Artificial intelligence based cardiotocogram assessment during labor.

Autor: Melaet R; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Nemo Healthcare BV, Veldhoven, the Netherlands., de Vries IR; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Nemo Healthcare BV, Veldhoven, the Netherlands. Electronic address: i.r.d.vries@tue.nl., Kok RD; Nemo Healthcare BV, Veldhoven, the Netherlands., Guid Oei S; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Obstetrics and Gynecology, Máxima Medical Centre, Veldhoven, the Netherlands., Huijben IAM; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., van Sloun RJG; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands., O E H van Laar J; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Department of Obstetrics and Gynecology, Máxima Medical Centre, Veldhoven, the Netherlands., Vullings R; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Nemo Healthcare BV, Veldhoven, the Netherlands.
Jazyk: angličtina
Zdroj: European journal of obstetrics, gynecology, and reproductive biology [Eur J Obstet Gynecol Reprod Biol] 2024 Apr; Vol. 295, pp. 75-85. Date of Electronic Publication: 2024 Feb 08.
DOI: 10.1016/j.ejogrb.2024.02.007
Abstrakt: Objective: To assess whether artificial intelligence, inspired by clinical decision-making procedures in delivery rooms, can correctly interpret cardiotocographic tracings and distinguish between normal and pathological events.
Study Design: A method based on artificial intelligence was developed to determine whether a cardiotocogram shows a normal response of the fetal heart rate to uterine activity (UA). For a given fetus and given the UA and previous FHR, the method predicts a fetal heart rate response, under the assumption that the fetus is still in good condition and based on how that specific fetus has responded so far. We hypothesize that this method, when having only learned from fetuses born in good condition, is incapable of predicting the response of a compromised fetus or an episode of transient fetal distress. The (in)capability of the method to predict the fetal heart rate response would then yield a method that can help to assess fetal condition when the obstetrician is in doubt. Cardiotocographic data of 678 deliveries during labor were selected based on a healthy outcome just after birth. The method was trained on the cardiotocographic data of 548 fetuses of this group to learn their heart rate response. Subsequently it was evaluated on 87 fetuses, by assessing whether the method was able to predict their heart rate responses. The remaining 43 cardiotocograms were segment-by-segment annotated by three experienced gynecologists, indicating normal, suspicious, and pathological segments, while having access to the full recording and neonatal outcome. This future knowledge makes the expert annotations of a quality that is unachievable during live interpretation.
Results: The comparison between abnormalities detected by the method (only using past and present input) and the annotated CTG segments by gynecologists (also looking at future input) yields an area under the curve of 0.96 for the distinction between normal and pathological events in majority-voted annotations.
Conclusion: The developed method can distinguish between normal and pathological events in near real-time, with a performance close to the agreement between three gynecologists with access to the entire CTG tracing and fetal outcome. The method has a strong potential to support clinicians in assessing fetal condition in clinical practice.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rik Vullings is one of the founders and shareholders of Nemo Healthcare BV, the Netherlands.
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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