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Fairness and generalizability of OCT normative databases: a comparative analysis.

Luis Filipe Nakayama1,2, Lucas Zago Ribeiro3, Juliana Angelica Estevão de Oliveira3

  • 1Laboratory of Computational Physiology, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, United States of America. luisnaka@mit.edu.

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Normative databases in optical coherence tomography (OCT) machines show limited diversity and static frameworks. This necessitates caution in interpreting OCT normality and calls for more representative datasets for machine learning in healthcare.

Keywords:
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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Machine Learning

Background:

  • Optical coherence tomography (OCT) relies on normative databases for normality assessment.
  • These databases provide a color-coded scale by comparing patient measurements to a reference population.
  • Supervised machine learning algorithms in healthcare heavily depend on accurate and representative data labels and reports.

Purpose of the Study:

  • To evaluate and compare normative databases from different OCT machines (Cirrus, Avanti, Spectralis, Triton).
  • To analyze patient demographics, inclusion/exclusion criteria, diversity, and statistical methods used in these databases.
  • To assess the fairness and generalizability of existing OCT normative databases.

Main Methods:

  • Data were extracted from FDA approvals and equipment manuals of Cirrus, Avanti, Spectralis, and Triton OCT devices.
  • Key variables compared included database size, participant demographics (sex, race, ethnicity, age), inclusion/exclusion criteria, participant country, and diversity indices.
  • Statistical approaches used in database construction were also analyzed.

Main Results:

  • Avanti OCT possesses the largest normative database (640 eyes).
  • Inclusion/exclusion criteria were consistent across databases, generally including adults and excluding pathological cases.
  • Spectralis showed the highest proportion of White participants (79.7%), Cirrus of Asian (24%), and Triton of Black (22%). Avanti had the highest overall racial diversity.
  • All databases employed regression models for statistical analysis, and sex diversity was comparable across datasets.

Conclusions:

  • Existing OCT normative databases are static, with limited update capabilities and lack support for new modules.
  • Interpretation of OCT normality requires caution due to the static nature and demographic limitations of current databases.
  • Development of more diverse, representative, and open-access datasets is crucial, particularly for advancing supervised machine learning in ophthalmology.