Reproductive MedicineNCT05671601
Application of Deep Learning to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Embryo Transfer
An observational study of Reproductive Medicine, run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School.
Trial summary
Aim of this research is to apply the deep learning automation based on Time-lapse imaging to jointly assess embryo development，so that it can ensure the consistency of embryo evaluation and improve the accuracy of evaluation. Read more about this study
- Who is it for?
- Ages 20 to 40 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Reproductive Medicine
- What is being tested?
- Automatic picture recognition and Manual Assessment Group
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- 30 Dec 2022to 15 Jun 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 20 to 40 years
- Women only
- Healthy volunteers accepted
- Adult
Study population: Patients are under 40 years old and need artificial intervention to have children
Who may be able to join
- (1) Age < 40 years old; (2) Routine IVF cycles; (3) Period number ≤ 2; (4) The number of ova collected is 5-15; (5) BMI: 18-25 kg/m 2, follicle stimulating hormone(FSH) ≤ 12 IU/L on the third day; (6) Patients with more than 3 high-quality embryos on Day3 and performed single blastocyst transplantation on day 5. (7) Patient without endometrial factors.
Who cannot join
- (1) Preimplantation Genetic Testing(PGT) is needed due to male infertility, ovulation cycle and chromosome abnormalities; (2) there are systemic diseases of clinical significance; (3) Pictures of blastocysts are not formed or available; (4) Incomplete or unclear image collection in prokaryotic, mitotic and blastocyst phases affected AI evaluation.
About this study
Official title: Application of Deep Learning Automation Based on Time-lapse Imaging to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Single Blastocyst Transfer
Aim of this research is to apply the deep learning automation based on Time-lapse imaging to jointly assess embryo development，so that it can ensure the consistency of embryo evaluation and improve the accuracy of evaluation.
Read the detailed description
This study is an observational prospective study after a retrospective analysis. It is a single-center study without randomization or blindness. In the early stage, 1000 patients are collected from three periods of embryo culture through Time-lapse to establish an automated joint evaluation system for the whole process of embryo development. At the later stage, the patients are divided into two groups: Time-Lapse imaging (TLI) +Artificial Intelligence(AI) assessment group and morphological assessment group. 100 patients with Day 5 single blastocyst transplantation are carried out to follow up the pregnancy outcome.
- Conditions
- Reproductive Medicine
- Being tested
- Automatic picture recognition, Manual Assessment Group
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Automatic picture recognitionDiagnostic test
A machine that processes photographs automatically taken
Manual Assessment GroupDiagnostic test
Manual recognition of pictures
| Study group | Role | What they receive |
|---|---|---|
| TLI+AI Assessment Group | Not described Receives: Diagnostic Test: Automatic picture recognition | |
| Morphological Assessment Group | Not described Receives: Diagnostic Test: Manual Assessment Group |
What is being measured
The primary outcome is the main question the study is designed to answer.
implantation rate
the probability of successful implantation of the embryo into the uterus
Measured: 2022-2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jan 2023
- Study start30 Dec 2022 (estimated)
- Primary completion15 Dec 2023 (estimated)
- Study completion15 Jun 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Reproductive MedicineStudies 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
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05671601
- Sponsor's protocol ID
- wangshanshan820
- Status last verified
- Nov 2022
- First submitted
- 5 Dec 2022
- Last updated
- 4 Jan 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 4 Jan 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Deep Learning Automation Based on Time-lapse Imaging to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Single Blastocyst Transfer
- ClinicalTrials.gov ID
- NCT05671601
- Acronym / sponsor ID
- wangshanshan820
- Conditions
- Reproductive Medicine
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical SchoolUniversity, hospital or other
- Study start
- 30 Dec 2022 (estimated)
- Primary completion
- 15 Dec 2023 (estimated)
- Study completion
- 15 Jun 2024 (estimated)
- First posted
- 4 Jan 2023 (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.
Aim of this research is to apply the deep learning automation based on Time-lapse imaging to jointly assess embryo development，so that it can ensure the consistency of embryo evaluation and improve the accuracy of evaluation.
Detailed description
This study is an observational prospective study after a retrospective analysis. It is a single-center study without randomization or blindness. In the early stage, 1000 patients are collected from three periods of embryo culture through Time-lapse to establish an automated joint evaluation system for the whole process of embryo development. At the later stage, the patients are divided into two groups: Time-Lapse imaging (TLI) +Artificial Intelligence(AI) assessment group and morphological assessment group. 100 patients with Day 5 single blastocyst transplantation are carried out to follow up the pregnancy outcome.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| TLI+AI Assessment Group | – | Diagnostic Test: Automatic picture recognition |
| Morphological Assessment Group | – | Diagnostic Test: Manual Assessment Group |
Who may qualify
- Ages
- 20 to 40 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- Patients are under 40 years old and need artificial intervention to have children
Key inclusion criteria (1 listed)
- (1) Age < 40 years old; (2) Routine IVF cycles; (3) Period number ≤ 2; (4) The number of ova collected is 5-15; (5) BMI: 18-25 kg/m 2, follicle stimulating hormone(FSH) ≤ 12 IU/L on the third day; (6) Patients with more than 3 high-quality embryos on Day3 and performed single blastocyst transplantation on day 5. (7) Patient without endometrial factors.
Key exclusion criteria (1 listed)
- (1) Preimplantation Genetic Testing(PGT) is needed due to male infertility, ovulation cycle and chromosome abnormalities; (2) there are systemic diseases of clinical significance; (3) Pictures of blastocysts are not formed or available; (4) Incomplete or unclear image collection in prokaryotic, mitotic and blastocyst phases affected AI evaluation.
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 |
|---|---|---|
| Automatic picture recognitionA machine that processes photographs automatically taken | Diagnostic test | TLI+AI Assessment Group |
| Manual Assessment GroupManual recognition of pictures | Diagnostic test | Morphological Assessment Group |
Primary outcome
implantation rate
Time frame: 2022-2023
the probability of successful implantation of the embryo into the uterus
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jan 2023.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Reproductive Medicine, run by The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 4 Jan 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
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?
implantation rate (time frame: 2022-2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 4 Jan 2023.