AMAL-For-Qatar: Advancing Precision Medicine with AI-Mediated for Fetal Life through Ultrasound Video Analysis

  • Househ, Mowafa (Principal Investigator)
  • Agus, Marco (Lead Principal Investigator)
  • Awwad, Johnny (Principal Investigator)
  • Alyafei, Khalid (Principal Investigator)
  • Alberry, Medhat (Principal Investigator)

Project: Basic Research

Project Details

Abstract

The ”AMAL-For-Qatar” project aims to revolutionize prenatal care by integrating advanced Artificial Intelligence (AI) to automate the analysis of fetal ultrasound videos during the 12 to 13-week scans. This project specifically targets the precise measurement of Nuchal Translucency (NT) [1, 2], Nasal Bone (NB) length [3], Crown- Rump Length (CRL) [4], and the detection of neural tube defects such as Anencephaly [5] and Spina Bifida [6]. These metrics are crucial for assessing risks of chromosomal abnormalities, including Down syndrome [3, 4, 7], thereby facilitating early and effective prenatal interventions. Building on existing research in the field, which often focuses on isolated aspects of prenatal diagnostics [5, 3, 8, 9], our project proposes a holistic solution that is not currently addressed in the literature. While significant advancements have been made in the application of deep learning for specific diagnostic tasks, a comprehensive, real-time system for diverse anomaly detection during ultrasound scans is still lacking. Our initiative fills this gap by providing an all-encompassing multi-task framework, which is especially pertinent given the limited availability of fetal medicine specialists in regions like Qatar and the broader MENA area

Submitting Institute Name

Hamad Bin Khalifa University (HBKU)
Sponsor's Award NumberPPM 07-0409-240041
Proposal IDEX-QNRF-PPM-37
StatusActive
Effective start/end date1/01/251/01/28

Collaborative partners

Primary Theme

  • Artificial Intelligence

Primary Subtheme

  • AI - Healthcare

Secondary Theme

  • Precision Health

Secondary Subtheme

  • PH - Diagnosis Treatment

Keywords

  • Fetal Ultrasound
  • Automatic diagnosis
  • Large Language Models

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