Artificial Intelligence and Gestacional Diabetes

NCT07345143 | Enrolling By Invitation DMC

• 1 other identifier: 78204024.6.0000.5415.
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Gestational Diabetes; Macrosomia, Fetal

Keywords

artificial intelligence; gestacional diabetes; machine learning; glucose monitoria; glucose sensor

Outcome Measures

Primary Outcomes (4)

• 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)

Secondary Outcomes (5)

• admission of the newborn to the intensive care unit

  From the moment of randomization to delivery (until 40 weeks of pregnancy), and from birth to 48 hours postpartum

• mother weight gain

  From the moment of randomization to delivery (until 40 weeks of pregnancy).

• gestational age at delivery

  From the moment of randomization to delivery (until 40 weeks of pregnancy).

• route of delivery

  From the moment of randomization to delivery (until 40 weeks of pregnancy).

• Blood pressure

  From the moment of randomization to delivery (until 40 weeks of pregnancy).

Study Arms (2)

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.

Other: monitoring model for women with gestacional diabetes using pharmacological therapy

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.

Interventions

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.

Also known as: artificial intelligence for women with gestacional diabetes

Device group: Case group (AI model use)

Eligibility Criteria

• Age: 18 Years - 40 Years
• Gender Eligibility Details: Only Women
• Healthy Volunteers: No
• Age Groups: Adult (18-64)

You may qualify if:

• Gestacional diabetes women with gestational age of up to 28 weeks and 6 days
• Gestacional diabetes women who sign the free and informed consent form

You may not qualify if:

• Gestational age greater than 28 completed weeks at the first consultation
• Participants with overt DM (fasting glucose \> 126 mg/dl or postprandial \> 200 mg/dl)
• Unknown outcome.

Sponsors & Collaborators

• JOSE FERNANDO VILELA-MARTIN: lead

Study Sites (1)

Fundação Faculdade Regional de Medicina de São José do Rio Preto

São José do Rio Preto, São Paulo, 15090-000, Brazil

Location

Related Publications (8)

• 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.

  PMID: 35297766 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.

  PMID: 34629152 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.

  PMID: 36924907 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.

  PMID: 35041752 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.

  PMID: 35613728 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.

  PMID: 34505412 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.

  PMID: 37560815 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.

  PMID: 18463375 BACKGROUND

MeSH Terms

Conditions

Diabetes, Gestational; Fetal Macrosomia

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Fetal Diseases; Pregnancy in Diabetics; Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Diabetes Complications; Birth Weight; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• 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

  STUDY CHAIR

• 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

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: TREATMENT
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: MD, PHD

Study Record Dates

• First Submitted: November 13, 2025
• First Posted: January 15, 2026
• Study Start: June 1, 2024
• Primary Completion (Estimated): December 20, 2026
• Study Completion (Estimated): December 20, 2027
• Last Updated: January 15, 2026

Record last verified: 2025-01

Locations

BR (1)