TY -非盟的沈益盟——陈Jiebin盟——郑Zequan盟——郑Jiabin盟——刘Zherui盟歌,剑盟——黄总和易盟——王,小玲盟——黄,Mengqi盟方,Po-Han盟——江Bangsheng AU - Tsang Winghei盟——他,Zonglin盟——刘,陶然盟——Akinwunmi Babatunde AU -王,气赵盟——张鬼马小精灵J P AU -黄,剑盟,明Wai-Kit PY - 2020 DA - 2020/9/15 TI -一个创新的基于人工智能的应用的诊断妊娠期糖尿病(GDM-AI):发展研究乔- J地中海互联网Res SP - e21573六世- 22 - 9千瓦- AI KW -应用程序KW -疾病诊断千瓦孕产妇保健KW -人工智能KW - App KW -女性KW -农村KW -创新KW -糖尿病KW -妊娠期糖尿病KW -诊断AB -背景:妊娠期糖尿病(GDM)会对母亲和新生儿造成不良后果。然而，由于GDM诊断的可获得性有限，生活在低收入和中等收入地区或国家的孕妇往往无法在当地医疗设施获得早期临床干预。在以往的研究中，人工智能(AI)在疾病诊断中的出色表现，证明了其在GDM诊断中的应用前景。目的:本研究旨在研究一种性能良好的AI算法在GDM诊断中在一个环境下的实现，该环境对医疗设备和人员的要求更低，并建立基于AI算法的应用程序。本研究也探讨了如果我们的应用被广泛使用可能取得的进展。方法:对2010年11月至2017年10月期间在华南地方医院暨南大学第一附属医院妇产科接受GDM检测的12304名孕妇门诊患者进行人工智能模型训练，该模型包含9种算法。GDM根据美国糖尿病协会(ADA) 2011年诊断标准进行诊断。选择年龄和空腹血糖作为关键参数。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. SN - 1438-8871 UR - //www.mybigtv.com/2020/9/e21573 UR - https://doi.org/10.2196/21573 UR - http://www.ncbi.nlm.nih.gov/pubmed/32930674 DO - 10.2196/21573 ID - info:doi/10.2196/21573 ER -