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

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.