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Birth and Labor Outcome Optimization through Multimodal AI

Objective

Cardiotocography (CTG) is a vital tool for monitoring fetal well-being during labor, recording fetal heart rate and uterine contractions to detect issues like hypoxia, which can cause neonatal deaths or long-term neurological damage. Early detection through CTG enables timely interventions, such as emergency cesarean sections, reducing risks. However, interpreting CTG data is challenging and depends heavily on clinician expertise, making assessments sometimes subjective and prone to error. This has driven interest in integrating artificial intelligence (AI) into CTG analysis to support clinicians with more accurate and timely decision-making. While AI shows promise in enhancing CTG interpretation, a significant challenge remains the absence of a universally accepted standard for assessing fetal outcomes. Many existing methods rely on surrogate markers or rigid scoring systems that fail to account for the complex physiological responses of individual fetuses. A pathophysiological approach, which considers unique fetal compensatory mechanisms and maternal health factors like hypertension or gestational diabetes, can provide a more nuanced and precise assessment of fetal well-being. The Birth and Labor Outcome Optimization through Multimodal AI (BLOOM) project is designed to address these challenges. It aims to develop, implement, and validate an advanced AI system specifically for use during labor. This system will integrate multimodal data, including fetal growth patterns from ultrasound screenings, maternal health conditions from electronic health records (EHR), and pathophysiological interpretations of CTG data. By combining these personalized factors with cutting-edge AI technologies, BLOOM seeks to create a more accurate and effective tool for assessing fetal well-being during labor, ultimately improving clinical outcomes and personalizing care.

Fields of science (EuroSciVoc)

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Keywords

Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)

Programme(s)

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Topic(s)

Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.

Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

UNIVERSITA DEGLI STUDI GABRIELE D'ANNUNZIO DI CHIETI-PESCARA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 321 948,72
Address
VIA DEI VESTINI 31
66100 Chieti
Italy

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Region
Sud Abruzzo Chieti
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Partners (2)

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