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Review Article

The present and future of intrapartum computerized cardiotocography: role of pattern recognition incorporating single vs. multiple parameters

Pages 7452-7458 | Received 22 Dec 2019, Accepted 25 Jun 2021, Published online: 17 Aug 2021
 

Abstract

Computer assisted cardiotocography holds a great promise in minimizing human errors thereby improving perinatal outcome. Despite exponential growth (Moore’s law) in computing power for decades, this promise remains unrealized. The systematic analyses of studies on computerized cardiotocography offer little guide to future. This analytical review presents a more qualitative discussion of available evidence as well as concepts regarding the development and acceptance of computerized cardiotocography. To begin with, a workable approach would be for computer algorithms to follow the most scientific visual cardiotocography interpretation frameworks incorporating multiple fetal heart rate parameters and uterine contractions. This ability could be studied and form the basis for regulation of computer algorithms. Addition of background risk factors would be another step. This may take form of familiar multi-tier systems or new alternative strategies like the fetal reserve index. “Machine learning” will remain challenging because of complex variability in fetal-maternal conditions, labor characteristics and clinical intervention changing the outcomes. Randomized controlled trials of adequate size may remain very rare. However, prospective and retrospective testing of computer algorithms with careful qualitative and comparative approach would help clinicians and hospital managers in their decisions. Singular parameters like the popular “deceleration area” and “deceleration capacity” have poor predictive value for fetal acidemia or hypoxic injury. Scientific pattern-recognition of important fetal heart rate parameters like decelerations seems crucial for visual as well as computerized cardiotocography. Success of computerizedcardiotocography depends on team effort by the obstetricians with in-depth practical knowledge/experience and skilled artificial intelligence (AI) specialists.

Acknowledgements

The author is thankful to the hospital’s library staff for their help in accessing the literature. The author has not worked, lectured or advised for any computerized CTG software companies. No funding has been received for this review. The views expressed are author’s opinion only.

Disclosure statement

No potential conflict of interest was reported by the author(s).

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