An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Abstract

Background Gestational diabetes mellitus (GDM) can cause adverse consequences to both mothers and their newborns. However, pregnant women living in low- and middle-income areas or countries often fail to receive early clinical interventions at local medical facilities due to restricted availability of GDM diagnosis. The outstanding performance of artificial intelligence (AI) in disease diagnosis in previous studies demonstrates its promising applications in GDM diagnosis. Objective This study aims to investigate the implementation of a well-performing AI algorithm in GDM diagnosis in a setting, which requires fewer medical equipment and staff and to establish an app based on the AI algorithm. This study also explores possible progress if our app is widely used. Methods An AI model that included 9 algorithms was trained on 12,304 pregnant outpatients with their consent who received a test for GDM in the obstetrics and gynecology department of the First Affiliated Hospital of Jinan University, a local hospital in South China, between November 2010 and October 2017. GDM was diagnosed according to American Diabetes Association (ADA) 2011 diagnostic criteria. Age and fasting blood glucose were chosen as critical parameters. For validation, we performed k-fold cross-validation (k=5) for the internal dataset and an external validation dataset that included 1655 cases from the Prince of Wales Hospital, the affiliated teaching hospital of the Chinese University of Hong Kong, a non-local hospital. Accuracy, sensitivity, and other criteria were calculated for each algorithm. Results The areas under the receiver operating characteristic curve (AUROC) of external validation dataset for support vector machine (SVM), random forest, AdaBoost, k-nearest neighbors (kNN), naive Bayes (NB), decision tree, logistic regression (LR), eXtreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT) were 0.780, 0.657, 0.736, 0.669, 0.774, 0.614, 0.769, 0.742, and 0.757, respectively. SVM also retained high performance in other criteria. The specificity for SVM retained 100% in the external validation set with an accuracy of 88.7%. Conclusions Our prospective and multicenter study is the first clinical study that supports the GDM diagnosis for pregnant women in resource-limited areas, using only fasting blood glucose value, patients’ age, and a smartphone connected to the internet. Our study proved that SVM can achieve accurate diagnosis with less operation cost and higher efficacy. Our study (referred to as GDM-AI study, ie, the study of AI-based diagnosis of GDM) also shows our app has a promising future in improving the quality of maternal health for pregnant women, precision medicine, and long-distance medical care. We recommend future work should expand the dataset scope and replicate the process to validate the performance of the AI algorithms.

Contribution of Artificial Intelligence in Pregnancy: A Scoping Review

“…The majority of the studies (n=22) were published between 2016 and 2020. AI techniques were used for predicting pregnancy disorders/complications in about 75% (n=18) of the included studies [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Specifically, the techniques discussed were utilized for predicting preeclampsia [5,7,9,15], preterm birth [6,13,19], gestational diabetes [8,14,21], gestational age [4,18], patient's metabolomics profile [12,20], suicidal behavior [11], uterine contractions [16], labor due date [17], and hypertensive disorder [10].…”

“…AI techniques were used for predicting pregnancy disorders/complications in about 75% (n=18) of the included studies [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Specifically, the techniques discussed were utilized for predicting preeclampsia [5,7,9,15], preterm birth [6,13,19], gestational diabetes [8,14,21], gestational age [4,18], patient's metabolomics profile [12,20], suicidal behavior [11], uterine contractions [16], labor due date [17], and hypertensive disorder [10]. Additionally, of the 24 studies, four (n=4, 17%) employed AI techniques for treatment and management of ectopic pregnancies [22], gestational diabetes [23], late-onset preeclampsia [5], and hypertensive disorder [10].…”

“…12 out of 18 studies (67%) used the ML models for prediction purposes [4, 5, 7-10, 14, 15, 18, 21, 23, 25]. In the second AI technique, deep learning (DL) -based techniques were implemented in eight studies (n=8, 33%) [6,8,9,16,19,22,24,27]. Similar to the ML studies, DL was used mainly for prediction purposes (n=7, 87%) [6,8,13,16,19,22,27].…”

Ensemble Classifier Technique to Predict Gestational Diabetes Mellitus (GDM)

“…Zheng et al [7] presented a simple approach to detect GDM in earlier pregnancy using biochemical markers as well as ML method. In the study conducted by Shen et al [8], it was mentioned that the investigation of optimal AI approach in GDM prediction requires minimal clinical devices and trainees so as to develop an application based on Artificial intelligence (AI). In the literature [9], the prediction of GDM using different ML approaches is projected on PIMA dataset.…”

Utilizing Fog computing and Explainable Deep Learning techniques for Gestational Diabetes prediction

“…(iii) Using biochemical markers and the ML method, Zheng et al [29] presented a straightforward method for detecting GDM in early pregnancy. (iv) In a study conducted by Shen et al [30], it was stated that the exploration of the best AI approach for GDM prediction required the least number of clinical devices and trainees in order to construct an AI-based application (AI). [31][32].…”