Automatic Evaluation of Progression Angle and Fetal Head Station through Intrapartum Echographic Monitoring

Author:

Casciaro Sergio1ORCID,Conversano Francesco1ORCID,Casciaro Ernesto1,Soloperto Giulia1ORCID,Perrone Emanuele2ORCID,Di Renzo Gian Carlo2,Perrone Antonio3

Affiliation:

1. National Research Council, Institute of Clinical Physiology, University Campus Ecotekne, Via Monteroni, 73100 Lecce, Italy

2. Department of Obstetrics and Gynecology, University of Perugia, Santa Maria della Misericordia University Hospital, San Sisto, 06132 Perugia, Italy

3. Obstetrics and Gynecology Department, “Vito Fazzi” Hospital, Piazza Filippo Muratore, 73100 Lecce, Italy

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 of0.9±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.

Publisher

Hindawi Limited

Subject

Applied Mathematics,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Modelling and Simulation,General Medicine

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