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Updated: Jul 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Ocular images-based artificial intelligence on systemic diseases.

Yuhe Tan1, Xufang Sun2

  • 1Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People's Republic of China.

Biomedical Engineering Online
|May 19, 2023
PubMed
Summary

Artificial intelligence (AI) using eye images shows promise for diagnosing systemic diseases. However, research is early-stage, with unclear disease mechanisms and limitations like small datasets and ethical concerns.

Keywords:
Artificial intelligenceDeep learningFundus photographsOcular imagesSystemic diseases

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Systemic Diseases

Background:

  • Ocular imaging offers a non-invasive window into systemic health.
  • Artificial intelligence (AI) is increasingly explored for medical diagnostics.

Purpose of the Study:

  • To summarize research advancements in using AI with ocular images for systemic disease detection.
  • To highlight current applications and limitations of this emerging field.

Main Methods:

  • A narrative literature review was conducted.
  • Key studies on AI in ocular diagnostics for systemic conditions were analyzed.

Main Results:

  • AI applied to ocular images shows potential for diagnosing endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases.
  • Current research is nascent, primarily focusing on diagnosis, with limited understanding of underlying disease mechanisms.
  • Significant limitations include small datasets, AI interpretability challenges, rare disease data scarcity, and ethical/legal considerations.

Conclusions:

  • Ocular image-based AI is a developing tool for systemic disease assessment.
  • Further research is needed to clarify the eye-body connection and overcome current study limitations.