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An Innovative Three-Stage Model for Prenatal Genetic Disorder Detection Based on Region-of-Interest in Fetal
Jiajie Tang1,2, Jin Han1,3, Yuxuan Jiang1,2,4
1Institute of Pediatrics, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou 510623, China.
Bioengineering (Basel, Switzerland)
|July 29, 2023
Summary
A new AI model, FGDS, uses fetal ultrasound images to detect genetic disorders early. This tool enhances prenatal diagnosis accuracy, aiding medical professionals in identifying potential conditions before birth.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Genetics
Background:
- Genetic syndromes impact 8% of the population, with diagnoses often delayed until after birth.
- Facial deformities are common indicators of chromosomal disorders.
- Prenatal ultrasound is crucial for detecting fetal abnormalities, but faces limitations.
Purpose of the Study:
- To develop and evaluate FGDS, a novel three-stage model for detecting genetic disorders using fetal ultrasound images.
- To improve the accuracy and efficiency of prenatal genetic disorder screening.
Main Methods:
- Developed a three-stage ensemble learning model (FGDS) trained on 2554 fetal ultrasound images.
- Utilized object detection for key region extraction and integrated disease information.
- Validated performance on internal and external test sets.
Main Results:
- FGDS achieved high accuracy in recognizing fetal facial structures (average precision 0.988).
- Internal testing showed FGDS sensitivity of 0.753 and specificity of 0.889.
- External testing demonstrated superior performance over mainstream models (sensitivity 0.768, specificity 0.837).
Conclusions:
- The proposed FGDS model shows significant potential for screening fetal genetic disorders.
- This AI-driven approach can enhance detection rates and support clinical decision-making.
- FGDS may help alleviate the workload for medical professionals in prenatal diagnostics.
