Cargando…

Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow

PURPOSE: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. METHODS: From January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening f...

Descripción completa

Detalles Bibliográficos
Autores principales: Yoon, Jung Hyun, Han, Kyungwha, Suh, Hee Jung, Youk, Ji Hyun, Lee, Si Eun, Kim, Eun-Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362167/
https://www.ncbi.nlm.nih.gov/pubmed/37484980
http://dx.doi.org/10.1016/j.ejro.2023.100509
_version_ 1785076364171280384
author Yoon, Jung Hyun
Han, Kyungwha
Suh, Hee Jung
Youk, Ji Hyun
Lee, Si Eun
Kim, Eun-Kyung
author_facet Yoon, Jung Hyun
Han, Kyungwha
Suh, Hee Jung
Youk, Ji Hyun
Lee, Si Eun
Kim, Eun-Kyung
author_sort Yoon, Jung Hyun
collection PubMed
description PURPOSE: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. METHODS: From January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening facility. Historic reads of three radiologists were used as radiologist interpretation. A commercially-available AI-CAD was used for analysis. One radiologist not involved in interpretation had retrospectively reviewed the abnormality features and assessed the significance (negligible vs. need recall) of the AI-CAD marks. Ground truth in terms of cancer, benign or absence of abnormality was confirmed according to histopathologic diagnosis or negative results on the next-round screen. RESULTS: Of the 6499 mammograms, 6282 (96.7%) were in the negative, 189 (2.9%) were in the benign, and 28 (0.4%) were in the cancer group. AI-CAD detected 5 (17.9%, 5 of 28) of the 9 cancers that were intially interpreted as negative. Of the 648 AI-CAD recalls, 89.0% (577 of 648) were marks seen on examinations in the negative group, and 267 (41.2%) of the AI-CAD marks were considered to be negligible. Stand-alone AI-CAD has significantly higher recall rates (10.0% vs. 3.4%, P < 0.001) with comparable sensitivity and cancer detection rates (P = 0.086 and 0.102, respectively) when compared to the radiologists’ interpretation. CONCLUSION: AI-CAD detected 17.9% additional cancers on screening mammography that were initially overlooked by the radiologists. In spite of the additional cancer detection, AI-CAD had significantly higher recall rates in the clinical workflow, in which 89.0% of AI-CAD marks are on negative mammograms.
format Online
Article
Text
id pubmed-10362167
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-103621672023-07-23 Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow Yoon, Jung Hyun Han, Kyungwha Suh, Hee Jung Youk, Ji Hyun Lee, Si Eun Kim, Eun-Kyung Eur J Radiol Open Article PURPOSE: To evaluate the stand-alone diagnostic performances of AI-CAD and outcomes of AI-CAD detected abnormalities when applied to the mammographic interpretation workflow. METHODS: From January 2016 to December 2017, 6499 screening mammograms of 5228 women were collected from a single screening facility. Historic reads of three radiologists were used as radiologist interpretation. A commercially-available AI-CAD was used for analysis. One radiologist not involved in interpretation had retrospectively reviewed the abnormality features and assessed the significance (negligible vs. need recall) of the AI-CAD marks. Ground truth in terms of cancer, benign or absence of abnormality was confirmed according to histopathologic diagnosis or negative results on the next-round screen. RESULTS: Of the 6499 mammograms, 6282 (96.7%) were in the negative, 189 (2.9%) were in the benign, and 28 (0.4%) were in the cancer group. AI-CAD detected 5 (17.9%, 5 of 28) of the 9 cancers that were intially interpreted as negative. Of the 648 AI-CAD recalls, 89.0% (577 of 648) were marks seen on examinations in the negative group, and 267 (41.2%) of the AI-CAD marks were considered to be negligible. Stand-alone AI-CAD has significantly higher recall rates (10.0% vs. 3.4%, P < 0.001) with comparable sensitivity and cancer detection rates (P = 0.086 and 0.102, respectively) when compared to the radiologists’ interpretation. CONCLUSION: AI-CAD detected 17.9% additional cancers on screening mammography that were initially overlooked by the radiologists. In spite of the additional cancer detection, AI-CAD had significantly higher recall rates in the clinical workflow, in which 89.0% of AI-CAD marks are on negative mammograms. Elsevier 2023-07-11 /pmc/articles/PMC10362167/ /pubmed/37484980 http://dx.doi.org/10.1016/j.ejro.2023.100509 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Yoon, Jung Hyun
Han, Kyungwha
Suh, Hee Jung
Youk, Ji Hyun
Lee, Si Eun
Kim, Eun-Kyung
Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow
title Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow
title_full Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow
title_fullStr Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow
title_full_unstemmed Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow
title_short Artificial intelligence-based computer-assisted detection/diagnosis (AI-CAD) for screening mammography: Outcomes of AI-CAD in the mammographic interpretation workflow
title_sort artificial intelligence-based computer-assisted detection/diagnosis (ai-cad) for screening mammography: outcomes of ai-cad in the mammographic interpretation workflow
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362167/
https://www.ncbi.nlm.nih.gov/pubmed/37484980
http://dx.doi.org/10.1016/j.ejro.2023.100509
work_keys_str_mv AT yoonjunghyun artificialintelligencebasedcomputerassisteddetectiondiagnosisaicadforscreeningmammographyoutcomesofaicadinthemammographicinterpretationworkflow
AT hankyungwha artificialintelligencebasedcomputerassisteddetectiondiagnosisaicadforscreeningmammographyoutcomesofaicadinthemammographicinterpretationworkflow
AT suhheejung artificialintelligencebasedcomputerassisteddetectiondiagnosisaicadforscreeningmammographyoutcomesofaicadinthemammographicinterpretationworkflow
AT youkjihyun artificialintelligencebasedcomputerassisteddetectiondiagnosisaicadforscreeningmammographyoutcomesofaicadinthemammographicinterpretationworkflow
AT leesieun artificialintelligencebasedcomputerassisteddetectiondiagnosisaicadforscreeningmammographyoutcomesofaicadinthemammographicinterpretationworkflow
AT kimeunkyung artificialintelligencebasedcomputerassisteddetectiondiagnosisaicadforscreeningmammographyoutcomesofaicadinthemammographicinterpretationworkflow