GenotypeNCT06791421
AI-Driven Genotype Prediction Using EHR and Multimodal Data
An observational study of Genotype, run by The Eye Hospital of Wenzhou Medical University at 4 sites in China.
Trial summary
The goal of this clinical study is to explore the potential of using electronic health records (EHR) and multimodal data (such as imaging, lab results, and clinical history) to predict a patient's genotype. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Genotype
- What is being tested?
- AI-Predictng Model
- How many people
- 100,000 participants
- Where
- 4 sitesChina
- Study dates
- 1 Jul 2023to Jun 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: The study population will be selected from multiple healthcare centers that maintain comprehensive electronic health records (EHR) and have access to multimodal clinical data, including lab results, medical imaging (e.g., X-rays, MRIs, CT scans), and medical history. Participants will be individuals with a variety of health conditions for which genotype information is relevant, although no specific genetic characteristics will be used for selection. The focus will be on utilizing the available health data to predict genetic information through the AI model. The study aims to evaluate the accuracy and utility of using non-genetic data, such as EHR and multimodal imaging, for predicting patient genotypes, which may provide an alternative approach to traditional genetic testing methods.
Who may be able to join
- Participants must have comprehensive electronic health records (EHR), including medical history, lab results, and relevant imaging data (e.g., X-rays, MRIs, CT scans).
- Participants must have existing genetic testing data available for comparison, if applicable.
- Participants must be willing to provide consent for the use of their health data in the study.
- Participants must have no active intervention related to genetic testing or prediction during the study period.
- Participants should have complete and verifiable health data to allow for accurate prediction by the AI model.
Who cannot join
- Participants without available EHR, lab results, or imaging data.
- Participants with ambiguous, inaccurate, or unverifiable genetic testing results that cannot be used for comparison.
- Patients with significant discrepancies or missing data that would prevent the AI model from making accurate predictions.
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites, 3 recruiting
- Recruiting
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
Guangzhou, Guangdong
Yunfang Yu, +86 020-81332199, yuyf9@mail.sysu.edu.cn
- Recruiting
Sun Yat-sen University Cancer Hospital
Guangzhou, Guangdong
Yuxing Lu, +86 13161233730, yxlu0613@gmail.com
- Completed
First Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
- Recruiting
Second Affiliated Hospital of Wenzhou Medical University
Wenzhou, Zhejiang
Sian Liu, +86-0577-88002888, liusan@mail3.sysu.edu.cn
About this study
Official title: Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models
The goal of this clinical study is to explore the potential of using electronic health records (EHR) and multimodal data (such as imaging, lab results, and clinical history) to predict a patient's genotype. The study will evaluate whether predictive models based on this non-genetic data can accurately infer genetic information, which traditionally requires direct genetic testing.
Read the detailed description
This multi-center, retrospective clinical study aims to evaluate the use of electronic health records (EHR) and multimodal data (such as clinical lab results, imaging data, and medical history) in predicting a patient's genotype. The primary objective of the study is to develop an AI-based prediction model that can infer genetic information by analyzing available health data, eliminating the need for direct genetic testing.The AI model will be trained to process and integrate large datasets, including EHR, lab results, and imaging data such as X-rays, MRIs, and ultrasounds, in order to predict genotypic information. The study will compare the AI-based predictions to actual genetic testing results to evaluate the accuracy of the model. If successful, this method could provide a non-invasive, cost-effective tool for genotype prediction, which could be used in personalized medicine, early disease diagnosis, and risk stratification.Participants will not undergo any genetic testing as part of the study. Instead, their historical medical data will be analyzed by the AI system to predict genetic information and associated disease risks. The study will assess the model's ability to predict genetic predispositions to various health conditions based on the available health data. By doing so, the study aims to advance the use of AI in clinical decision-making and genetic diagnostics.
- Conditions
- Genotype
- Being tested
- AI-Predictng Model
- Keywords
- AI, AI-prediction, Genotype, Multimodal data
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Predictng ModelOther
The intervention in this study involves an AI-based predictive model designed to analyze and integrate patient electronic health records (EHR), clinical lab results, and multimodal imaging data (e.g., X-rays, MRIs, CT scans). The AI model is trained to predict a patient's genotype based on these non-genetic data sources. This model uses machine learning algorithms to detect patterns and infer genetic information that would traditionally require direct genetic testing. There are no active treatments or genetic tests involved in this intervention; rather, the AI system serves as a tool to predict genetic information from available clinical data, offering a non-invasive and potentially more accessible alternative to genetic testing.
| Study group | Role | What they receive |
|---|---|---|
| AI-Based Genotype Prediction Using EHR and Multimodal Data | This cohort consists of patients whose historical health data, including electronic health records (EHR), clinical lab results, and multimodal imaging data (such as X-rays, MRIs, and CT scans), will be analyzed by an AI-based prediction model to predict their genotype. There are no active interventions in this cohort, as the study aims to use non-genetic health data to infer genetic information. Participants will not undergo genetic testing but will provide their health data for analysis by the AI system. The goal of this group is to assess the accuracy of the AI model in predicting genotypes and identifying genetic predispositions to various diseases based on available health data. Receives: Other: AI-Predictng Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Curve (AUC)
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
Measured: 1 year
F1 Score
The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.
Measured: 1 year
Secondary outcomes (2)
Sensitivity (True Positive Rate)
Sensitivity measures how well the AI model identifies true positive cases, such as correctly diagnosing pregnant women with complications or identifying neonatal disorders.
Measured: 1 year
Specificity (True Negative Rate)
Specificity measures the ability of the AI model to correctly identify cases without diseases, ensuring that healthy mothers and infants are correctly identified as negative.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2025
- Study start1 Jul 2023
- Primary completionJun 2025 (estimated)
- Study completionJun 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GenotypeStudies 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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The Eye Hospital of Wenzhou Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Kang Zhang, Wenzhou Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06791421
- Sponsor's protocol ID
- Genotype
- Status last verified
- Apr 2025
- First submitted
- 19 Jan 2025
- Last updated
- 17 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Patient Genotypes Using Electronic Health Records and Multimodal Data Through AI-Based Models
- ClinicalTrials.gov ID
- NCT06791421
- Acronym / sponsor ID
- Genotype
- Conditions
- Genotype
- Keywords
- AI, AI-prediction, Genotype, Multimodal data
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- The Eye Hospital of Wenzhou Medical UniversityUniversity, hospital or other
- Study start
- 1 Jul 2023 (actual)
- Primary completion
- Jun 2025 (estimated)
- Study completion
- Jun 2025 (estimated)
- First posted
- 24 Jan 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.
The goal of this clinical study is to explore the potential of using electronic health records (EHR) and multimodal data (such as imaging, lab results, and clinical history) to predict a patient's genotype. The study will evaluate whether predictive models based on this non-genetic data can accurately infer genetic information, which traditionally requires direct genetic testing.
Detailed description
This multi-center, retrospective clinical study aims to evaluate the use of electronic health records (EHR) and multimodal data (such as clinical lab results, imaging data, and medical history) in predicting a patient's genotype. The primary objective of the study is to develop an AI-based prediction model that can infer genetic information by analyzing available health data, eliminating the need for direct genetic testing.The AI model will be trained to process and integrate large datasets, including EHR, lab results, and imaging data such as X-rays, MRIs, and ultrasounds, in order to predict genotypic information. The study will compare the AI-based predictions to actual genetic testing results to evaluate the accuracy of the model. If successful, this method could provide a non-invasive, cost-effective tool for genotype prediction, which could be used in personalized medicine, early disease diagnosis, and risk stratification.Participants will not undergo any genetic testing as part of the study. Instead, their historical medical data will be analyzed by the AI system to predict genetic information and associated disease risks. The study will assess the model's ability to predict genetic predispositions to various health conditions based on the available health data. By doing so, the study aims to advance the use of AI in clinical decision-making and genetic diagnostics.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI-Based Genotype Prediction Using EHR and Multimodal Data | – | Other: AI-Predictng ModelThis cohort consists of patients whose historical health data, including electronic health records (EHR), clinical lab results, and multimodal imaging data (such as X-rays, MRIs, and CT scans), will be analyzed by an AI-based prediction model to predict their genotype. There are no active interventions in this cohort, as the study aims to use non-genetic health data to infer genetic information. Participants will not undergo genetic testing but will provide their health data for analysis by the AI system. The goal of this group is to assess the accuracy of the AI model in predicting genotypes and identifying genetic predispositions to various diseases based on available health data. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- The study population will be selected from multiple healthcare centers that maintain comprehensive electronic health records (EHR) and have access to multimodal clinical data, including lab results, medical imaging (e.g., X-rays, MRIs, CT scans), and medical history. Participants will be individuals with a variety of health conditions for which genotype information is relevant, although no specific genetic characteristics will be used for selection. The focus will be on utilizing the available health data to predict genetic information through the AI model. The study aims to evaluate the accuracy and utility of using non-genetic data, such as EHR and multimodal imaging, for predicting patient genotypes, which may provide an alternative approach to traditional genetic testing methods.
Key inclusion criteria (5 listed)
- Participants must have comprehensive electronic health records (EHR), including medical history, lab results, and relevant imaging data (e.g., X-rays, MRIs, CT scans).
- Participants must have existing genetic testing data available for comparison, if applicable.
- Participants must be willing to provide consent for the use of their health data in the study.
- Participants must have no active intervention related to genetic testing or prediction during the study period.
- Participants should have complete and verifiable health data to allow for accurate prediction by the AI model.
Key exclusion criteria (3 listed)
- Participants without available EHR, lab results, or imaging data.
- Participants with ambiguous, inaccurate, or unverifiable genetic testing results that cannot be used for comparison.
- Patients with significant discrepancies or missing data that would prevent the AI model from making accurate predictions.
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 |
|---|---|---|
| AI-Predictng ModelThe intervention in this study involves an AI-based predictive model designed to analyze and integrate patient electronic health records (EHR), clinical lab results, and multimodal imaging data (e.g., X-rays, MRIs, CT scans). The AI model is trained to predict a patient's genotype based on these non-genetic data sources. This model uses machine learning algorithms to detect patterns and infer genetic information that would traditionally require direct genetic testing. There are no active treatments or genetic tests involved in this intervention; rather, the AI system serves as a tool to predict genetic information from available clinical data, offering a non-invasive and potentially more accessible alternative to genetic testing. | Other | AI-Based Genotype Prediction Using EHR and Multimodal Data |
Primary outcome
Area Under the Curve (AUC)
Time frame: 1 year
AUC of the ROC curve, used to quantify diagnostic accuracy. No unit (a ratio or percentage, typically expressed as a number between 0 and 1).
F1 Score
Time frame: 1 year
The F1 score is the harmonic mean of precision and sensitivity (recall). It is a good measure of the model's ability to identify both true positives and minimize false positives, especially in cases where the classes are imbalanced (e.g., when the number of healthy cases is much higher than disease cases). The F1 score ranges from 0 to 1, with 1 indicating perfect precision and recall.
2 secondary outcomes
Sensitivity (True Positive Rate)
Time frame: 1 year
Specificity (True Negative Rate)
Time frame: 1 year
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 17 Apr 2025.
- Individual participant dataSharing planned: No
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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.
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Genotype, run by The Eye Hospital of Wenzhou Medical University at 4 sites in China.
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 5 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: looking for participants now, as of the sponsor's last update on 17 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in China. 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?
Contact Fei Liu, MD (+86 13810512704, liufei_2359@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Curve (AUC) (time frame: 1 year). The study lists 2 primary outcomes in all. 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 17 Apr 2025.