Machine Learning in Obstetrics and Gynecology Diagnosis

Dear Colleagues,

The summary of a recent review suggested that different machine learning approaches would be optimal for different types of data regarding obstetrics and gynecology diagnosis: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79–0.99 for accuracy, 0.22–0.97 for sensitivity, 0.86–1.00 for specificity, 0.54–0.83 for the area under the receiver operating characteristic curve, and 0.44–1.47 for the mean square error divided by the variance. For example, the following maternal variables were reported to be major determinants of preterm birth: delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, the mother not having graduated high school, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptoms, gastroesophageal reflux disease, Helicobacter pylori, an urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth. Likewise, important predictors of the newborn’s body mass index were found to be the first abdominal circumference and estimated fetal weight in week 36 or later, gestational age at delivery, the first abdominal circumference between weeks 21 and 35, the maternal body mass index at delivery, the maternal weight at delivery and the first biparietal diameter in week 36 or later.

The existing literature on machine learning in obstetrics and gynecology diagnosis has several limitations. First, a cross-sectional design is still common in these studies, and data improvement with a longitudinal design would hence strengthen the performance of artificial intelligence in this area. Second, these studies have not analyzed the possible mediating effects among predictors for the early diagnosis of obstetric and gynecologic conditions. Third, the usage of big data (e.g., national health data) would be a good strategy for future studies. Fourth, investigating whether binary categories (no, yes) are common and analyzing various factors based on more refined categories will be interesting issues for future research. Fifth, the literature is lacking and more examination is needed on possible pathways between specific disease conditions. Sixth, uniting different kinds of deep learning methods for different types of data would bring forth more profound clinical implications. Seventh, little research has been conducted on ethical issues relating to the application of artificial intelligence. This is not surprising given that the application of artificial intelligence in this area has started to expand very recently. Nevertheless, if the current trend continues, this situation is expected to change, and professionals in obstetrics and gynecology would have to devote more attention to these issues and discuss them thoroughly. Finally, no basic or translational research has been performed on the basis of artificial intelligence.

This Special Issue aims to publish advanced reviews or original studies on machine learning in obstetrics and gynecology diagnosis and treatment. This Special Issue covers (but is not limited to) the application of machine learning in maternal–fetal medicine, reproductive endocrinology, gynecologic oncology, and urogynecology.

Original research or review articles are welcomed for this Special Issue. The topics of this Special Issue include (but are not limited to) the following:

• Machine learning in the early diagnosis of obstetric and gynecologic problems;
• Machine learning in the treatment of obstetric and gynecologic problems;
• Machine learning in the prognosis of obstetric and gynecologic problems;
• Machine learning in the workflow in obstetrics and gynecology;
• Explainable artificial intelligence in obstetrics and gynecology diagnosis and treatment.

Prof. Dr. Ki Hoon Ahn
Prof. Dr. Kwang-Sig Lee
Guest Editors

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• obstetrics
• gynecology
• machine learning
• diagnosis
• treatment
• prognosis