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Development and validation of a computer-aided diagnostic tool to screen for age-related macular degeneration by optical coherence tomography
  1. P Serrano-Aguilar1,2,
  2. R Abreu3,
  3. L Antón-Canalís4,
  4. C Guerra-Artal4,
  5. Y Ramallo-Fariña2,5,
  6. F Gómez-Ulla6,
  7. J Nadal7
  1. 1Evaluation and Planning Unit, Canary Islands Health Service, Canary Islands, Spain
  2. 2CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
  3. 3Department of Ophthalmology, University Hospital of La Candelaria, Canary Islands, Spain
  4. 4Artificial Intelligence and Systems Group (GIAS), University of Las Palmas de Gran Canaria, Canary Islands, Spain
  5. 5Canary Islands Foundation for Health and Research (FUNCIS), Canary Islands, Spain
  6. 6Technological Institute of Ophthalmology (ITO), University of Santiago de Compostela, Santiago de Compostela, Spain
  7. 7Barraquer Ophthalmology Centre, Barcelona, Spain
  1. Correspondence to Dr Pedro Serrano-Aguilar, C/ Pérez de Rozas, n° 5, 4ª planta, CP: 38004, Santa Cruz de Tenerife, Islas Canarias, Spain; pserrano{at}gobiernodecanarias.org

Abstract

Background To develop and assess the technical validity of new computer-aided diagnostic software (CAD) for automated analyses of optical coherence tomography (OCT) images for the purpose of screening for neovascular age-related macular degeneration.

Methods Artificial visual techniques were used to develop the CAD in two steps: normalisation and feature vector extraction from OCT images; and training and classification by means of decision trees. Technical validation was performed by a retrospective study design based on OCT images randomly extracted from clinical charts. Images were classified as normal or abnormal to serve for screening purposes. Sensitivity, specificity, positive predictive values and negative predictive values were obtained.

Results The CAD was able to quantify image information by working in the perceptually uniform hue–saturation–value colour space. Particle swarm optimisation with Haar-like features is suitable to reveal structural features in normal and abnormal OCT images. Decision trees were useful to characterise normal and abnormal images using feature vectors obtained from descriptive statistics of detected structures. The sensitivity of the CAD was 96% and the specificity 92%.

Conclusions This new CAD for automated analysis of OCT images offers adequate sensitivity and specificity to distinguish normal OCT images from those showing potential neovascular age-related macular degeneration. These results will enable its clinical validation and a subsequent cost-effectiveness assessment to be made before recommendations are made for population-screening purposes.

  • Age-related macular degeneration
  • optical coherence tomography
  • computer-aided diagnostic
  • choroid
  • medical education
  • telemedicine
  • epidemiology
  • imaging
  • diagnostic tests/investigation
  • treatment lasers
  • treatment surgery
  • treatment medical
  • retina
  • macula
  • degeneration

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Footnotes

  • Funding This research study was financed by the Health Institute Carlos III (Fondo de Investigaciones Sanitarias File No PS09/01308).

  • Competing interests None to declare.

  • Patient consent Obtained.

  • Ethics approval Ethics Committee of the University Hospital of Nuestra Señora de la Candelaria (HUNSC) of Tenerife, Canary Islands (Spain). The study was carried out in accordance with the Declaration of Helsinki.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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