Skip to main content

Advertisement

Log in

OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications

  • Retinal Disorders
  • Published:
Graefe's Archive for Clinical and Experimental Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

Intravitreal injections with anti-vascular endothelial growth factor (anti-VEGF) medications have become the standard of care for their respective indications. Optical coherence tomography (OCT) scans of the central retina provide detailed anatomical data and are widely used by clinicians in the decision-making process of anti-VEGF indication. In recent years, significant progress has been made in artificial intelligence and computer vision research. We trained a deep convolutional artificial neural network to predict treatment indication based on central retinal OCT scans without human intervention.

Method

A total of 183,402 retinal OCT B-scans acquired between 2008 and 2016 were exported from the institutional image archive of a university hospital. OCT images were cross-referenced with the electronic institutional intravitreal injection records. OCT images with a following intravitreal injection during the first 21 days after image acquisition were assigned into the ‘injection’ group, while the same amount of random OCT images without intravitreal injections was labeled as ‘no injection’. After image preprocessing, OCT images were split in a 9:1 ratio to training and test datasets. We trained a GoogLeNet inception deep convolutional neural network and assessed its performance on the validation dataset. We calculated prediction accuracy, sensitivity, specificity, and receiver operating characteristics.

Results

The deep convolutional neural network was successfully trained on the extracted clinical data. The trained neural network classifier reached a prediction accuracy of 95.5% on the images in the validation dataset. For single retinal B-scans in the validation dataset, a sensitivity of 90.1% and a specificity of 96.2% were achieved. The area under the receiver operating characteristic curve was 0.968 on a per B-scan image basis, and 0.988 by averaging over six B-scans per examination on the validation dataset.

Conclusion

Deep artificial neural networks show impressive performance on classification of retinal OCT scans. After training on historical clinical data, machine learning methods can offer the clinician support in the decision-making process. Care should be taken not to mistake neural network output as treatment recommendation and to ensure a final thorough evaluation by the treating physician.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Parikh R, Ross JS, Sangaralingham LR et al (2017) Trends of anti-vascular endothelial growth factor use in ophthalmology among privately insured and Medicare advantage patients. Ophthalmology 124:352–358. https://doi.org/10.1016/j.ophtha.2016.10.036

    Article  PubMed  Google Scholar 

  2. Keane PA, Patel PJ, Liakopoulos S et al (2012) Evaluation of age-related macular degeneration with optical coherence tomography. Surv Ophthalmol 57:389–414. https://doi.org/10.1016/j.survophthal.2012.01.006

    Article  PubMed  Google Scholar 

  3. Huang D, Swanson EA, Lin CP et al (1991) Optical coherence tomography. Science 254:1178–1181

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Drexler W, Fujimoto JG (2008) State-of-the-art retinal optical coherence tomography. Prog Retin Eye Res 27:45–88. https://doi.org/10.1016/j.preteyeres.2007.07.005

    Article  PubMed  Google Scholar 

  5. Patel PJ, Browning AC, Chen FK et al (2009) Interobserver agreement for the detection of optical coherence tomography features of neovascular age-related macular degeneration. Invest Ophthalmol Vis Sci 50:5405–5410. https://doi.org/10.1167/iovs.09-3505

    Article  PubMed  Google Scholar 

  6. Framme C, Panagakis G, Walter A et al (2012) Interobserver variability for retreatment indications after ranibizumab loading doses in neovascular age-related macular degeneration. Acta Ophthalmol 90:49–55. https://doi.org/10.1111/j.1755-3768.2010.01940.x

    Article  CAS  PubMed  Google Scholar 

  7. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539

    Article  CAS  PubMed  Google Scholar 

  8. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. http://arxiv.org/abs/1409.1556v6 . Accessed 12 Feb 2017

  9. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in Neural Information Processing Systems 25 (NIPS 2012). MIT Press, Cambridge MA, pp 1097–1105

    Google Scholar 

  10. Szegedy C, Liu W, Jia Y, et al. (2014) Going deeper with convolutions. http://arxiv.org/abs/1409.4842v1 . Accessed 12 Feb 2017

  11. Abràmoff MD, Lou Y, Erginay A et al (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57:5200–5206. https://doi.org/10.1167/iovs.16-19964

    Article  PubMed  Google Scholar 

  12. Asaoka R, Murata H, Iwase A, Araie M (2016) Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier. Ophthalmology 123:1974–1980. https://doi.org/10.1016/j.ophtha.2016.05.029

    Article  PubMed  Google Scholar 

  13. Lee CS, Baughman DM, Lee AY (2016) Deep learning is effective for the classification of OCT images of normal versus age-related macular degeneration. http://arxiv.org/abs/1612.04891v1 . Accessed 12 Feb 2017

  14. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167v3 . Accessed 12 Feb 2017

  15. Heng LZ, Pefkianaki M, Pefianaki M et al (2015) Interobserver agreement in detecting spectral-domain optical coherence tomography features of diabetic macular edema. PLoS One 10:e0126557. https://doi.org/10.1371/journal.pone.0126557

    Article  PubMed  PubMed Central  Google Scholar 

  16. Engelbert M, Zweifel SA, Freund KB (2009) ‘Treat and extend’ dosing of intravitreal antivascular endothelial growth factor therapy for type 3 neovascularization/retinal angiomatous proliferation. Retina 29:1424–1431. https://doi.org/10.1097/IAE.0b013e3181bfbd46

    Article  PubMed  Google Scholar 

  17. Nguyen A, Yosinski J, Clune J (2014) Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. http://arxiv.org/abs/1412.1897v4 . Accessed 12 Mar 2017

Download references

Funding

No funding was received for this research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Philipp Prahs.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge, or beliefs) in the subject matter or materials discussed in this manuscript.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. For this type of study formal consent is not required.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Prahs, P., Radeck, V., Mayer, C. et al. OCT-based deep learning algorithm for the evaluation of treatment indication with anti-vascular endothelial growth factor medications. Graefes Arch Clin Exp Ophthalmol 256, 91–98 (2018). https://doi.org/10.1007/s00417-017-3839-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00417-017-3839-y

Keywords

Navigation