Fetal Gestational Age Prediction in Brain Magnetic Resonance Imaging Using Artificial Intelligence: A Comparative Study of Three Biometric Techniques

Accurately predicting a fetus’s gestational age (GA) is crucial in prenatal care. This study aimed to develop an artificial intelligence (AI) model to predict GA using biometric measurements from fetal brain magnetic resonance imaging (MRI). We assessed the significance of using different reference...

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Bibliographic Details
Published inReproductive medicine (Basel, Switzerland) Vol. 5; no. 3; pp. 113 - 135
Main Authors Vahedifard, Farzan, Liu, Xuchu, Marathu, Kranthi K., Ai, H. Asher, Supanich, Mark P., Kocak, Mehmet, Adler, Seth, Ansari, Shehbaz M., Akyuz, Melih, Adepoju, Jubril O., Byrd, Sharon
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2024
Subjects
Artificial intelligence
fetal brain
gestational age
Magnetic resonance imaging
Management decisions
MRI
Neural networks
United States > US
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Summary:Accurately predicting a fetus’s gestational age (GA) is crucial in prenatal care. This study aimed to develop an artificial intelligence (AI) model to predict GA using biometric measurements from fetal brain magnetic resonance imaging (MRI). We assessed the significance of using different reference standards for interpreting GA predictions. Measurements of biparietal diameter (BPD), fronto-occipital diameter (FOD), and head circumference (HC) were obtained from 52 normal fetal MRI cases from Rush University. Both manual and AI-based methods were utilized, and comparisons were made using three reference standards (Garel, Freq, and Bio). The AI model showed a strong correlation with manual measurements, particularly for HC, which exhibited the highest correlation with actual values. Differences between GA predictions and picture archiving and communication system (PACS) records varied by reference, ranging from 0.47 to 2.17 weeks for BPD, 0.46 to 2.26 weeks for FOD, and 0.75 to 1.74 weeks for HC. Pearson correlation coefficients between PACS records and GA predictions exceeded 0.97 across all references. In conclusion, the AI model demonstrated high accuracy in predicting GA from fetal brain MRI measurements. This approach offers improved accuracy and convenience over manual methods, highlighting the potential of AI in enhancing prenatal care through precise GA estimation.
ISSN:2673-3897
2673-3897
DOI:10.3390/reprodmed5030012