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CATEGORIES:CMIH Hub seminar series
SUMMARY:Testing breast cancer mammography screening artifi
 cial intelligence algorithms using the Cambridge C
 ohort database\; study design\, methods and statis
 tical analysis - Dr. Sarah Hickman
DTSTART;TZID=Europe/London:20210727T130000
DTEND;TZID=Europe/London:20210727T140000
UID:TALK161494AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/161494
DESCRIPTION:Two-million women aged between 50-70 are screened 
 for breast cancer every year in the UK and each ma
 mmogram is read by two expert readers. Screening i
 s therefore a labour-intensive repetitive task whi
 ch could be improved through the use of Artificial
  Intelligence (AI)\, to automate screen reading or
  through priority triage of cases that could be ca
 ncer. With the aim of improving patient outcomes a
 nd screening efficiency.\n\nThere are now over fiv
 e FDA approved algorithms as well as numerous acad
 emic algorithms that have been developed for eithe
 r computer aided detection and diagnosis (CADe and
  x) or computer aided triage (CADt) approaches. Ho
 wever\, all the current literature is from retrosp
 ective studies using cancer enriched cohorts\, wit
 h limited research investigating the use of AI in 
 the UK screening programme. We have created a mamm
 ographic imaging database which will be used to in
 dependently and systematically test mammography AI
  algorithms from institutions world-wide to evalua
 te performance as well as provide data for ongoing
  development and prospective testing.\n\nThis semi
 nar will cover the study design\, methods and stat
 istical analysis planned for future testing using 
 this database as well as provide a forum to discus
 s key areas of analysis that should be addressed a
 s part of this testing.\n\nJoin Zoom Meeting\nhttp
 s://maths-cam-ac-uk.zoom.us/j/92575403744?pwd=RHhq
 WC9wcUVWQi9xSzc1UE9BVGk3Zz09\n\nMeeting ID: 925 75
 40 3744\nPasscode: 974971\n
LOCATION:Virtual (see abstract for Zoom link)
CONTACT:J.W.Stevens
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