2022 | Excellent | Health | Ireland | SDG10 | SDG3 | SDG5
AI_PREMie (Artificial Intelligence to Prevent preterm birth due to preeclampsia while protecting Mothers’ lives)

Company or Institution

University College Dublin







Sustainable Development Goals (SDGs)

SDG 3: Good Health and Well-being

SDG 5: Gender Equality

SDG 10: Reduced Inequality

General description of the AI solution

Preeclampsia (PET) is a serious complication affecting one in every twelve pregnancies. PET claims the lives of 76,000 mothers and 500,000 babies annually, making it one of the world’s deadliest pregnancy complications.
AI_PREMie is a new decision-support solution for diagnosis and risk stratification in PET. It draws upon cutting-edge biomedical, clinical and machine learning know-how to solve this problem. First, it will remove diagnostic uncertainty by permitting accurate diagnosis of PET in an expectant mother between 20-40 weeks gestation, which can be challenging even for the most experienced of staff. Second, by also assisting in the prediction of the patient’s risk of progression to severe disease, it will enable timely treatment and may permit more accurate control over delivery, potentially allowing a baby to remain in utero for several more precious days, impacting their survival and their long-term neurodevelopmental health.
The AI_PREMie project has generated an easily interpretable decision support tool for diagnosis and risk stratification in PET that uses powerful ML algorithms to combine quantitative information from patented biomarker signals together with clinically-relevant maternal haematological/demographic/clinical assessment data. The team believes that AI_PREMie has the capacity to augment the clinical approach to care in the management of a hypertensive pregnant patient, in order to achieve the best possible outcomes for mother and baby.
Trialling AI_PREMie in three of Ireland’s largest and busiest maternity hospitals (The National Maternity Hospital, The Rotunda Hospital and The Coombe Hospital), representing 50% of all births in Ireland, has demonstrated compelling evidence that our technology works to accurately separate high-risk from low risk patients and will be used by care providers in a busy hospital setting. In short, AI_PREMie arms care providers with an affordable risk stratification tool to closely observe pregnancies complicated by preeclampsia, helping to prevent unnecessary adverse outcomes for mother and baby.


1. Rohlfing AK, Kolb K, Sigle M, Ziegler M, Bild A, Münzer P, Sudmann J, Dicenta V, Harm T, Manke MC, Geue S, Kremser M, Chatterjee M, Liang C, von Eysmondt H, Dandekar T, Heinzmann D, Günter M, von Ungern-Sternberg S, Büttcher M, Castor T, Mencl S, Langhauser F, Sies K, Ashour D, Beker MC, Lämmerhofer M, Autenrieth SE, Schäffer TE, Laufer S, Szklanna P, Maguire P, Heikenwalder M, Müller KAL, Hermann DM, Kilic E, Stumm R, Ramos G, Kleinschnitz C, Borst O, Langer HF, Rath D, Gawaz M. (2022) ACKR3 regulates platelet activation and ischemia-reperfusion tissue injury. Nat Commun. 13:1823. doi: 10.1038/s41467-022-29341-1. PMID: 35383158.

2. Szklanna PB, Altaie H, Comer SP, Cullivan S, Kelliher S, Weiss L, Curran J, Dowling E, O’Reilly KMA, Cotter AG, Marsh B, Gaine S, Power N, Lennon Á, McCullagh B, Ní Áinle F, Kevane B, Maguire PB. (2021) Routine Hematological Parameters May Be Predictors of COVID-19 Severity. Frontiers in Medicine, 8, 682843. PMID: 34336889.

3. Cullivan S, Murphy CA, Weiss L, Comer SP, Kevane B, McCullagh B, Maguire PB, Ní Ainle F, Gaine SP. (2021) Platelets, extracellular vesicles and coagulation in pulmonary arterial hypertension. Pulm Circ. 11:20458940211021036. PMID: 34158919.

4. Szklanna PB, Parsons ME, Wynne K, O'Connor H, Egan K, Allen S, Ní Áinle F, Maguire PB. (2019) The Platelet Releasate is Altered in Human Pregnancy. Proteomics Clin Appl. 13: e1800162. PMID: 30318839.

5. O'Gorman N, Wright D, Poon LC, Rolnik DL, Syngelaki A, de Alvarado M, Carbone IF, Dutemeyer V, Fiolna M, Frick A, Karagiotis N, Mastrodima S, de Paco Matallana C, Papaioannou G, Pazos A, Plasencia W, Nicolaides KH. (2017) Multicenter screening for pre‐eclampsia by maternal factors and biomarkers at 11–13 weeks' gestation: comparison with NICE guidelines and ACOG recommendations. Ultrasound in Obstetrics & Gynecology, 49(6), 756-760. PMID:28295782.



Public Exposure

Mentorship Program


International Research Centre
on Artificial Intelligence (IRCAI)
under the auspices of UNESCO 

Jožef Stefan Institute
Jamova cesta 39
SI-1000 Ljubljana



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