Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis

Balayla, Jacques; Shrem, Guy

doi:10.1007/s00404-019-05151-7

Cited by 68 publications

(48 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, in a meta-analysis our team conducted, we determined that relative to the use of clinical (visual) evaluation of the FHR, the use of AI did not change the incidence rates of neonatal acidosis, cord pH below 7.20, 5 min APGAR scores < 7, mode of delivery, NICU admission, neonatal seizures, or perinatal death. With regard to the degrees of interrater reliability, we found a weighed mean Cohen's kappa of 0.49 (0.32-0.66), which indicates moderate agreement between expert observers and computerized systems [8]. It is thus understood that current EFM interpretation may not be a reliable predictor of neonatal pH status, and as such, it has failed as a public health screening program.…”

Section: Introductionmentioning

confidence: 85%

Solving the Obstetrical Paradox: The FETAL Technique—A Step toward Noninvasive Evaluation of Fetal pH

Balayla

Shrem

2020

Journal of Pregnancy

Self Cite

View full text Add to dashboard Cite

Every year, about 85 percent of the approximately 5 million births in North America are evaluated with the electronic fetal monitoring (EFM). Clinicians use the EFM as a proxy to assess fetal oxygenation status, fetal well-being, and potential compromise. Despite the widespread use of this technology, neonatal hypoxia and acidosis continue to make up a high proportion of neonatal morbidity at term. Indeed, though the fetal heart rhythm is inextricably linked to fetal acid-base status, EFM has not been shown to reliably predict neonatal pH status nor has it reduced adverse maternal or neonatal outcomes. As a consequence, the high false-positive rate of EFM for predicting adverse neonatal outcomes has led to an increase in the rate of operative vaginal and cesarean delivery, with elevated rates of associated maternal and neonatal morbidity. This fact invariably leads to a paradox we have henceforth defined as the “obstetrical paradox.” Herein, we explore the potential solutions to this paradox and introduce a novel noninvasive technique to assess fetal acid-base status in utero known as the “FETAL technique” (Fourier Evaluation of Tracings and Acidosis in Labour). The FETAL technique, currently under investigation, applies the discrete Fourier transformation to EFM tracings to determine the spectral frequency distribution of the fetal heart rate. These specific frequency distributions correlate with specific umbilical pH values and may provide the missing link between fetal heat rate patterns and acid-base status at birth. As we work toward realizing the full potential benefits of EFM, finding the best assessment strategies to evaluate fetal pH in real time remains a key goal in obstetrics.

show abstract

Section: Introductionmentioning

confidence: 85%

Solving the Obstetrical Paradox: The FETAL Technique—A Step toward Noninvasive Evaluation of Fetal pH

Balayla

Shrem

2020

Journal of Pregnancy

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, there is no evidence on whether these systems really improve the prediction of fetal distress or acidemia compared to visual CTG interpretation alone, and reports about their clinical performance were not found. In a recent systematic review, the degree of interobserver reliability between human and ML interpretations of CTG signals was determined [57], and it was concluded that the use of ML for interpretation of CTG during labor does not improve neonatal outcomes and has yet to prove its reliability relative to expert observers. The root of the problem may be that any supervised ML-based system needs to be trained with human annotations and, given that the benefit of Fetal Diagn Ther 2020;47:363-372 DOI: 10.1159/000505021 CTG themselves for labor monitoring has not been clearly demonstrated, it is not surprising that adding an automatic system to evaluate CTG signals with similar information does not offer advantages in reducing adverse perinatal outcomes.…”

Section: For Fetal Diagnosismentioning

confidence: 99%

Machine Learning in Fetal Cardiology: What to Expect

et al. 2020

View full text Add to dashboard Cite

In fetal cardiology, imaging (especially echocardiography) has demonstrated to help in the diagnosis and monitoring of fetuses with a compromised cardiovascular system potentially associated with several fetal conditions. Different ultrasound approaches are currently used to evaluate fetal cardiac structure and function, including conventional 2-D imaging and M-mode and tissue Doppler imaging among others. However, assessment of the fetal heart is still challenging mainly due to involuntary movements of the fetus, the small size of the heart, and the lack of expertise in fetal echocardiography of some sonographers. Therefore, the use of new technologies to improve the primary acquired images, to help extract measurements, or to aid in the diagnosis of cardiac abnormalities is of great importance for optimal assessment of the fetal heart. Machine leaning (ML) is a computer science discipline focused on teaching a computer to perform tasks with specific goals without explicitly programming the rules on how to perform this task. In this re-view we provide a brief overview on the potential of ML techniques to improve the evaluation of fetal cardiac function by optimizing image acquisition and quantification/segmentation, as well as aid in improving the prenatal diagnoses of fetal cardiac remodeling and abnormalities.

show abstract

“…Perhaps no other clinical factor has had a greater disproportionate influence into the cesarean section rate than the introduction of the electronic fetal heart rate (FHR) monitor (EFM) in the 1960s. 7 These authors believe therein lies ample potential for intervention to reduce this rising trend.…”

Section: The Electronic Fetal Heart Rate Monitormentioning

confidence: 99%

“…Given these limitations, obstetrical care providers developed the EFM system, a noninvasive tool which evaluates baseline variability in FHR patterns to evaluate fetal wellbeing/distress in real-time. 7 The concept of fetal distress is a comprehensive, umbrella term. Though several risk factors have been described to characterize fetal distress (meconium staining, maternal fever, abnormal fetal tracings, among others), we refer herein to cases of hypoxia/acidosis leading to a low fetal pH at birth.…”

Section: The Electronic Fetal Heart Rate Monitormentioning

confidence: 99%

“…On the other hand, the introduction of the EFM has contributed to an increase in the cesarean section rate six to sevenfold without any significant benefit. 8 Despite the physiological basis for the EFM patterns, the poor specificity associated with the current interpretation of the EFM therefore leads to a paradox that has been coined as the “Obstetrical Paradox.” 7 To better and more accurately estimate the predictive ability of the EFM, we need to consider Bayes' theorem in this clinical context.…”

Section: The Electronic Fetal Heart Rate Monitormentioning

confidence: 99%

See 1 more Smart Citation

Bayes Theorem and Protopathic Bias: Methodological Concerns When Addressing the Impact of Fetal Heart Rate Patterns on the Cesarean Section Rate

Balayla

Lasry

Gil

et al. 2020

AJP Rep

Self Cite

View full text Add to dashboard Cite

Over the last 30 years, the caesarean section rate has reached global epidemic proportions. This trend is driven by multiple factors, an important one of which is the use and inconsistent interpretation of the electronic fetal monitoring (EFM) system. Despite its introduction in the 1960s, the EFM has not definitively improved neonatal outcomes, yet it has since significantly contributed to a seven-fold increase in the caesarean section rate. As we attempt to reduce the caesarean rates in the developed world, we should consider focusing on areas that have garnered little attention in the literature, such as physician sensitization to the poor predictive power of the EFM and the research method biases that are involved in studying the abnormal heart rate patterns—umbilical cord pH relationship. Herein, we apply Bayes theorem to different clinical scenarios to illustrate the poor predictive power of the EFM, as well as shed light on the principle of protopathic bias, which affects the classification of research outcomes among studies addressing the effects of the EFM on caesarean rates. We propose and discuss potential solutions to the aforementioned considerations, which include the re-examination of guidelines with which we interpret fetal heart rate patterns and the development of noninvasive technologies that evaluate fetal pH in real time.

show abstract

Use of artificial intelligence (AI) in the interpretation of intrapartum fetal heart rate (FHR) tracings: a systematic review and meta-analysis

Cited by 68 publications

References 21 publications

Solving the Obstetrical Paradox: The FETAL Technique—A Step toward Noninvasive Evaluation of Fetal pH

Solving the Obstetrical Paradox: The FETAL Technique—A Step toward Noninvasive Evaluation of Fetal pH

Machine Learning in Fetal Cardiology: What to Expect

Bayes Theorem and Protopathic Bias: Methodological Concerns When Addressing the Impact of Fetal Heart Rate Patterns on the Cesarean Section Rate

Contact Info

Product

Resources

About