June 2020
Volume 61, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2020
Optimising Treatment of Neovascular Age-related Macular Degeneration using Reinforcement Learning
Author Affiliations & Notes
  • Gongyu Zhang
    Moorfields Eye Hospital, London, United Kingdom
    Department of Computer Science, UCL, United Kingdom
  • Zeyu Guan
    Moorfields Eye Hospital, London, United Kingdom
  • Edward Korot
    Moorfields Eye Hospital, London, United Kingdom
  • Reena Chopra
    Moorfields Eye Hospital, London, United Kingdom
  • Gabriella Moraes
    Moorfields Eye Hospital, London, United Kingdom
  • Siegfried Wagner
    Moorfields Eye Hospital, London, United Kingdom
  • Livia Faes
    Moorfields Eye Hospital, London, United Kingdom
  • Dun Jack Fu
    Moorfields Eye Hospital, London, United Kingdom
  • Hagar Khalid
    Moorfields Eye Hospital, London, United Kingdom
  • daniel Araujo ferraz
    Moorfields Eye Hospital, London, United Kingdom
  • Konstantinos Balaskas
    Moorfields Eye Hospital, London, United Kingdom
  • Daniel Alexander
    Department of Computer Science, UCL, United Kingdom
  • Pearse Andrew Keane
    Moorfields Eye Hospital, London, United Kingdom
    UCL Institute of Ophthalmology, United Kingdom
  • Footnotes
    Commercial Relationships   Gongyu Zhang, None; Zeyu Guan, None; Edward Korot, None; Reena Chopra, DeepMind (E); Gabriella Moraes, None; Siegfried Wagner, None; Livia Faes, None; Dun Jack Fu, None; Hagar Khalid, None; daniel ferraz, None; Konstantinos Balaskas, Heidelberg (R), TopCon (R); Daniel Alexander, None; Pearse Keane, Allergan (R), Bayer (R), Carl Zeiss Meditec (R), DeepMind (C), Haag-Streit (R), Heidelberg Engineering (R), Novartis (R), Topcon (R)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 1628. doi:
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      Gongyu Zhang, Zeyu Guan, Edward Korot, Reena Chopra, Gabriella Moraes, Siegfried Wagner, Livia Faes, Dun Jack Fu, Hagar Khalid, daniel Araujo ferraz, Konstantinos Balaskas, Daniel Alexander, Pearse Andrew Keane; Optimising Treatment of Neovascular Age-related Macular Degeneration using Reinforcement Learning. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1628.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Develop a reinforcement learning algorithm to optimise treatment decisions of neovascular
AMD. The algorithm provides two-dimensional treatment suggestions including whether the patient should be injected and when to schedule the next follow up visit.

Methods : We used a previously developed deep learning OCT segmentation model as input for our model. Segmented tissues included as input were subretinal fluid (SRF), intraretinal fluid (IRF). The model was optimized to reach a goal of zero volume of IRF and SRF for three consecutive visits. Patient admissions were modelled by Markov decision processes. Action space, problem state, reward function was developed in this framework. Q function of temporal difference learning was updated using iteration and sampling from the historical patient data. The output is whether the patient should be injected and when to schedule the next followup visit.

Results :

The mean, [median] number of treatment visits for the AI policy to obtain 3 consecutive visits with 0 IRF and SRF was 16, [13] , which was less than clinicians’ 25, [22]. Therefore, AI policy can help patients reduce visit time.
Based on historical data, when deciding not to inject, the majority of clinicians in our dataset scheduled a followup visit between 40 to 45 days. For comparison, when deciding not to inject, the majority of the time, our reinforcement learning AI model scheduled a followup visit between 90 to 95 days.
When the decision was made to inject, the clinician and AI model followup times were 45 to 50 days, and70 to 75 days respectively.

Conclusions : A data-driven approach using reinforcement learning was developed to optimize the treatment of neovascular age-related macular degeneration. The algorithm demonstrates the generation of treatment courses for AMD patients to reduce total IRF and SRF with less visits and longer inter-visit intervals than clinicians from historical data.

This is a 2020 ARVO Annual Meeting abstract.

 

 

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