Intracranial AnomaliesALYSSIANCT07261618
AI-Assisted 2D Fetal Brain Ultrasound for Intracranial Anomaly Detection
An observational study of Intracranial Anomalies and Ultrasound Imaging, run by Sanliurfa Mehmet Akif Inan Education and Research Hospital at 1 site in Turkey (Türkiye).
Trial summary
Timely detection of fetal brain anomalies is critical for improving prenatal counseling and postnatal neurological outcomes. Ultrasonography is the most commonly used and effective imaging method for evaluating fetal structures; however, diagnostic accuracy can be affected by operator experience, fetal position, and… Read more about this study
- Who is it for?
- Ages 18 to 45 years, women onlyHealthy volunteers can take part
- Conditions the study is for
- Intracranial Anomalies, Ultrasound Imaging, Artificial Intelligence
- What is being tested?
- Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound
- How many people
- 800 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 15 Oct 2025to 30 Nov 2025
- Phase
- Observational
- 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 45 years
- Women only
- Healthy volunteers accepted
- Adult
Study population: This study will use archived and anonymized 2D fetal brain ultrasound images obtained during routine prenatal screening examinations conducted between the 18th and 24th weeks of gestation. The dataset represents a diverse population of pregnant individuals aged 18-45 years who underwent standard obstetric ultrasound evaluations. All images were acquired as part of routine clinical care and stored in the institutional digital archive. Only diagnostically adequate images clearly displaying the lateral ventricles and other intracranial regions were included. The study population therefore consists of ultrasound records rather than direct human participants, ensuring complete anonymity and protection of personal data.
Who may be able to join
- Archived 2D fetal brain ultrasound images obtained during routine prenatal examinations.
- Gestational age between 18 and 24 weeks at the time of imaging.
- Maternal age between 18 and 45 years.
- Clear visualization of the lateral ventricles and other intracranial regions.
- Images meeting diagnostic quality standards suitable for analysis.
- Fully anonymized images with no patient identifiers.
- Availability of expert assessment to classify each image as normal or abnormal.
Who cannot join
- Ultrasound images with poor diagnostic quality or motion artifacts.
- Incomplete, duplicate, or corrupted image records.
- Ambiguous gestational age or missing clinical metadata.
- Images containing any identifiable patient information.
- Cases outside the specified gestational window (before 18 or after 24 weeks).
- Images unrelated to the fetal brain (misfiled or mislabeled data).
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evaluation of an Artificial Intelligence-Assisted Diagnostic Model for the Analysis of Archived 2D Fetal Brain Ultrasound Images to Improve Detection and Standardization of Intracranial Anomalies
Timely detection of fetal brain anomalies is critical for improving prenatal counseling and postnatal neurological outcomes. Ultrasonography is the most commonly used and effective imaging method for evaluating fetal structures; however, diagnostic accuracy can be affected by operator experience, fetal position, and image quality, leading to variability in interpretation. Artificial intelligence (AI)-based image analysis offers a new opportunity to standardize diagnostic assessment and reduce subjectivity in ultrasound interpretation.
This study aims to evaluate the diagnostic accuracy and clinical applicability of an AI-assisted model (Alyssia) designed to analyze archived 2D fetal brain ultrasound images. The model will be trained and validated to distinguish between normal and abnormal intracranial findings, focusing particularly on the lateral ventricles and other relevant brain regions. The research employs an observational, retrospective design using anonymized ultrasound data obtained during routine prenatal examinations between 18 and 24 weeks of gestation.
Read more
Expert clinicians will review and label all eligible images to establish ground truth classifications for model training and validation. A deep learning-based algorithm will be developed to automatically classify these images, and its performance will be evaluated using accuracy, sensitivity, specificity, precision, and F1-score metrics. Misclassified cases will be qualitatively analyzed to determine contributing factors such as image quality, anatomical variability, and gestational differences.
By comparing AI model outputs with expert-labeled references, the study will assess the model's ability to enhance diagnostic standardization and reduce inter-observer variability. The findings are expected to provide valuable insights into the integration of AI-based decision support systems in prenatal neurosonography. Ultimately, this research aims to support earlier and more reliable detection of fetal brain anomalies, contributing to improved prenatal care and healthier outcomes for mothers and infants.
- Being tested
- Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Alyssia - AI-Assisted Diagnostic Model for Fetal Brain UltrasoundDiagnostic test
Artificial intelligence-based diagnostic tool designed to classify archived 2D fetal brain ultrasound images as normal or abnormal to detect intracranial anomalies.
| Study group | Role | What they receive |
|---|---|---|
| Normal Fetal Brain Images | Archived 2D fetal brain ultrasound images classified as normal by expert reviewers. Receives: Diagnostic Test: Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound | |
| Abnormal Fetal Brain Images | Archived 2D fetal brain ultrasound images with confirmed intracranial anomalies, labeled by experts. Receives: Diagnostic Test: Alyssia - AI-Assisted Diagnostic Model for Fetal Brain Ultrasound |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the AI-Assisted Model (Alyssia)
The primary outcome is the diagnostic accuracy of the Alyssia artificial intelligence model in classifying archived 2D fetal brain ultrasound images as normal or abnormal. Model performance will be evaluated by comparing AI-generated classifications with expert-labeled ground truth data.
Measured: From study start to model validation (approximately 6 weeks).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Dec 2025
- Study start15 Oct 2025
- Primary completion30 Nov 2025 (estimated)
- Study completion30 Nov 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Intracranial AnomaliesStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sanliurfa Mehmet Akif Inan Education and Research Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Nefise Nazlı YENIGUL, Sanliurfa Mehmet Akif Inan Education and Research Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07261618
- Sponsor's protocol ID
- E-47749665-050.04-4465
- Other IDs
- MEF University Ethics Committe
- Acronym
- ALYSSIA
- Status last verified
- Nov 2025
- First submitted
- 21 Nov 2025
- Last updated
- 3 Dec 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of an Artificial Intelligence-Assisted Diagnostic Model for the Analysis of Archived 2D Fetal Brain Ultrasound Images to Improve Detection and Standardization of Intracranial Anomalies
- ClinicalTrials.gov ID
- NCT07261618
- Acronym / sponsor ID
- ALYSSIA / E-47749665-050.04-4465
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 800 participants (estimated)
- Lead sponsor
- Sanliurfa Mehmet Akif Inan Education and Research HospitalUniversity, hospital or other
- Study start
- 15 Oct 2025 (actual)
- Primary completion
- 30 Nov 2025 (estimated)
- Study completion
- 30 Nov 2025 (estimated)
- First posted
- 3 Dec 2025 (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.
Timely detection of fetal brain anomalies is critical for improving prenatal counseling and postnatal neurological outcomes. Ultrasonography is the most commonly used and effective imaging method for evaluating fetal structures; however, diagnostic accuracy can be affected by operator experience, fetal position, and image quality, leading to variability in interpretation. Artificial intelligence (AI)-based image analysis offers a new opportunity to standardize diagnostic assessment and reduce subjectivity in ultrasound interpretation.
This study aims to evaluate the diagnostic accuracy and clinical applicability of an AI-assisted model (Alyssia) designed to analyze archived 2D fetal brain ultrasound images. The model will be trained and validated to distinguish between normal and abnormal intracranial findings, focusing particularly on the lateral ventricles and other relevant brain regions. The research employs an observational, retrospective design using anonymized ultrasound data obtained during routine prenatal examinations between 18 and 24 weeks of gestation.
Expert clinicians will review and label all eligible images to establish ground truth classifications for model training and validation. A deep learning-based algorithm will be developed to automatically classify these images, and its performance will be evaluated using accuracy, sensitivity, specificity, precision, and F1-score metrics. Misclassified cases will be qualitatively analyzed to determine contributing factors such as image quality, anatomical variability, and gestational differences.
By comparing AI model outputs with expert-labeled references, the study will assess the model's ability to enhance diagnostic standardization and reduce inter-observer variability. The findings are expected to provide valuable insights into the integration of AI-based decision support systems in prenatal neurosonography. Ultimately, this research aims to support earlier and more reliable detection of fetal brain anomalies, contributing to improved prenatal care and healthier outcomes for mothers and infants.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Normal Fetal Brain Images | – | Diagnostic Test: Alyssia - AI-Assisted Diagnostic Model for Fetal Brain UltrasoundArchived 2D fetal brain ultrasound images classified as normal by expert reviewers. |
| Abnormal Fetal Brain Images | – | Diagnostic Test: Alyssia - AI-Assisted Diagnostic Model for Fetal Brain UltrasoundArchived 2D fetal brain ultrasound images with confirmed intracranial anomalies, labeled by experts. |
Who may qualify
- Ages
- 18 to 45 years
- Sex
- Female only
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- This study will use archived and anonymized 2D fetal brain ultrasound images obtained during routine prenatal screening examinations conducted between the 18th and 24th weeks of gestation. The dataset represents a diverse population of pregnant individuals aged 18-45 years who underwent standard obstetric ultrasound evaluations. All images were acquired as part of routine clinical care and stored in the institutional digital archive. Only diagnostically adequate images clearly displaying the lateral ventricles and other intracranial regions were included. The study population therefore consists of ultrasound records rather than direct human participants, ensuring complete anonymity and protection of personal data.
Key inclusion criteria (7 listed)
- Archived 2D fetal brain ultrasound images obtained during routine prenatal examinations.
- Gestational age between 18 and 24 weeks at the time of imaging.
- Maternal age between 18 and 45 years.
- Clear visualization of the lateral ventricles and other intracranial regions.
- Images meeting diagnostic quality standards suitable for analysis.
Key exclusion criteria (6 listed)
- Ultrasound images with poor diagnostic quality or motion artifacts.
- Incomplete, duplicate, or corrupted image records.
- Ambiguous gestational age or missing clinical metadata.
- Images containing any identifiable patient information.
- Cases outside the specified gestational window (before 18 or after 24 weeks).
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 |
|---|---|---|
| Alyssia - AI-Assisted Diagnostic Model for Fetal Brain UltrasoundArtificial intelligence-based diagnostic tool designed to classify archived 2D fetal brain ultrasound images as normal or abnormal to detect intracranial anomalies. | Diagnostic test | Abnormal Fetal Brain Images, Normal Fetal Brain Images |
Primary outcome
Diagnostic Accuracy of the AI-Assisted Model (Alyssia)
Time frame: From study start to model validation (approximately 6 weeks).
The primary outcome is the diagnostic accuracy of the Alyssia artificial intelligence model in classifying archived 2D fetal brain ultrasound images as normal or abnormal. Model performance will be evaluated by comparing AI-generated classifications with expert-labeled ground truth data.
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 3 Dec 2025.
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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Intracranial Anomalies and Ultrasound Imaging, run by Sanliurfa Mehmet Akif Inan Education and Research Hospital at 1 site in Turkey (Türkiye).
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 7 inclusion and 6 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: under way, not looking for participants, as of the sponsor's last update on 3 Dec 2025. 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 Turkey (Türkiye). 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?
Diagnostic Accuracy of the AI-Assisted Model (Alyssia) (time frame: From study start to model validation (approximately 6 weeks).). 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 3 Dec 2025; AzTrials last refreshed its copy on 8 Nov 2026.