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Prediction of premature rupture of fetal membranes using deep learning in East China
Cuiyu Yang1,2,3, Rui Feng4, Xinhui Wang5
1Assisted Reproduction Unit, Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310020, China.
Insights
Forecasting premature rupture of fetal membranes (PROM) using deep learning can predict 86.1% of cases. This allows for personalized treatment and better resource allocation, though it presents a false-positive paradox.
Area of Science:
- Obstetrics and Gynecology
- Environmental Health
- Artificial Intelligence in Medicine
Background:
- Premature rupture of fetal membranes (PROM) poses risks to neonates and strains medical resources.
- Accurate forecasting of PROM is crucial for timely intervention and resource management in China.
Purpose of the Study:
- To develop a deep learning model for predicting the odds of PROM.
- To assess the utility of the Large Language Model-DeepSeek in healthcare settings for PROM prediction.
- To identify clinical covariates, air pollution, and meteorological factors associated with PROM.
Main Methods:
- Analysis of medical records from 20,392 mother-neonate dyads in Hangzhou, China (2014-2019).
- Estimation of maternal exposure to air pollution and meteorological conditions based on addresses.
- Application of deep learning algorithms to predict PROM occurrence.
- Utilized Bonferroni correction for statistical significance of covariates.
Main Results:
- Identified 32 statistically significant clinical covariates associated with PROM, with 25 detectable at least one week prior.
- Established links between air pollution, meteorological conditions, and PROM.
- Deep learning model achieved an 86.1% forecasting accuracy for PROM cases.
- DeepSeek demonstrated uniformity, thoroughness, and robustness in healthcare application.
Conclusions:
- Deep learning offers a promising approach for forecasting PROM, enabling individualized treatment and optimized resource allocation.
- The study highlights the potential of AI, specifically DeepSeek, to enhance healthcare processes and medical resource management.
- Addressing the trade-off between prediction accuracy and false-positive rates in PROM forecasting remains a critical challenge.
Abstract:
If premature rupture of fetal membranes (PROM) can be forecasted, doctors can formulate individualized medical treatment plans and optimize the utilization efficiency of unevenly distributed medical resources in China to lower the odds of PROM, preterm birth, and neonatal mortality. We collected the medical records of 20,392 dyads of mothers and term-birth neonates who had received prenatal care services from January 1, 2014 to December 31, 2019 in Hangzhou, Zhejiang province, East China. According to participants' home and working addresses, maternal exposure to air pollution and meteorological conditions was estimated. Deep learning was used to predict the odds of PROM occurrence. The efficiency of Large Language Model-DeepSeek was tested in healthcare settings. Of 32 clinical covariates have been identified to be statistically significantly associated with PROM, 25 variables-7 positively and 18 negatively linked to PROM-can be detected at least one week before PROM or delivery. Using the Bonferroni correction as a stricter classification tool, 10 out of 32 clinical covariates were statistically associated with PROM. Air pollution exposure and meteorological conditions that were associated with PROM were identified. Based on these findings, approximately 86.1% of PROM cases can be forecasted using deep learning. Thus, individualized treatment can be crafted and vital medical resources can be allocated in advance. DeepSeek can facilitate healthcare processes and optimization of medical resources, showing its uniformity, thoroughness, and robustness. However, the improvement of prediction accuracy for PROM was accompanied by increasing false-positive cases, which is a paradox that needs to be solved.