id:"swepub:oai:lup.lub.lu.se:140af394-6390-41fe-a3b7-e438191af2c1"
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- Lång, KristinaLund 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 (författare)
Identifying normal mammograms in a large screening population using artificial intelligence
- Artikel/kapitelEngelska2020
Förlag, utgivningsår, omfång ...
- 2020-09-02
- Springer Science and Business Media LLC,2020
Nummerbeteckningar
- LIBRIS-ID:oai:lup.lub.lu.se:140af394-6390-41fe-a3b7-e438191af2c1
- https://lup.lub.lu.se/record/140af394-6390-41fe-a3b7-e438191af2c1URI
- https://doi.org/10.1007/s00330-020-07165-1DOI
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- Språk:engelska
- Sammanfattning på:engelska
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- SwePubSwePub
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- Objectives: To evaluate the potential of artificial intelligence (AI) to identify normal mammograms in a screening population. Methods: In this retrospective study, 9581 double-read mammography screening exams including 68 screen-detected cancers and 187 false positives, a subcohort of the prospective population-based Malmö Breast Tomosynthesis Screening Trial, were analysed with a deep learning–based AI system. The AI system categorises mammograms with a cancer risk score increasing from 1 to 10. The effect on cancer detection and false positives of excluding mammograms below different AI risk thresholds from reading by radiologists was investigated. A panel of three breast radiologists assessed the radiographic appearance, type, and visibility of screen-detected cancers assigned low-risk scores (≤ 5). The reduction of normal exams, cancers, and false positives for the different thresholds was presented with 95% confidence intervals (CI). Results: If mammograms scored 1 and 2 were excluded from screen-reading, 1829 (19.1%; 95% CI 18.3–19.9) exams could be removed, including 10 (5.3%; 95% CI 2.1–8.6) false positives but no cancers. In total, 5082 (53.0%; 95% CI 52.0–54.0) exams, including 7 (10.3%; 95% CI 3.1–17.5) cancers and 52 (27.8%; 95% CI 21.4–34.2) false positives, had low-risk scores. All, except one, of the seven screen-detected cancers with low-risk scores were judged to be clearly visible. Conclusions: The evaluated AI system can correctly identify a proportion of a screening population as cancer-free and also reduce false positives. Thus, AI has the potential to improve mammography screening efficiency. Key Points: • Retrospective study showed that AI can identify a proportion of mammograms as normal in a screening population. • Excluding normal exams from screening using AI can reduce false positives.
Ämnesord och genrebeteckningar
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
- Artificial intelligence
- Breast cancer
- Mammography
- Mass screening
Biuppslag (personer, institutioner, konferenser, titlar ...)
- Dustler, MagnusLund 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(Swepub:lu)med-mdu (författare)
- Dahlblom, VictorLund 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(Swepub:lu)vi3480da (författare)
- Åkesson, AnnaSkåne University Hospital(Swepub:lu)med-aa7 (författare)
- Andersson, IngvarLund 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(Swepub:lu)ront-ian (författare)
- Zackrisson, SophiaLund 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(Swepub:lu)smi-sza (författare)
- Diagnostisk radiologi, MalmöForskargrupper vid Lunds universitet (creator_code:org_t)
Sammanhörande titlar
- Ingår i:European Radiology: Springer Science and Business Media LLC0938-79941432-1084
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