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Artificial intelligence (AI) technology can assist medical teams in remote monitoring and continuing education of women with gestational diabetes (GDM), potentially improving adherence to interventions and impacting outcomes. An AI remote monitoring model called "monitoring model for women with GDM using pharmacological therapy," created by the ChamouDr technical team, will be analyzed focusing on disease education, glycemic control monitoring, and therapeutic interventions. Women diagnosed with GDM are invited to participate in the study and sign a free and informed consent form. The AI tool is installed on the pregnant woman's cell phone, who receives instructions to collect capillary blood glucose 6 times a day according to the protocol, at home, and report the results via WhatsApp to the study tool. Algorithm generated by the AI model based on self monitoring of blood glucose (SMBG) informs about diabetes control in the last week. The dashboard is accessible via a web browser, and signals: in green and red for patients with satisfactory and unsatisfactory control, respectively. Thus, the AI model optimizes the team's time in analyzing and treating patients appropriately in a simple, cost-effective, and accessible way.
AI technology can assist medical teams in remote monitoring and continuing education of women with GDM. Objective: To analyze the results of using an AI model in remote monitoring and continuing education of women with GDM and pharmacological treatment, correlating them with clinical outcomes for the mother-fetus binomial. Methods: prospective, longitudinal, interventional clinical study approved by the local ethics committee. Patients signed a consent form to participate. An AI remote monitoring model called "monitoring model for women with GDM using pharmacological therapy," created by the ChamouDr technical team, will be analyzed focusing on disease education, glycemic control monitoring, and therapeutic interventions. The modell uses WhatsApp®, through a structured chatbot and AI resources, to communicate with the participant. Comparative analyses will be conducted between two groups of 100 pregnant women with GDM on insulin therapy, followed in the high-risk prenatal clinic of the Obstetrics Department of a tertiary hospital: case group using the AI model versus control group, composed of patients previously monitored under conventional in-person supervision, without the use of this technology. Algorithm generated by the AI model based on SMBG informs about diabetes control in the last week. The dashboard is accessible via a web browser, and signals: in green and red for patients with satisfactory and unsatisfactory control, respectively. Thus, the AI model optimizes the team's time in analyzing and treating patients appropriately in a simple, cost-effective, and accessible way.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Device group: Case group (AI model use) | Experimental | Case group - Device group. AI model to monitor glucose control and send education to treat women with gestacional diabetes in insulin treatment. |
|
| Control group (no use of AI model) | No Intervention | No device group: Control group - women with gestacional diabetes in insulin treatment under conventional treatment, without the AI model use. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| monitoring model for women with gestacional diabetes using pharmacological therapy | Other | Artificial Intelligence modell through WhatsApp® to remote monitoring gestacional diabetes in insulin treatment, focusing on disease education, glycemic control monitoring, and therapeutic interventions. |
| Measure | Description | Time Frame |
|---|---|---|
| fetal death | Fetal death resulting from metabolic changes caused by gestational diabetes | From the moment of randomization to delivery (until 40 weeks of pregnancy) |
| Fetal birth weight | Fetal weight at birth assessed using a precision scale. | From the moment of randomization to delivery (until 40 weeks of pregnancy) |
| neonatal hypoglycemia | Neonatal hypoglycemia is the abnormal reduction of glucose in the newborn's blood to levels considered insufficient to meet the metabolic needs of the brain and other tissues. Plasma glucose parameters: < 40 mg/dL in the first 4 hours of life, < 45 mg/dL between 4 and 24 hours of life, After 24 hours, values < 50-60 mg/dL | From the moment of randomization to delivery (until 40 weeks of pregnancy), and Assessment of neonatal blood glucose levels from birth up to 48 hours post-birth. |
| glycemic control | Glycemic control will be evaluated according to capillary glucose measurements that are taken 6 times a day: fasting, before and 1 hour after meals, following the target ranges of 70 to 95 mg/dL fasting; 70 ton 100 mg/dL pre-prandial; and 100 to 140 mg/dL post-prandial. | From the moment of randomization to delivery (until 40 weeks of pregnancy) |
| Measure | Description | Time Frame |
|---|---|---|
| admission of the newborn to the intensive care unit | The need for the newborn to be admitted to an intensive care unit due to metabolic disorders associated with poor maternal glycemic control. | From the moment of randomization to delivery (until 40 weeks of pregnancy), and from birth to 48 hours postpartum |
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Inclusion Criteria:
Exclusion Criteria:
Only Women
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| Name | Affiliation | Role |
|---|---|---|
| José F Vilela-Martin, MD, PhD | Hospital de Base | Principal Investigator |
| Vanessa V Goulart, MD, MSc | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Principal Investigator |
| Ligia C Junqueira, MD | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Study Chair |
| Amanda T Lotierzo, MD | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Study Chair |
| Carolina C Amorim, MD | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Study Chair |
| Maria Amalia BC Cançado, MD | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Study Chair |
| Leticia A Mantoani, student | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Study Chair |
| Rodrigo F Zancaner | Chamoudr company | Study Chair |
| Rafael Beolchi | Chamoudr company |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Fundação Faculdade Regional de Medicina de São José do Rio Preto | São José do Rio Preto | São Paulo | 15090-000 | Brazil |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 35297766 | Background | Dhombres F, Bonnard J, Bailly K, Maurice P, Papageorghiou AT, Jouannic JM. Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review. J Med Internet Res. 2022 Apr 20;24(4):e35465. doi: 10.2196/35465. | |
| 34629152 | Background | Akazawa M, Hashimoto K. Artificial intelligence in gynecologic cancers: Current status and future challenges - A systematic review. Artif Intell Med. 2021 Oct;120:102164. doi: 10.1016/j.artmed.2021.102164. Epub 2021 Sep 3. |
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| ID | Term |
|---|---|
| D016640 | Diabetes, Gestational |
| D005320 | Fetal Macrosomia |
| ID | Term |
|---|---|
| D011248 | Pregnancy Complications |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D003920 | Diabetes Mellitus |
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| ID | Term |
|---|---|
| D001185 | Artificial Intelligence |
| ID | Term |
|---|---|
| D000465 | Algorithms |
| D055641 | Mathematical Concepts |
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| mother weight gain |
Maternal weight gain assessed during the gestational follow-up period up to delivery. |
| From the moment of randomization to delivery (until 40 weeks of pregnancy). |
| gestational age at delivery | Gestational age at the time of natural childbirth or cesarean section in weeks | From the moment of randomization to delivery (until 40 weeks of pregnancy). |
| route of delivery | Description of whether it was a natural birth or a cesarean section. | From the moment of randomization to delivery (until 40 weeks of pregnancy). |
| Blood pressure | Evaluate if hypertension is present and assess blood pressure levels during pregnancy and up to delivery. | From the moment of randomization to delivery (until 40 weeks of pregnancy). |
| Lucas F Queiroz | Chamoudr company | Study Chair |
| Luciana N Cosenso-Martin, MD, PhD | Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil | Study Chair |
| 36924907 | Background | Grunebaum A, Chervenak J, Pollet SL, Katz A, Chervenak FA. The exciting potential for ChatGPT in obstetrics and gynecology. Am J Obstet Gynecol. 2023 Jun;228(6):696-705. doi: 10.1016/j.ajog.2023.03.009. Epub 2023 Mar 15. |
| 35041752 | Background | Sweeting A, Wong J, Murphy HR, Ross GP. A Clinical Update on Gestational Diabetes Mellitus. Endocr Rev. 2022 Sep 26;43(5):763-793. doi: 10.1210/endrev/bnac003. |
| 35613728 | Background | Ye W, Luo C, Huang J, Li C, Liu Z, Liu F. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ. 2022 May 25;377:e067946. doi: 10.1136/bmj-2021-067946. |
| 34505412 | Background | Mistry SK, Das Gupta R, Alam S, Kaur K, Shamim AA, Puthussery S. Gestational diabetes mellitus (GDM) and adverse pregnancy outcome in South Asia: A systematic review. Endocrinol Diabetes Metab. 2021 Oct;4(4):e00285. doi: 10.1002/edm2.285. Epub 2021 Jul 3. |
| 37560815 | Background | Ugwudike B, Kwok M. Update on gestational diabetes and adverse pregnancy outcomes. Curr Opin Obstet Gynecol. 2023 Oct 1;35(5):453-459. doi: 10.1097/GCO.0000000000000901. Epub 2023 Aug 9. |
| 18463375 | Background | HAPO Study Cooperative Research Group; Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, Hadden DR, McCance DR, Hod M, McIntyre HD, Oats JJ, Persson B, Rogers MS, Sacks DA. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008 May 8;358(19):1991-2002. doi: 10.1056/NEJMoa0707943. |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
| D005315 | Fetal Diseases |
| D011254 | Pregnancy in Diabetics |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D048909 | Diabetes Complications |
| D001724 | Birth Weight |
| D001835 | Body Weight |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |