Gestational DiabetesNCT07345143
Artificial Intelligence and Gestacional Diabetes
A study testing monitoring model for women with gestacional diabetes using pharmacological therapy in people with Gestational Diabetes and Macrosomia, Fetal, run by JOSE FERNANDO VILELA-MARTIN at 1 site in Brazil.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 40 years, women only
- Conditions the study is for
- Gestational Diabetes, Macrosomia, Fetal
- What is being tested?
- monitoring model for women with gestacional diabetes using pharmacological therapy
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteBrazil
- Study dates
- 1 Jun 2024to 20 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 40 years
- Women only
- No healthy volunteers
- Adult
Who may be able to join
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Brazil1 site
Fundação Faculdade Regional de Medicina de São José do Rio Preto
São José do Rio Preto, São Paulo, 15090-000
About this study
Official title: Artificial Intelligence in Education and Monitoring Women With Gestational Diabetes
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.
- Conditions
- Gestational Diabetes, Macrosomia, Fetal
- Being tested
- monitoring model for women with gestacional diabetes using pharmacological therapy
- Keywords
- artificial intelligence, gestacional diabetes, machine learning, glucose monitoria, glucose sensor
- Medical subject headings
- Diabetes, Gestational, Fetal Macrosomia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
monitoring model for women with gestacional diabetes using pharmacological therapyOther
Also called artificial intelligence for women with gestacional diabetes
Artificial Intelligence modell through WhatsApp® to remote monitoring gestacional diabetes in insulin treatment, focusing on disease education, glycemic control monitoring, and therapeutic interventions.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
fetal death
Fetal death resulting from metabolic changes caused by gestational diabetes
Measured: From the moment of randomization to delivery (until 40 weeks of pregnancy)
Fetal birth weight
Fetal weight at birth assessed using a precision scale.
Measured: 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
Measured: 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.
Measured: From the moment of randomization to delivery (until 40 weeks of pregnancy)
Secondary outcomes (5)
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.
Measured: From the moment of randomization to delivery (until 40 weeks of pregnancy), and from birth to 48 hours postpartum
mother weight gain
Maternal weight gain assessed during the gestational follow-up period up to delivery.
Measured: 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
Measured: 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.
Measured: 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.
Measured: From the moment of randomization to delivery (until 40 weeks of pregnancy).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jan 2026
- Study start1 Jun 2024
- Primary completion20 Dec 2026 (estimated)
- Study completion20 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Gestational DiabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- JOSE FERNANDO VILELA-MARTIN
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, JOSE FERNANDO VILELA-MARTIN, Hospital de Base
- Investigators
- José F Vilela-Martin, MD, PhD, Principal investigator, Hospital de BaseVanessa V Goulart, MD, MSc, Principal investigator, Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, BrazilLigia C Junqueira, MD, Study chair, Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, BrazilAmanda T Lotierzo, MD, Study chair, Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, BrazilCarolina C Amorim, MD, Study chair, Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, BrazilMaria Amalia BC Cançado, MD, Study chair, Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, BrazilLeticia A Mantoani, student, Study chair, Hospital de Base, Sao Jose do Rio Preto, Sao Paulo, BrazilRodrigo F Zancaner, Study chair, Chamoudr company
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07345143
- Sponsor's protocol ID
- 78204024.6.0000.5415.
- Status last verified
- Jan 2025
- First submitted
- 13 Nov 2025
- Last updated
- 15 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
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 35297766Akazawa 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 34629152Grunebaum 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 36924907Sweeting 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 35041752Ye 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 35613728Mistry 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 34505412Ugwudike 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 37560815HAPO 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
Study at a glance
From the registry record, last updated by the sponsor on 15 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence in Education and Monitoring Women With Gestational Diabetes
- ClinicalTrials.gov ID
- NCT07345143
- Acronym / sponsor ID
- 78204024.6.0000.5415.
- Conditions
- Gestational Diabetes, Macrosomia, Fetal
- Keywords
- artificial intelligence, gestacional diabetes, machine learning, glucose monitoria, glucose sensor
- Study type
- Interventional
- Recruitment status
- Enrolling by invitation
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- JOSE FERNANDO VILELA-MARTINUniversity, hospital or other
- Study start
- 1 Jun 2024 (actual)
- Primary completion
- 20 Dec 2026 (estimated)
- Study completion
- 20 Dec 2027 (estimated)
- First posted
- 15 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Device group: Case group (AI model use) | Experimental | Other: monitoring model for women with gestacional diabetes using pharmacological therapyCase 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. |
Who may qualify
- Ages
- 18 to 40 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| monitoring model for women with gestacional diabetes using pharmacological therapyAlso: artificial intelligence for women with gestacional diabetesArtificial Intelligence modell through WhatsApp® to remote monitoring gestacional diabetes in insulin treatment, focusing on disease education, glycemic control monitoring, and therapeutic interventions. | Other | Device group: Case group (AI model use) |
Primary outcome
fetal death
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)
Fetal death resulting from metabolic changes caused by gestational diabetes
Fetal birth weight
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)
Fetal weight at birth assessed using a precision scale.
neonatal hypoglycemia
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.
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
glycemic control
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)
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.
5 secondary outcomes
admission of the newborn to the intensive care unit
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy), and from birth to 48 hours postpartum
mother weight gain
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
gestational age at delivery
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
route of delivery
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
Blood pressure
Time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jan 2026.
- Background: PubMed 35297766Dhombres 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.
- Background: PubMed 34629152Akazawa 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.
- Background: PubMed 36924907Grunebaum 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.
- Background: PubMed 35041752Sweeting 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.
- Background: PubMed 35613728Ye 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.
- Background: PubMed 34505412Mistry 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.
- Background: PubMed 37560815Ugwudike 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.
- Background: PubMed 18463375HAPO 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
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
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing monitoring model for women with gestacional diabetes using pharmacological therapy in people with Gestational Diabetes and Macrosomia, Fetal, run by JOSE FERNANDO VILELA-MARTIN at 1 site in Brazil.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 15 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Brazil. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
fetal death (time frame: From the moment of randomization to delivery (until 40 weeks of pregnancy)). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07345143, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jan 2026.