June 2020
Volume 61, Issue 7
ARVO Annual Meeting Abstract  |   June 2020
Automated quantification of CNV volume using deep learning AI algorithm
Author Affiliations & Notes
  • Maria Vähätupa
    Experimentica Ltd., Kuopio, Finland
  • Symantas Ragauskas
    Experimentica UAB, Vilnius, Lithuania
  • Agne Ziniauskaite
    Experimentica UAB, Vilnius, Lithuania
  • Anna Knuuttila
    Aiforia Technologies Ltd., Helsinki, Finland
  • Hanna-Kaisa Sihvo
    Aiforia Technologies Ltd., Helsinki, Finland
  • Mikael Jääskeläinen
    Aiforia Technologies Ltd., Helsinki, Finland
  • Simon Kaja
    Experimentica Ltd., Kuopio, Finland
    Department of Ophthalmology, Loyola University Chicago, Illinois, United States
  • Giedrius Kalesnykas
    Experimentica Ltd., Kuopio, Finland
  • Marc Cerrada-Gimenez
    Experimentica Ltd., Kuopio, Finland
  • Footnotes
    Commercial Relationships   Maria Vähätupa, Experimentica Ltd. (E); Symantas Ragauskas, Experimentica Ltd. (E), Experimentica Ltd. (I); Agne Ziniauskaite, Experimentica UAB (E); Anna Knuuttila, 3Aiforia Technologies Ltd (E); Hanna-Kaisa Sihvo, 3Aiforia Technologies Ltd (E); Mikael Jääskeläinen, 3Aiforia Technologies Ltd (E); Simon Kaja, Experimentica Ltd (E), Experimentica Ltd (I), Experimentica Ltd. (F), Experimentica Ltd. (P), Experimentica Ltd. (R), Experimentica Ltd. (S), K&P Scientific LLC (P), K&P Scientific LLC (R), K&P Scientific LLC (C), K&P Scientific LLC (F), K&P Scientific LLC (I), K&P Scientific LLC (S); Giedrius Kalesnykas, Experimentica Ltd. (E), Experimentica Ltd. (I), Experimentica Ltd. (S), Experimentica Ltd. (P), Experimentica Ltd. (R), Spouse - Experimentica Ltd. (I), Spouse - Experimentica Ltd. (S); Marc Cerrada-Gimenez, Experimentica Ltd. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4194. doi:
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      Maria Vähätupa, Symantas Ragauskas, Agne Ziniauskaite, Anna Knuuttila, Hanna-Kaisa Sihvo, Mikael Jääskeläinen, Simon Kaja, Giedrius Kalesnykas, Marc Cerrada-Gimenez; Automated quantification of CNV volume using deep learning AI algorithm. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4194.

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

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Purpose : An artificial intelligence (AI) algorithm was applied in combination with spectral-domain optical coherence tomography (SD-OCT) to quantify the volume of choroidal neovascularization (CNV) lesions in the mouse CNV model.

Methods : Choroidal neovascularization and vascular leakage were induced using a 532 nm laser in the mouse. The development of CNV lesions were monitored in vivo by fluorescent angiography (FA) and SD-OCT at days 5, 10 and 14 post-induction. The efficacy of systemically administered aflibercept (Eylea®, Bayer AG, Germany) at doses of 5, 15 or 25 mg/kg was evaluated.
The analysis of the CNV leakage area was performed by manually measuring the leakage area from FA images. This analysis was benchmarked against a convolutional neural network (CNN) trained to recognize and quantify the CNV lesion using Aiforia® Cloud (Aiforia Technologies Ltd, Finland) using semantic segmentation and supervised learning. After follow-up period choroidal flat mounts were stained by an endothelial marker, isolectin GS-IB4.

Results : A significant dose-dependent decrease in the CNV lesion development was seen in aflibercept-treated animals as compared to controls. The 25 mg/kg dose significantly decreased CNV leakage area by 42% (p = 0.046) at day 5. Both 5 mg/kg and 15 mg/kg aflibercept-treated mice did not show statistically significant decrease in CNV leak as compared to control mice. The AI-based methodology for lesion volume analysis (CNN model) at Day 14 showed that groups treated with aflibercept at a dose of 15 mg/kg or 25 mg/kg decrease CNV volume by an average of 53%. Moreover, a decrease in isolectin-positive areas was found in the choroidal flat mounts stainings from the 25 mg/kg and 15 mg/kg dose groups as compared to saline treated mice.

Conclusions : The automated lesion volume analysis and the area of vascular leakage measured with FA showed similar results indicating that systemically administered aflibercept presents a dose-dependent reduction on CNV lesion development. The use of AI-driven CNV volume evaluation represent an unbiased and standardized methodology for CNV pathology quantification.

This is a 2020 ARVO Annual Meeting abstract.


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