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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.

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