Ovarian torsion: developing a machine-learned algorithm for diagnosis

ConclusionBased on the largest series of pediatric ovarian torsion in the literature to date, we quantified sonographic features and used machine learning to create an algorithm to identify the presence of ovarian torsion — an algorithm that performs better than simple approaches relying on single features. Although complex combinations using multiple-interaction models provide slightly better performance, a clinically pragmatic decision tree can be employed to detect torsion, providing sensitivity levels of 95±14 % and specificity of 92±2%.

Source: Pediatric Radiology - January 21, 2020 Category: Radiology Source Type: research