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Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.

Zahra Heidari1,2, Masoud Mirghorbani3, Mahdi Abounoori4

  • 1Department of Ophthalmology, Bu-Ali Sina Hospital, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran. zheidar1@lakeheadu.ca.

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Summary

Artificial intelligence (AI) shows high accuracy in detecting vitreoretinal diseases (VRDs) from retinal images. AI tools, especially convolutional neural networks (CNNs), offer promising diagnostic performance for early intervention.

Keywords:
Artificial intelligenceDeep learningMachine learningRetinaVitreoretinal disease

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Early detection of vitreoretinal diseases (VRDs) is crucial for preventing vision loss.
  • Current detection relies on manual interpretation of multimodal imaging.
  • Artificial intelligence (AI) offers a promising approach for early VRD detection and intervention.

Purpose of the Study:

  • To evaluate and summarize the diagnostic performance of AI models in detecting VRDs using retinal imaging.
  • To assess the accuracy, sensitivity, and specificity of AI in VRD detection across various subgroups.

Main Methods:

  • A systematic meta-analysis was conducted, registering the study in PROSPERO (CRD42023450207).
  • A comprehensive literature search was performed across PubMed/MEDLINE, EMBASE, and Web of Science up to August 2023.
  • Study validity was assessed using the QUADAS-2 tool, and eligible articles were categorized into nine VRD subgroups for meta-analysis.

Main Results:

  • 195 studies were included, yielding an overall pooled estimate of accuracy (PEA) of 95.76%.
  • Pooled sensitivity (PESen) was 91.94% and pooled specificity (PESpe) was 96.09%.
  • AI models, particularly convolutional neural networks (CNNs), demonstrated high PEA (>90%) across subgroups.

Conclusions:

  • AI diagnostic tools, especially CNNs, exhibit robust performance in detecting VRDs.
  • Caution is advised when applying results from studies with limited generalizability to real-world settings.
  • Further research into emerging AI models like large language models (LLMs) for VRD detection is recommended.