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Mouse In Vivo Placental Targeted CRISPR Manipulation

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Machine learning applications in placenta accreta spectrum disorders.

Mahsa Danaei¹, Maryam Yeganegi², Sepideh Azizi³

¹Department of Obstetrics and Gynecology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.

European Journal of Obstetrics & Gynecology and Reproductive Biology: X

|January 23, 2025

Summary

Machine learning (ML) and radiomics show promise in diagnosing placenta accreta spectrum (PAS) disorders using medical imaging. These advanced techniques improve classification and risk stratification, aiding obstetric care.

Keywords:

Diagnostic Methods Machine Learning Placenta Accreta Spectrum Predictive Models Radiomics Surgical Planning

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Area of Science:

Obstetrics and Gynecology
Medical Imaging
Artificial Intelligence

Background:

Placenta accreta spectrum (PAS) disorders pose significant challenges in obstetric care.
Accurate diagnosis and prediction of PAS are crucial for effective management and improved patient outcomes.

Purpose of the Study:

To review the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of PAS.
To highlight advancements in ML algorithms and radiomic techniques for PAS classification and risk stratification.

Main Methods:

Utilizing medical imaging modalities such as magnetic resonance imaging (MRI) and ultrasound.
Applying deep learning models (e.g., nnU-Net, DenseNet-PAS) for classification and risk stratification.
Integrating quantitative imaging features with clinical data for enhanced accuracy.

Main Results:

ML and radiomics demonstrate superior performance over traditional methods, achieving high AUC scores.
Deep learning models show efficacy in classifying and predicting PAS.
ML can potentially predict surgical morbidity using demographic and obstetric factors.

Conclusions:

Radiomics and ML hold significant potential to improve PAS diagnosis, prediction, and surgical planning.
Standardized methodologies are essential for consistent feature extraction and model validation.
Integration into clinical workflows can enhance patient outcomes in high-risk pregnancies.