First-trimester preterm preeclampsia prediction model for prevention with low-dose aspirin.
Ritsuko Kimata PoohPublished in: The journal of obstetrics and gynaecology research (2024)
Emerging research explores predictors like maternal ophthalmic arterial waveform. Regional variations, especially in Asian populations, are considered. Machine learning and AI show promise, but examiner expertise remains essential for accurate prediction. In conclusion, integrating FMF's first-trimester PE screening with low-dose aspirin offers a promising strategy. Further advancements may enhance precision and broaden prevention efforts.
Keyphrases
- low dose
- machine learning
- high dose
- artificial intelligence
- big data
- pregnancy outcomes
- early onset
- preterm birth
- low birth weight
- high resolution
- quality improvement
- type diabetes
- deep learning
- preterm infants
- physical activity
- mass spectrometry
- coronary artery disease
- percutaneous coronary intervention
- weight loss
- antiplatelet therapy
- atrial fibrillation
- cataract surgery