Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation
Objective: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. Methods: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a m...
Published in: | CURRENT MEDICAL IMAGING |
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Main Authors: | , , , , |
Format: | Article; Early Access |
Language: | English |
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BENTHAM SCIENCE PUBL LTD
2024
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001178516800001 |
author |
Hamid Marlina Tanty Ramli; Mumin Nazimah A. B.; Hamid Shamsiah Abdul; Rahmat Kartini |
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Hamid Marlina Tanty Ramli; Mumin Nazimah A. B.; Hamid Shamsiah Abdul; Rahmat Kartini Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation Radiology, Nuclear Medicine & Medical Imaging |
author_facet |
Hamid Marlina Tanty Ramli; Mumin Nazimah A. B.; Hamid Shamsiah Abdul; Rahmat Kartini |
author_sort |
Hamid |
spelling |
Hamid, Marlina Tanty Ramli; Mumin, Nazimah A. B.; Hamid, Shamsiah Abdul; Rahmat, Kartini Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation CURRENT MEDICAL IMAGING English Article; Early Access Objective: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. Methods: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results. Results Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (kappa =0.606, p < 0.001) and the BI-RADS category with and without AI (kappa =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively. Conclusion: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients. BENTHAM SCIENCE PUBL LTD 1573-4056 1875-6603 2024 10.2174/0115734056280191231207052903 Radiology, Nuclear Medicine & Medical Imaging WOS:001178516800001 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001178516800001 |
title |
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation |
title_short |
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation |
title_full |
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation |
title_fullStr |
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation |
title_full_unstemmed |
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation |
title_sort |
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation |
container_title |
CURRENT MEDICAL IMAGING |
language |
English |
format |
Article; Early Access |
description |
Objective: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. Methods: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results. Results Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (kappa =0.606, p < 0.001) and the BI-RADS category with and without AI (kappa =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively. Conclusion: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients. |
publisher |
BENTHAM SCIENCE PUBL LTD |
issn |
1573-4056 1875-6603 |
publishDate |
2024 |
container_volume |
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container_issue |
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doi_str_mv |
10.2174/0115734056280191231207052903 |
topic |
Radiology, Nuclear Medicine & Medical Imaging |
topic_facet |
Radiology, Nuclear Medicine & Medical Imaging |
accesstype |
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id |
WOS:001178516800001 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001178516800001 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809678795853004800 |