Fetal Heart Monitoring and Pregnancy Surveillance:
Fetal heart rate (FHR) monitoring helps the healthcare provider to
monitor the fetus and diagnose associated high-risk complications. It
also gives a qualitative and quantitative overview of baseline fetal
heart rate, variability, acceleration, deceleration, uterine contraction
intensity, and FHR pattern changes [6]. Currently, AI is used to
monitor the fetal heart rate during labor via analyzing cardiotocographs
and estimating possible outcomes. As Desai states in his study, the
benefits of its incorporation in obstetrics, specifically antepartum
monitoring, would be advantageous [2]. This technology would help
decrease the discrepancies between different obstetricians interpreting
intrapartum monitoring, thus providing a more reliable and replicable
output for each analysis, and ultimately reducing perinatal and maternal
complications and morbidity. Artificial intelligence systems can also
provide supporting evidence in cases of unpredictable poor outcomes that
can potentially result in litigation. Some examples of where AI has been
tested in cardiotocography (CTG) analysis include: CAFE (Computer-Aided
Fetal Evaluator) studied the possibility of an AI system being able to
interpret CTG data [2,6]. The results concluded that the AI system
read the information at a similar level as the experts in the field and
was also able to detect errors. Some limitations in this study were
disagreements between specialists when interpreting some data, mostly in
interpreting variations, which led to an inability to conclude if the
error in interpretation arose from a problem in the system, or in
obtaining agreement between experts in the field [7]. This was an
obstacle in proving that the system is optimum and confirming its
precision. More robust research is required to enhance flexibility in
data interpretation.
The INFANT study protocol is a large trial currently evaluating the
ability of AI interpretation of CTG during labor to assist practitioners
in deciding the best management on an individual basis [8]. The goal
is to make FHR reading more reliable, to aid the physician in
interpreting and decision-making, and decrease the burden of work by
making it more efficient. Perinatal asphyxia is a significant problem
worldwide, and by creating an efficient way to monitor FHR, it would
improve care and decrease poor outcomes. Thus, an alarm monitor that
warns of potential fetal distress can guide the practitioner to act in a
timely matter and perform intrapartum interventions when necessary. It
can also provide reassurance and avoid unnecessary treatment in those
women where fetal distress is not observed.
System 8000 is a computer system that analyzes the antenatal fetal heart
rate (FHR) [8,9]. It monitors changes in FHR and detects amplitudes
associated with hypoxemia by detecting decelerations and changes in
variability. Currently, a decrease in variation is the most dependable
index of fetal deterioration, but unfortunately, there is significant
observer variation in interpreting this data. The introduction of AI can
decrease this difference between readers, which can lead to a more
reliable interpretation of variation, decrease unnecessary
interventions, and expedite the delivery if necessary.
CTG technology arose 50 years ago, and frequent differences between
specialists urge for a system that decreases error and unifies
interpretation while analyzing CTG. A study was done by Kazantsev et al.
urges the development of new technologies that can address the issue of
maternal and infant mortality [9,10] The researchers in this study
believe that AI technology can be used for out-patient care in the form
of home monitors that can adequately provide surveillance of high-risk
patients. This technology in conjunction with telemedicine can
potentially aid in earlier detection of potential complications and
reassure the clinician and the patient that a safe system of monitoring
is being used even if an outside clinic or hospital care. It also
provides a warning system that informs the patient of dangerous FHR
readings and signals them to notify the physician to decide on further
care according to the in-home AI analysis. In this study, they used
neural network recognition algorithms that can provide a solution to
interpretation discrepancies between specialists. It also mentioned that
outpatient monitoring is readily accessible because most homes have
access to the Internet and smartphones. The insertion of AI into Doppler
ultrasound has proven to be cost-effective. The system is also able to
exclude readings of uncertain meaning, such as pseudo accelerations and
pseudo decelerations that occur due to intense fetal movement, thus not
falsely alarming the patient of possible fetal endangerment. The
downside of the study was that the proposed method was only tested in
one subject, and even though it proved successful, a larger study is
needed to replicate its results and validate this intervention. The
possibility of guiding decision-making and management using
telecommunications combined with in-home pregnancy monitoring can prove
beneficial in early detection of pregnancy complications and decrease
maternal and infant mortality [8,9,10].