Shim JW, et al. J Korean Med Sci. 2015 Oct;30(Suppl 1):S25-S34. https://doi.org/10.3346/jkms.2015.30.S1.S25

Statistical and analytical methods using artificial intelligence approaches such as machine learning (ML) are increasingly being applied to the field of pediatrics, particularly to neonatology. This study compared the representative ML analysis and the logistic regression (LR), which is a traditional statistical analysis method, using them to predict mortality of very low birth weight infants (VLBWI). We included 7472 VLBWI data from a nationwide Korean neonatal network. Eleven predictor variables (neonatal factors: male sex, gestational age, 5 min Apgar scores, body temperature, and resuscitation at birth; maternal factors: diabetes mellitus, hypertension, chorioamnionitis, premature rupture of membranes, antenatal steroid, and cesarean delivery) were selected based on clinical impact and statistical analysis. We compared the predicted mortality between ML methods—such as artificial neural network (ANN), random forest (RF), and support vector machine (SVM)—and LR with a randomly selected training set (80%) and a test set (20%). The model performances of area under the receiver operating curve (95% confidence interval) equaled LR 0.841 (0.811–0.872), ANN 0.845 (0.815–0.875), and RF 0.826 (0.795–0.858). The exception was SVM 0.631 (0.578–0.683). No statistically significant differences were observed between the performance of LR, ANN, and RF (i.e., p > 0.05). However, the SVM model was lower (p < 0.01). We suggest that VLBWI mortality prediction using ML methods would yield the same prediction rate as the traditional statistical LR method and may be suitable for predicting mortality. However, low prediction rates are observed in certain ML methods; hence, further research is needed on these limitations and selecting an appropriate method.

Introduction: Retinopathy of prematurity (ROP) remains a leading cause of preventable blindness in preterm infants. This study aimed to develop machine learning (ML) models using non-imaging clinical data to predict ROP, severe ROP (sROP), and treated ROP (tROP) in very low birth weight (VLBW) infants. Methods: We utilized nationwide clinical data from the Korean Neonatal Network, including 44 perinatal and neonatal variables. Two deep learning models, Multilayer Perceptron (MLP) and Neural Oblivious Decision Ensembles (NODE), optimized for tabular data, were applied. Additionally, we developed simplified models using eight key variables selected through clinical and algorithmic relevance. Results: MLP and NODE models demonstrated high predictive performance. For the full 44-variable models, the area under the receiver operating characteristic curve (AUROC) was as follows: ROP (0.853/0.855), sROP (0.888/0.890), and tROP (0.905/0.909). The reduced 8-variable models yielded comparable AUROCs: ROP (0.851/0.855), sROP (0.895/0.895), and tROP (0.910/0.909). Conclusion: The proposed ML models based on nationwide non-imaging clinical data enable early risk identification and timely intervention for ROP in VLBW infants. This cost-effective and scalable approach may help improve outcomes, especially in resource-limited settings.

Megalencephaly-capillary malformation-polymicrogyria syndrome (MCAP) is a rare genetic disorder characterized by megalencephaly, polymicrogyria, body overgrowth, and cutaneous capillary malformations. It has been reported recently that MCAP is related to a somatic mosaic mutation in the phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) gene. We report a case of hemimegalencephaly with polymicrogyria and cutaneous capillary malformations diagnosed by genetic evaluation of MCAP in the neonatal period. The PIK3CA mutation [c.1635G>T (p. Glu545Asp)] was determined by Sanger sequencing. The patient was treated with a ventriculoperitoneal shunt for progressive hydrocephalus. Because of the dynamic, progressive clinical manifestations and tumor-prone traits of MCAP, early diagnosis is important. Moreover, since the phosphoinositide 3-kinase (PI3K)-specific inhibitor, a targeted therapy for the PI3K/AKT/mTOR signaling pathway is emerging as a new therapy, early genetic diagnosis is becoming increasingly important.