Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.

Introduction: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including postpartum hemorrhage. Despite its significance, PAS remains frequently underdiagnosed before delivery. This study delves into the realm of machine learning to enhance the precision of PAS classification. We introduce two distinct models for PAS classification employing ultrasound texture features.

Methods: The first model leverages machine learning techniques, harnessing texture features extracted from ultrasound scans. The second model adopts a linear classifier, utilizing integrated features derived from 'weighted z-scores'. A novel aspect of our approach is the amalgamation of classical machine learning and statistical-based methods for feature selection. This, coupled with a more transparent classification model based on quantitative image features, results in superior performance compared to conventional machine learning approaches.

Results: Our linear classifier and machine learning models attain test accuracies of 87 % and 92 %, and 5-fold cross validation accuracies of 88.7 (4.4) and 83.0 (5.0), respectively.

Conclusions: The proposed models illustrate the effectiveness of practical and robust tools for enhanced PAS detection, offering non-invasive and computationally-efficient diagnostic tools. As adjunct methods for prenatal diagnosis, these tools can assist clinicians by reducing the need for unnecessary interventions and enabling earlier planning of management strategies for delivery.