CClinicalTrials.gg
Enrolling by invitationNCT07345143Updated Jan 15, 2026

Artificial Intelligence and Gestacional Diabetes

An interventional study of monitoring model for women with gestacional diabetes using pharmacological therapy in Gestational Diabetes and Macrosomia, Fetal, sponsored by JOSE FERNANDO VILELA-MARTIN. Enrolling by invitation at 1 site in Brazil. Open to female participants aged 18 Years to 40 Years. Per ClinicalTrials.gov, last updated 2026-01-15.

Sponsored by JOSE FERNANDO VILELA-MARTIN · Not applicable, Interventional, and Treatment

Phase
Not applicable
Study type
Interventional
Enrollment
100
Allocation
Non-randomized
Ages
18 Years to 40 Years
Sex
Female
01

Study 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.

Read the detailed description

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.

02

Conditions studied

  • Gestational Diabetes
  • Macrosomia, Fetal

Keywords

  • artificial intelligence
  • gestacional diabetes
  • machine learning
  • glucose monitoria
  • glucose sensor
03

Who can participate

Ages eligible
18 Years to 40 Years
Sexes eligible
Female
Accepts healthy volunteers
No

Inclusion criteria

  • 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

Exclusion criteria

Exclusion Criteria:

  • 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.
04

Study design

Phase
Not applicable
Primary purpose
Treatment
Allocation
Non-randomized
Intervention model
Parallel assignment
Masking
None (open label)
Enrollment
100 participants (estimated)

Study arms

  • Experimental
    Device group: Case group (AI model use)

    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

  • No intervention
    Control group (no use of AI model)

    No device group: Control group - women with gestacional diabetes in insulin treatment under conventional treatment, without the AI model use.

Interventions

  • Othermonitoring model for women with gestacional diabetes using pharmacological therapy

    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

05

What researchers measure

Primary outcomes

  1. fetal death

    Fetal death resulting from metabolic changes caused by gestational diabetes

    Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)

  2. Fetal birth weight

    Fetal weight at birth assessed using a precision scale.

    Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)

  3. 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

    Time frame: 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.

  4. 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.

    Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)

Secondary outcomes

  1. 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.

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

  2. mother weight gain

    Maternal weight gain assessed during the gestational follow-up period up to delivery.

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

  3. gestational age at delivery

    Gestational age at the time of natural childbirth or cesarean section in weeks

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

  4. route of delivery

    Description of whether it was a natural birth or a cesarean section.

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

  5. Blood pressure

    Evaluate if hypertension is present and assess blood pressure levels during pregnancy and up to delivery.

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

06

Study locations

1 site
  • Fundação Faculdade Regional de Medicina de São José do Rio Preto
    São José do Rio Preto, São Paulo 15090-000, Brazil
07

References and documents

Publications

  • 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. PubMed 35297766 ↗
  • 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. PubMed 34629152 ↗
  • 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. PubMed 36924907 ↗
  • 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. PubMed 35041752 ↗
  • 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. PubMed 35613728 ↗
  • 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. PubMed 34505412 ↗
  • 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. PubMed 37560815 ↗
  • 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. PubMed 18463375 ↗

Individual participant data

Plan to share: Undecided

08

Registry details

Key details

Study ID
NCT07345143
Lead sponsor
JOSE FERNANDO VILELA-MARTIN
Responsible party
JOSE FERNANDO VILELA-MARTIN (MD, PHD, Hospital de Base) — Sponsor-investigator
First posted
Jan 15, 2026
Start date
Jun 1, 2024
Primary completion
Dec 20, 2026 (estimated)
Completion
Dec 20, 2027 (estimated)
Last update
Jan 15, 2026

Study contacts

José F Vilela-Martin, MD, PhD
principal investigator · Hospital de Base
Vanessa V Goulart, MD, MSc
principal investigator · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil
Ligia C Junqueira, MD
study chair · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil
Amanda T Lotierzo, MD
study chair · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil
Carolina C Amorim, MD
study chair · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil
Maria Amalia BC Cançado, MD
study chair · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil
Leticia A Mantoani, student
study chair · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil
Rodrigo F Zancaner
study chair · Chamoudr company
Rafael Beolchi
study chair · Chamoudr company
Lucas F Queiroz
study chair · Chamoudr company
Luciana N Cosenso-Martin, MD, PhD
study chair · Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, Brazil

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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