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SUMMARY:Testing breast cancer mammography screening artificial intelligenc
 e algorithms using the Cambridge Cohort database\; study design\, methods 
 and statistical analysis - Dr. Sarah Hickman
DTSTART:20210727T120000Z
DTEND:20210727T130000Z
UID:TALK161494@talks.cam.ac.uk
CONTACT:J.W.Stevens
DESCRIPTION:Two-million women aged between 50-70 are screened for breast c
 ancer every year in the UK and each mammogram is read by two expert reader
 s. Screening is therefore a labour-intensive repetitive task which could b
 e improved through the use of Artificial Intelligence (AI)\, to automate s
 creen reading or through priority triage of cases that could be cancer. Wi
 th the aim of improving patient outcomes and screening efficiency.\n\nTher
 e are now over five FDA approved algorithms as well as numerous academic a
 lgorithms that have been developed for either computer aided detection and
  diagnosis (CADe and x) or computer aided triage (CADt) approaches. Howeve
 r\, all the current literature is from retrospective studies using cancer 
 enriched cohorts\, with limited research investigating the use of AI in th
 e UK screening programme. We have created a mammographic imaging database 
 which will be used to independently and systematically test mammography AI
  algorithms from institutions world-wide to evaluate performance as well a
 s provide data for ongoing development and prospective testing.\n\nThis se
 minar will cover the study design\, methods and statistical analysis plann
 ed for future testing using this database as well as provide a forum to di
 scuss key areas of analysis that should be addressed as part of this testi
 ng.\n\nJoin Zoom Meeting\nhttps://maths-cam-ac-uk.zoom.us/j/92575403744?pw
 d=RHhqWC9wcUVWQi9xSzc1UE9BVGk3Zz09\n\nMeeting ID: 925 7540 3744\nPasscode:
  974971\n
LOCATION:Virtual (see abstract for Zoom link)
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