CXR interpreted by radiologists and by AI software. Results CT detected pulmonary nodules in 99 cases (66%) while the visual model of analysis, as well as AI, detected nodules among 92 cases (61.3%) compared to 93 (62%) cases detected by combined visual/AI model. A total of 216 nodules were detected by CT (64.4% solid and 31.5% GG). Only 188 nodules were detected by the AI while 170 nodules were detected by visual analysis. As per case classification or nodule analysis, the AI showed the highest area under curve (AUC) (0.890, 95% CI) and (0.875, 95% CI), respectively, followed by the combined visual/AI model. Regarding the nodules’ texture, the AI model’s sensitivity for solid nodules was 91.4% which was greater than the combined visual/AI and visual models alone, while in GG nodules, the combined visual/AI model’s sensitivity was higher than the AI and visual models. The probability score using the combined visual/AI model was significantly higher than using the visual model alone ( P value = 0.001). Conclusions The use of the AI model in CXR interpretation regarding nodules and masses detection helps in more accurate decision-making and increases the diagnostic performance affecting the patient’s morbidity and mortality.</description><identifier>ISSN: 2090-4762</identifier><identifier>ISSN: 0378-603X</identifier><identifier>EISSN: 2090-4762</identifier><identifier>DOI: 10.1186/s43055-023-01093-y</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Artificial intelligence ; Artificial intelligence (AI) ; Calcification ; Chest X-ray (CXR) ; Clinical medicine ; Comparative analysis ; Confidence ; Deep learning ; Drug dosages ; FDA approval ; Imaging ; Interventional Radiology ; Lung cancer ; Lung diseases ; Masses ; Medical research ; Medicine ; Medicine &amp; Public Health ; Medicine, Experimental ; Neural networks ; Nodules ; Nuclear Medicine ; Patients ; Pleural effusion ; Radiology ; Software</subject><ispartof>Egyptian Journal of Radiology and Nuclear Medicine, 2023-12, Vol.54 (1), p.142-11, Article 142</ispartof><rights>The Author(s) 2023</rights><rights>COPYRIGHT 2023 Springer</rights><rights>The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c447t-db3cb247c342cad9c5ec5cf4e5840514a3326350114680bb836f9fff0b51c2ca3</cites><orcidid>0000-0002-3458-4396 ; 0000-0003-3682-0062 ; 0000-0002-1244-1001 ; 0000-0001-6491-3739</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,864,27924,27925</link.rule.ids></links><search><creatorcontrib>Farouk, Suzan</creatorcontrib><creatorcontrib>Osman, Ahmed M.</creatorcontrib><creatorcontrib>Awadallah, Shrouk M.</creatorcontrib><creatorcontrib>Abdelrahman, Ahmed S.</creatorcontrib><title>The added value of using artificial intelligence in adult chest X-rays for nodules and masses detection in daily radiology practice</title><title>Egyptian Journal of Radiology and Nuclear Medicine</title><addtitle>Egypt J Radiol Nucl Med</addtitle><description>Background Pulmonary nodule detection in CXR is challenging. Recently, the use of artificial intelligence (AI) has been a major attraction. The current study aimed to evaluate the diagnostic performance of the AI in the detection of pulmonary nodules or masses on CXR compared to the radiologist’s interpretation and to assess its impact on the reporting process. The current study included 150 patients who had CXR interpreted by radiologists and by AI software. Results CT detected pulmonary nodules in 99 cases (66%) while the visual model of analysis, as well as AI, detected nodules among 92 cases (61.3%) compared to 93 (62%) cases detected by combined visual/AI model. A total of 216 nodules were detected by CT (64.4% solid and 31.5% GG). Only 188 nodules were detected by the AI while 170 nodules were detected by visual analysis. As per case classification or nodule analysis, the AI showed the highest area under curve (AUC) (0.890, 95% CI) and (0.875, 95% CI), respectively, followed by the combined visual/AI model. Regarding the nodules’ texture, the AI model’s sensitivity for solid nodules was 91.4% which was greater than the combined visual/AI and visual models alone, while in GG nodules, the combined visual/AI model’s sensitivity was higher than the AI and visual models. The probability score using the combined visual/AI model was significantly higher than using the visual model alone ( P value = 0.001). 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Osman, Ahmed M. ; Awadallah, Shrouk M. ; Abdelrahman, Ahmed S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c447t-db3cb247c342cad9c5ec5cf4e5840514a3326350114680bb836f9fff0b51c2ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Artificial intelligence (AI)</topic><topic>Calcification</topic><topic>Chest X-ray (CXR)</topic><topic>Clinical medicine</topic><topic>Comparative analysis</topic><topic>Confidence</topic><topic>Deep learning</topic><topic>Drug dosages</topic><topic>FDA approval</topic><topic>Imaging</topic><topic>Interventional Radiology</topic><topic>Lung cancer</topic><topic>Lung diseases</topic><topic>Masses</topic><topic>Medical research</topic><topic>Medicine</topic><topic>Medicine &amp; Public Health</topic><topic>Medicine, Experimental</topic><topic>Neural networks</topic><topic>Nodules</topic><topic>Nuclear Medicine</topic><topic>Patients</topic><topic>Pleural effusion</topic><topic>Radiology</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Farouk, Suzan</creatorcontrib><creatorcontrib>Osman, Ahmed M.</creatorcontrib><creatorcontrib>Awadallah, Shrouk M.</creatorcontrib><creatorcontrib>Abdelrahman, Ahmed S.</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health &amp; 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Recently, the use of artificial intelligence (AI) has been a major attraction. The current study aimed to evaluate the diagnostic performance of the AI in the detection of pulmonary nodules or masses on CXR compared to the radiologist’s interpretation and to assess its impact on the reporting process. The current study included 150 patients who had CXR interpreted by radiologists and by AI software. Results CT detected pulmonary nodules in 99 cases (66%) while the visual model of analysis, as well as AI, detected nodules among 92 cases (61.3%) compared to 93 (62%) cases detected by combined visual/AI model. A total of 216 nodules were detected by CT (64.4% solid and 31.5% GG). Only 188 nodules were detected by the AI while 170 nodules were detected by visual analysis. As per case classification or nodule analysis, the AI showed the highest area under curve (AUC) (0.890, 95% CI) and (0.875, 95% CI), respectively, followed by the combined visual/AI model. Regarding the nodules’ texture, the AI model’s sensitivity for solid nodules was 91.4% which was greater than the combined visual/AI and visual models alone, while in GG nodules, the combined visual/AI model’s sensitivity was higher than the AI and visual models. The probability score using the combined visual/AI model was significantly higher than using the visual model alone ( P value = 0.001). Conclusions The use of the AI model in CXR interpretation regarding nodules and masses detection helps in more accurate decision-making and increases the diagnostic performance affecting the patient’s morbidity and mortality.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1186/s43055-023-01093-y</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3458-4396</orcidid><orcidid>https://orcid.org/0000-0003-3682-0062</orcidid><orcidid>https://orcid.org/0000-0002-1244-1001</orcidid><orcidid>https://orcid.org/0000-0001-6491-3739</orcidid><oa>free_for_read</oa></addata></record>
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subjects Algorithms
Artificial intelligence
Artificial intelligence (AI)
Calcification
Chest X-ray (CXR)
Clinical medicine
Comparative analysis
Confidence
Deep learning
Drug dosages
FDA approval
Imaging
Interventional Radiology
Lung cancer
Lung diseases
Masses
Medical research
Medicine
Medicine & Public Health
Medicine, Experimental
Neural networks
Nodules
Nuclear Medicine
Patients
Pleural effusion
Radiology
Software
title The added value of using artificial intelligence in adult chest X-rays for nodules and masses detection in daily radiology practice
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