PRISM
Funded research projectPRISM: Prioritising Allocation of Scarce Perinatal Pathology Resource with ML-Assisted Placenta Pathology Screening
Research lead
Project core team
Professor Owen Arthurs
Dr Telmo de Menezes e Silva Filho
Dr Carolina Fuentes Toro
Shiren Patel
Professor Neil Sebire
Dr Guanxiong Sun
Hub Research theme
Service and resource planning
Background
Approximately a third of pregnancies experience complications that can result in restricted growth of an unborn baby, premature birth, stillbirth or maternal death. In these circumstances, a placenta will be reviewed by a specialist pathologist to determine underlying causes and inform clinical care so as to reduce risk in future pregnancies. However, a nationwide shortage of specialist pathologists is leading to huge delays and backlogs in analysing placentas, compromising care that could minimise risk of harm to mother and baby in future pregnancies.
Intended outcomes
To address this resource capacity shortage, Professor O’Hara’s team will develop an artificial intelligence (AI) triage system for digital placenta pathology that pre-screens placentas for normality / abnormalities and prioritises high-risk cases for review by the pathologist.
The system will significantly reduce perinatal pathology workload, enabling faster review of priority cases, and guiding treatment that mitigates risk to mother and baby in future pregnancies.
Project blog posts
Behind the research – PRISM
In this 'Behind the Research' series post, we spoke to Kenton O'Hara, Professor of Human-Computer Interaction from the University of Bristol about his project PRISM: Prioritising Allocation of Scarce Perinatal Pathology Resource with ML-Assisted Placenta Pathology...