Ophthalmic Multimodal AI-Assisted Medical Decision-Making

Condition(s) studied

Investigational drug(s) / intervention(s)

Diagnostic Test: AI-Based Diagnostic and Prognostic Model for Ocular Diseases: This intervention involves an AI system that leverages multimodal data fusion to support the clinical decision-making and evaluation of ophthalmic diseases. It integrates multi-modal data, including fundus photography, optical coherence tomography (OCT), and patient clinical records, to provide real-time, precise, and personalized diagnostic support. Unlike other models, this system utilizes a longitudinal patient dataset to predict disease progression and treatment outcomes.Key distinguishing features include: 1. Multi-Modal Data Integration: Combines imaging, clinical, and genetic data for comprehensive analysis. 2. Predictive Capability: Offers advanced prognostic predictions, enabling personalized treatment plans. 3. Deep Learning Framework: Employs state-of-the-art deep learning algorithms for improved diagnostic accuracy and efficiency. 4. Real-World Validation: Validated using a large cohort of diverse patient data, ensuring generalizability and robustness.

Study summary

This is a multi-center, retrospective clinical study designed to evaluate the application and effectiveness of an AI-assisted medical decision support system, leveraging multimodal data fusion, in ophthalmic clinical practice.

Eligibility

Primary outcome measure(s)

• Area Under the Curve (AUC) — 1 years
  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).
• Sensitivity — 1 years
  Sensitivity (also called True Positive Rate) is a measure of how well a model identifies positive instances. It is defined as the proportion of actual positive cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
• Accuracy Accuracy Accuracy — 1 years
  Accuracy measures the proportion of all correct predictions (true positives and true negatives) out of the total number of cases evaluated by the model. No unit (a ratio or percentage, typically expressed as a percentage).
• Specificity — 1 years
  Specificity (also called True Negative Rate) measures the proportion of actual negative cases correctly identified by the model. No unit (a ratio or percentage, typically expressed as a percentage).
• False Positive Rate — 1 years
  False Positive Rate (FPR) measures the proportion of actual negative cases that are incorrectly identified as positive by the model. No unit (a ratio or percentage, typically expressed as a percentage).
• False Negative Rate — 1 years
  False Negative Rate (FNR) measures the proportion of actual positive cases that are incorrectly identified as negative by the model. No unit (a ratio or percentage, typically expressed as a percentage).
• Postoperative Complication Rate — 1 years
  Percentage (%) of patients experiencing postoperative complications.
• Recurrence Risk Rate — 1 years
  Percentage (%) of patients experiencing recurrence during the follow-up period.
• Survival Rate — 1 years
  Percentage (%) of patients alive, calculated using Kaplan-Meier survival curves.
• Effectiveness of Decision Support — 1 years
  Percentage (%) improvement in the accuracy of treatment decisions with AI assistance compared to traditional decisions.
• Decision Time Efficiency — 1 years
  Average time (seconds) required for physicians to make diagnostic and treatment decisions, before and after AI assistance.

Trial sites (5)

More The Eye Hospital of Wenzhou Medical University trials in China

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