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Correspondence between areas causing recall in breast cancer screening and artificial intelligence findings
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- Dahlblom, Victor (author)
- Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital
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- Tingberg, Anders (author)
- Lund University,Lunds universitet,Medicinsk strålningsfysik, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Medical Radiation Physics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital
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- Zackrisson, Sophia (author)
- Lund University,Lunds universitet,Diagnostisk radiologi, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,LTH profilområde: Avancerade ljuskällor,LTH profilområden,Lunds Tekniska Högskola,Radiology Diagnostics, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,LTH Profile Area: Photon Science and Technology,LTH Profile areas,Faculty of Engineering, LTH,Skåne University Hospital
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- Bosmans, Hilde (editor)
- Marshall, Nicholas (editor)
- Van Ongeval, Chantal (editor)
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- (creator_code:org_t)
- SPIE, 2022
- 2022
- English.
- In: 16th International Workshop on Breast Imaging, IWBI 2022. - : SPIE. - 0277-786X .- 1996-756X. - 9781510655843 ; 12286
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- False positive recall is a major issue in breast cancer screening and the introduction of artificial intelligence (AI) might affect which women who are unnecessarily recalled. We have investigated how an AI system works on false positive recalls at screening and compared with radiologist findings. Two-view digital mammography (DM) examinations from 656 recalled women (136 with screening detected cancer), were analysed with a commercial AI system. The AI findings were matched with the areas on the images causing the recalls. The agreement was studied both at the examination level and for individual findings. Scores were compared between true positive and false positive recalls. ROC analysis was used to study the AI-system's ability to distinguish between true and false positive recalls. It was also studied how the AI system performed on cases where there were discordant readings. AI identified the same areas as radiologists in 80% of the cases recalled on DM. For true positives both the proportion of matching areas and AI scores were higher than for false positive recalls. The AI system also had a relatively large AUC (0.83) for differentiating between false positive recalls and cancers. Further, the AI system identified most of the findings leading to recall in cases where only one of the readers had marked the case for discussion. There is a relatively large agreement between the AI system and radiologists. The AI system scores the false positives lower than true positives. AI complements a single reader in a way similar to a second reader.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- artificial intelligence
- breast cancer screening
- False positive recalls
Publication and Content Type
- kon (subject category)
- ref (subject category)
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