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Deep Neural Networks for Image-Based Dietary Assessment
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Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning

Burçin Kurt1, Beril Gürlek2, Seda Keskin3

  • 1Faculty of Medicine, Department of Biostatistics and Medical Informatics, Karadeniz Technical University, Trabzon, Turkey. burcinnkurt@gmail.com.

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Summary

A new deep learning model accurately identifies pregnant women at risk for gestational diabetes (GD), reducing the need for unnecessary oral glucose tolerance tests (OGTT). This AI-driven system improves diagnostic efficiency and patient outcomes.

Keywords:
Bayesian optimizationClinical decision support systemDeep learningGestational diabetes (GD)Random forestSVM

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Obstetrics and Gynecology

Background:

  • Gestational diabetes (GD) diagnosis relies on oral glucose tolerance tests (OGTT), which can be resource-intensive and lead to unnecessary procedures for low-risk pregnancies.
  • Developing accurate, non-invasive diagnostic tools is crucial for efficient prenatal care and reducing healthcare costs.

Purpose of the Study:

  • To develop and validate a deep learning-based clinical decision support system for identifying pregnant women at risk of gestational diabetes (GD).
  • To reduce the application of unnecessary oral glucose tolerance tests (OGTT) in pregnant women not belonging to the GD risk group.

Main Methods:

  • A prospective study involving 489 patients between 2019-2021.
  • Development of a clinical decision support system using deep learning algorithms, specifically RNN-LSTM, combined with Bayesian optimization.
  • Model training and validation on the collected patient dataset.

Main Results:

  • A novel decision support model utilizing RNN-LSTM with Bayesian optimization was successfully developed.
  • The model achieved 95% sensitivity and 99% specificity for identifying patients in the GD risk group.
  • The model demonstrated high diagnostic accuracy with an Area Under the Curve (AUC) of 98% (95% CI: 0.95-1.00, p < 0.001).

Conclusions:

  • The developed clinical diagnosis system effectively assists physicians in identifying gestational diabetes risk.
  • The system has the potential to significantly save costs and time while minimizing adverse effects by preventing unnecessary OGTTs for low-risk individuals.