Machine Learning Techniques in Clinical Vision Sciences.

Area of Science:

• Clinical vision science and biomedical informatics.

Background:

• Early detection of ocular diseases is crucial for preventing blindness.
• Advancements in diagnostic devices, imaging, and genomics provide new data for patient management.
• Machine learning (ML) offers decision-support to enhance disease detection and monitoring accuracy.

Purpose of the Study:

• To review the contribution of ML techniques in ocular disease diagnosis and monitoring.
• To discuss technical aspects and applications of ML in clinical vision science.

Main Methods:

• Review of ML approaches, including supervised and unsupervised learning.
• Discussion of data preprocessing steps to minimize bias and ensure model performance.
• Presentation of ML applications in specific ocular diseases.

Main Results:

• ML techniques can automatically recognize complex patterns in datasets for disease detection.
• Proper data handling is essential for reliable ML model performance.
• ML shows clinical benefits in diagnosing and monitoring glaucoma, age-related macular degeneration, and diabetic retinopathy.

Conclusions:

• ML is a valuable tool for improving the diagnosis and monitoring of ocular diseases.
• Objective clinical decision-making is enhanced by ML-driven insights.
• Further application of ML in vision science holds promise for preventing irreversible visual impairment.