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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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  1. Home
  3. An Artificial Intelligence Method For Phenotyping Of Oct-derived Thickness Maps Using Unsupervised And Self-supervised Deep Learning.
  1. Home
  3. An Artificial Intelligence Method For Phenotyping Of Oct-derived Thickness Maps Using Unsupervised And Self-supervised Deep Learning.

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An Artificial Intelligence Method for Phenotyping of OCT-Derived Thickness Maps Using Unsupervised and

Saber Kazeminasab1, Sayuri Sekimitsu2, Mojtaba Fazli3

  • 1Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Harvard University, Boston, MA, USA. skazeminasabhashemabad@meei.harvard.edu.

Journal of Imaging Informatics in Medicine
|May 20, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Artificial intelligence (AI) advances ophthalmic disease research by analyzing optical coherence tomography (OCT) images. A novel AI method successfully transfers OCT phenotypes across datasets, identifying clinically meaningful retinal layer clusters for glaucoma research.

Keywords:
ClusteringCorrelation analysisDeep unsupervised learningDimensionality reductionImage phenotyping for OCT imagesSelf-supervised learning in feature extraction

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Understanding ophthalmic disease physiology and genetics is crucial.
  • Optical coherence tomography (OCT) provides detailed retinal imaging.
  • Developing methods for cross-dataset analysis of OCT data is challenging.

Purpose of the Study:

  • To enhance understanding of ophthalmic disease physiology and genetic architecture.
  • To introduce a novel AI methodology for transferring OCT phenotypes across datasets.
  • To phenotype and cluster OCT-derived retinal layer thicknesses using glaucoma as a model.

Main Methods:

  • Employed unsupervised and self-supervised learning techniques.
  • Integrated deep learning, manifold learning, and Gaussian mixture models.
  • Applied the method to two large datasets: Massachusetts Eye and Ear (MEE) and UK Biobank (UKBB).

    • Main Results:

    • Identified 9 to 11 clinically meaningful phenotypic clusters per retinal layer, consistent across datasets.
    • Demonstrated strong intra-cluster similarity and significant correlations with glaucoma severity markers (visual field mean deviation, cup-to-disc ratio).
    • Validated the model's robustness and generalizability across diverse datasets.

    Conclusions:

    • The developed AI methodology enables robust OCT-based phenotyping and phenotype transfer.
    • This approach facilitates translational research in ophthalmic disease mechanisms and genetic discovery.
    • The findings pave the way for improved understanding and diagnosis of eye diseases.