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Deep Learning-Based Precision Cropping of Eye Regions in Strabismus Photographs: Algorithm Development and Validation

Dawen Wu1, Yanfei Li2, Zeyi Yang1

  • 1Department of Ophthalmology, West China Hospital, Sichuan University, 37 Guoxue Xiang (Alley), Chengdu, Sichuan Province, 610041, China, 86 18980601759.

Journal of Medical Internet Research
|July 17, 2025
PubMed
Summary
This summary is machine-generated.

An automated eye-gaze preprocessing algorithm significantly improves accuracy and efficiency in image cropping and head tilt correction for artificial intelligence (AI) development and clinical ophthalmology. This AI-driven platform enhances workflow, supports telemedicine, and aids strabismus care.

Keywords:
AI management systemartificial intelligenceclinical workflowimage preprocessingocular alignment

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Area of Science:

  • Ophthalmology and Computer Vision
  • Artificial Intelligence in Healthcare
  • Medical Image Analysis

Background:

  • Traditional manual preprocessing of ocular gaze photographs is inefficient and inconsistent, hindering AI model development and clinical applications.
  • Manual cropping and head tilt correction are time-consuming, introducing variability in datasets.

Purpose of the Study:

  • To develop and validate an advanced preprocessing algorithm for automated, accurate, and standardized eye region cropping.
  • To enhance efficiency and consistency in clinical workflows and AI data preprocessing for ocular gaze analysis.

Main Methods:

  • Utilized a retrospective and prospective dataset of 5832 images from 648 patients.
  • Developed a rotating bounding box detection framework for eye region cropping and head tilt correction.
  • Validated the algorithm using precision, recall, mAP, cross-validation, expert review, and downstream task performance comparison (strabismus screening).

Main Results:

  • The algorithm achieved high performance across multiple datasets, with precision and recall of 1.000 and high mAP scores.
  • Automated cropping reduced image preparation time from 10 hours (manual) to 30 seconds.
  • The model with head tilt correction significantly improved strabismus screening AUC (0.917) compared to methods without correction.

Conclusions:

  • An AI-driven platform with an automated preprocessing algorithm offers improved eye region cropping and head tilt correction for AI and clinical use.
  • The system enhances workflow efficiency, supports telemedicine privacy, and advances ophthalmological research and strabismus care.