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Updated: Dec 21, 2025

Multiplexed Fluorescent Immunohistochemical Staining of Four Endometrial Immune Cell Types in Recurrent Miscarriage

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Published on: August 4, 2021

Machine Learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential

V Bruno¹, M D'Orazio², C Ticconi³

¹Academic Department of Biomedicine and Prevention, University of Rome Tor Vergata, and Clinical Department of Surgical Sciences, Section of Gynecology, Tor Vergata University Hospital, Viale Oxford, 81 - 00133, Rome, Italy. valentinabruno_86@hotmail.it.

Scientific Reports

|May 16, 2020

Summary

Machine learning (ML) can objectively classify recurrent pregnancy loss (RPL) patients into risk groups. An ML model achieved 81% accuracy, outperforming standard guidelines for better prognosis and treatment.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Reproductive Medicine
Artificial Intelligence in Healthcare
Clinical Decision Support Systems

Background:

Recurrent pregnancy loss (RPL) is a complex condition with ongoing debates regarding its definition, causes, and treatment.
Objective classification and risk stratification of RPL patients remain challenging for clinicians.
Existing diagnostic work-ups may not consistently provide clear prognostic or therapeutic guidance.

Purpose of the Study:

To stratify patients with recurrent pregnancy loss (RPL) into distinct risk classes using a machine learning (ML) algorithm.
To validate an ML-driven diagnostic work-up for improved prognosis and tailored therapeutic strategies in RPL.
To enhance objectivity in RPL patient classification and care access through advanced computational methods.

Main Methods:

A cohort of 734 patients with RPL was analyzed.
A Support Vector Machine (SVM) ML algorithm was employed to stratify patients into four risk classes based on miscarriage history.
Model performance was evaluated using the full dataset (43 features) and a reduced informative feature set (18 features).

Main Results:

The ML model achieved high classification accuracy, with 81.86% ± 0.35% using all features and 81.71% ± 0.37% using 18 key features.
In contrast, applying the same ML method with only ESHRE-recommended features resulted in significantly lower accuracy (58.52% ± 0.58%).
The findings highlight the superior performance of the comprehensive ML approach over guideline-based feature selection for RPL classification.

Conclusions:

Machine learning algorithms, particularly SVM, can effectively stratify RPL patients into objective risk categories.
The developed ML approach demonstrates potential as a clinical decision support tool for RPL management.
Objective risk stratification by ML can guide patients towards appropriate clinical management and treatment pathways.