The goal of this clinical study is to evaluate the effectiveness of an AI agent in diagnosing and predicting diseases using electronic health records (EHR) and multimodal imaging data. The AI agent leverages advanced machine learning algorithms to process and analyze diverse health data sources, aiming to assist healthcare providers in making more accurate diagnoses and predictions.
Participants must have comprehensive electronic health records (EHR) available, including demographic information, medical history, and laboratory results.
Participants must have available multimodal imaging data (e.g., X-rays, CT scans, MRIs, ultrasounds) relevant to their health condition.
Participants must have a confirmed diagnosis of one or more diseases or health conditions based on clinical records or imaging data.
Patients must provide consent for the use of their historical health data for research purposes.
Participants with ambiguous or unverifiable diagnoses that cannot be accurately categorized.
Duplicate or redundant patient data (e.g., repeated records of the same patient without clear differentiation).
The participants for this study will be selected from multiple healthcare centers and hospitals that maintain comprehensive electronic health records (EHR) and multimodal imaging data. The study population will include patients who have a variety of diseases or health conditions, with data available for diagnosis and disease progression. Participants will have confirmed diagnoses based on clinical records or imaging data, including but not limited to conditions captured by X-rays, CT scans, MRIs, and ultrasounds. Both those with complex health conditions and those with more common illnesses will be included to evaluate the AI system's diagnostic and predictive capabilities across a broad spectrum of cases.Participants from these centers will provide historical health data, and there will be no active intervention beyond the use of their existing clinical and imaging data for training and testing the AI system.
Case-only
Retrospective
Not listed
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).
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.
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.
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.
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The Eye Hospital of Wenzhou Medical University
Lead sponsor
Wenzhou Medical University
Sponsor institution
