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 Number | PPM 07-0409-240041 |
---|---|
Proposal ID | EX-QNRF-PPM-37 |
Status | Active |
Effective start/end date | 1/01/25 → 1/01/28 |
Collaborative partners
- Hamad Bin Khalifa University (lead)
- Sidra Medicine
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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