Health TrialsbyHealthPanel

AI-Driven Cancer Diagnosis and Prediction With EHR

ConditionTumor
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age0-90
SponsorThe Eye Hospital of Wenzhou Medical University

About this trial

This is a multi-center, clinical study designed to evaluate the application and effectiveness of an AI-assisted predictive model for identifying and diagnosing cancer, leveraging multimodal health data.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

None

Disqualifiers

Patients with incomplete or missing key electronic health record data or insufficient follow-up data.

Individuals with severe cognitive disorders or other terminal illnesses that would prevent meaningful participation.

Pregnant women (although pediatric cancers are being considered, pregnant women would be excluded for safety reasons).

Trial population

The study population consists of individuals aged 0 to 90 years who have received care at participating study centers. Participants must have comprehensive electronic health records (EHRs) available, including medical history, laboratory test results, imaging data, and genetic information (if available). Both individuals diagnosed with cancer (including pediatric and adult cancers) and healthy individuals with no history of cancer will be included in the study to evaluate the AI-assisted model's diagnostic and predictive capabilities. The study will focus on patients with complete and documented care records from the participating centers, ensuring a diverse cohort for analysis across different age groups and cancer types.

Trial design

Design model

Cohort

Time perspective

Other

Treatments tested in this trial

  • AI-Based Diagnostic and Prognostic Model

    Diagnostic test

    This intervention involves an AI system that integrates multimodal data, including patient medical history, laboratory test results, imaging data, and genetic information, to predict the risk of cancer. The system uses deep learning algorithms to provide real-time, accurate predictions, enabling early identification of cancer risks. By analyzing historical health data, the model aims to predict potential cancer developments, improving early detection and treatment outcomes.

Treatment groups

1,000,000 Participants
are divided into 2 treatment groups
Group A: Healthy Cohort1 intervention
Group B: Tumor Cohort1 intervention

Trial outcomes

Primary outcomes

1

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).

Time frame
1 year
2

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.

Time frame
1 year

Secondary outcomes

1

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.

Time frame
1 year
2

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.

Time frame
1 year

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

The Eye Hospital of Wenzhou Medical University

Lead sponsor

Wenzhou Medical University

Sponsor institution

Source ClinicalTrials.gov
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