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VAI-B: A multicente...
VAI-B: A multicenter platform for the external validation of artificial intelligence algorithms in breast imaging
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- Cossío, Fernando (författare)
- Karolinska Institutet,Karolinska Institute,Karolinska Institute Department of Oncology-Pathology Stockholm Sweden; Karolinska University Hospital Department of Radiology Stockholm Sweden
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- Schurz, Haiko (författare)
- Karolinska Institutet,Karolinska Institute,Karolinska Institute Department of Oncology-Pathology Stockholm Sweden
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- Engström, Mathias (författare)
- Collective Minds Radiology Stockholm Sweden
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- Barck-Holst, Carl (författare)
- West Code Group Stockholm Sweden
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- Tsirikoglou, Apostolia (författare)
- Karolinska Institutet,Karolinska Institute,Karolinska Institute Department of Oncology-Pathology Stockholm Sweden
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- Lundström, Claes (författare)
- Linköping University,Linköping University Center for Medical Image Science and Visualization (CMIV) Linköping Sweden
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- Gustafsson, Håkan (författare)
- Linköping University,Linköping University Center for Medical Image Science and Visualization (CMIV) Linköping Sweden; Linköping University Department of Medical Radiation Physics Department of Health Medicine and Caring Sciences Linköping Sweden
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- Smith, Kevin, 1975- (författare)
- KTH Royal Institute of Technology,KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Zackrisson, Sophia (författare)
- 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,LU profilområde: Ljus och material,Lunds universitets profilområden,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,LU Profile Area: Light and Materials,Lund University Profile areas,Lund University Department of Diagnostic Radiology Translational Medicine Malmö Sweden; Skåne University Hospital Department of Imaging and Physiology Malmö Sweden
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- Strand, Fredrik (författare)
- Karolinska Institutet,Karolinska Institute,Karolinska Institute Department of Oncology-Pathology Stockholm Sweden; Karolinska University Hospital Department of Radiology Stockholm Sweden
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(creator_code:org_t)
- SPIE-Intl Soc Optical Eng, 2023
- 2023
- Engelska.
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Ingår i: Journal of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 2329-4302 .- 2329-4310. ; 10:6
- Relaterad länk:
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https://doi.org/10.1...
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http://dx.doi.org/10... (free)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://lup.lub.lu.s...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Purpose: Multiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data. Approach: We developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data onpremises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes. Results: To pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database. Conclusions: We have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Nyckelord
- breast cancer
- data management
- machine learning
- mammography
- validation
- breast cancer
- data management
- machine learning
- mammography
- validation
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Cossío, Fernando
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Schurz, Haiko
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Engström, Mathia ...
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Barck-Holst, Car ...
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Tsirikoglou, Apo ...
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Lundström, Claes
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visa fler...
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Gustafsson, Håka ...
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Smith, Kevin, 19 ...
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Zackrisson, Soph ...
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Strand, Fredrik
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Radiologi och bi ...
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Cancer och onkol ...
- Artiklar i publikationen
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Journal of Medic ...
- Av lärosätet
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Kungliga Tekniska Högskolan
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Lunds universitet
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Karolinska Institutet