d-GDM: A mobile diagnostic decision support system for gestational diabetes

Abstract

Objective: The aim of the study is to describe a portable and convenient software to facilitate the diagnostics of gestational (GDM) and pre-gestational diabetes (PGDM). Materials and methods: An open source software, d-GDM, was developed in Java. The integrated development environment Android Studio was used as the Android operational system. The software for GDM diagnosis uses the criteria endorsed by the International Association of Diabetes and Pregnancy Study Group, modified by the World Health Organization. Results: GDM diagnosis criteria is not simple to follow, therefore, errors or inconsistencies in diagnosis are expected and could delay the appropriate treatment. The d-GDM, was developed to assist GDM diagnosis with precision and consistency diagnostic reports. The open source software can be manipulated conveniently. The operator requires information regarding the gestational period and selects the appropriate glycaemic marker options from the menu. During operation, pressing the button "diagnosticar" on the screen will present the diagnosis and information for the follow up. d-GDM is available in Portuguese or English and can be downloaded from the Google PlayStore. A responsive web version of d-GDM is also available. The usefulness and accuracy of d-GDM was verify by field tests involving 22 subjects and 5 mobile phone brands. The approval regards userfriendliness and efficiency were 95% or higher. The GDM diagnosis were 100% correct, in this pilot test. d-GDM is a user-friendly, free software for diagnosis that was developed for mobile devices. It has the potential to contribute and facilitate the diagnosis of gestational diabetes for healthcare professionals.

mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review

“…Five papers 21,36–39 failed to describe the AI engine powering their GDM tool. Finally, two papers present applications (GDm‐Health and d‐GDM) that do not use any AI‐based engine 21,39 . However, these papers were included in the literature collection because they met the requirement of describing an approach for clinical decision support in GDM.…”

“…[32][33][34] There was also one work 35 that described use of the Microsoft Azure AI without classifying the underlying AI engine. Five papers 21,[36][37][38][39] failed to describe the AI engine powering their GDM tool. Finally, two papers present applications (GDm-Health and d-GDM) that do not use any AI-based engine.…”

“…Of the 18 papers (67%), 12 present solutions intended for co-use by women with GDM and their clinicians. Five 32,35,36,39,42 were intended for clinical or research use only, and it was noted that these were all primarily GDM diagnostic tools. In addition, one work, 26 focussed only on the patient-facing portion, or user interface, of the MobiGuide app.…”

Maternal and Neonatal Outcomes of SARS-CoV-2 Infection in a Cohort of Pregnant Women with Comorbid Disorders

“…We used the Brazilian Diabetes Society criteria for gestational diabetes according to which it is defined as fasting glycemia of 92–125 mg/dL during the first trimester, or 1-h plasma glucose >180 mg/dL or 2-h plasma glucose of 153–199 following a 75 g glucose load during the second and third trimesters [ 8 , 9 ]. This study defined HIV using the following testing algorithm [ 10 ]: 1.…”