Automatic Evaluation of Progression Angle and Fetal Head Station through Intrapartum Echographic Monitoring. (9th September 2013)

• Record Type: Journal Article
• Authors: Casciaro, Sergio; Conversano, Francesco; Casciaro, Ernesto; Soloperto, Giulia; Perrone, Emanuele; Di Renzo, Gian Carlo; Perrone, Antonio
• Other Names: Mischi Massimo Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2013(2013)
• Publication Date: 2013-09-09
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2013/278978
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : Labor progression is routinely assessed through transvaginal digital inspections, meaning that the clinical decisions taken during the most delicate phase of pregnancy are subjective and scarcely supported by technological devices. In response to such inadequacies, we combined intrapartum echographic acquisitions with advanced tracking algorithms in a new method for noninvasive, quantitative, and automatic monitoring of labor. Aim of this work is the preliminary clinical validation and accuracy evaluation of our automatic algorithm in assessing progression angle (PA) and fetal head station (FHS). A cohort of 10 parturients underwent conventional labor management, with additional translabial echographic examinations after each uterine contraction. PA and FHS were evaluated by our automatic algorithm on the acquired images. Additionally, an experienced clinical sonographer, blinded regarding the algorithm results, quantified on the same acquisitions of the two parameters through manual contouring, which were considered as the standard reference in the evaluation of automatic algorithm and routine method accuracies. The automatic algorithm (mean error ± 2SD) provided a global accuracy of 0.9 ± 4.0 mm for FHS and 4° ± 9° for PA, which is far above the diagnostic ability shown by the routine method, and therefore it resulted in a reliable method for earlier identification of abnormal labor patterns in support of clinical decisions.