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Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence.
Invest Ophthalmol Vis Sci 2019; 60(7):2551-2562IO

Abstract

Purpose

To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates to CEP290- and NPHP5-associated Leber congenital amaurosis (LCA) to determine the potential for functional improvement.

Methods

Patients with RP (n = 20) and LCA due to CEP290 (n = 12) or NPHP5 (n = 6) mutations were studied. A patient with CEP290 mutations but mild retinal degeneration was included. RP patients had cone-mediated macular function. A machine learning technique was used to associate perimetric sensitivities to local structure in RP patients. Models trained on RP data were applied to predict visual function in LCA.

Results

The RP and LCA patients had comparable retinal structure. RP patients had peak sensitivity at the fovea surrounded by decreasing sensitivity. Machine learning could successfully predict perimetry results from segmented or unsegmented optical coherence tomography (OCT) input. Application of machine learning predictions to LCA within the residual macular island of photoreceptor structure showed differences between predicted and measured sensitivities defining treatment potential. In patients with retained vision, the treatment potential was 4.6 ± 2.9 dB at the fovea but 16.4 ± 4.4 dB at the parafovea. In patients with limited or no vision, the treatment potential was 17.6 ± 9.4 dB.

Conclusions

Cone vision improvement potential in LCA due to CEP290 or NPHP5 mutations is predictable from retinal structure using a machine learning approach. This should allow individual prediction of the maximal efficacy in clinical trials and guide decisions about dosing. Similar strategies can be used in other retinal degenerations to estimate the extent and location of treatment potential.

Authors+Show Affiliations

Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.Scheie Eye Institute, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31212307

Citation

Sumaroka, Alexander, et al. "Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence." Investigative Ophthalmology & Visual Science, vol. 60, no. 7, 2019, pp. 2551-2562.
Sumaroka A, Garafalo AV, Semenov EP, et al. Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence. Invest Ophthalmol Vis Sci. 2019;60(7):2551-2562.
Sumaroka, A., Garafalo, A. V., Semenov, E. P., Sheplock, R., Krishnan, A. K., Roman, A. J., ... Cideciyan, A. V. (2019). Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence. Investigative Ophthalmology & Visual Science, 60(7), pp. 2551-2562. doi:10.1167/iovs.19-27156.
Sumaroka A, et al. Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence. Invest Ophthalmol Vis Sci. 2019 Jun 3;60(7):2551-2562. PubMed PMID: 31212307.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Treatment Potential for Macular Cone Vision in Leber Congenital Amaurosis Due to CEP290 or NPHP5 Mutations: Predictions From Artificial Intelligence. AU - Sumaroka,Alexander, AU - Garafalo,Alexandra V, AU - Semenov,Evelyn P, AU - Sheplock,Rebecca, AU - Krishnan,Arun K, AU - Roman,Alejandro J, AU - Jacobson,Samuel G, AU - Cideciyan,Artur V, PY - 2019/6/19/entrez PY - 2019/6/19/pubmed PY - 2019/6/19/medline SP - 2551 EP - 2562 JF - Investigative ophthalmology & visual science JO - Invest. Ophthalmol. Vis. Sci. VL - 60 IS - 7 N2 - Purpose: To use supervised machine learning to predict visual function from retinal structure in retinitis pigmentosa (RP) and apply these estimates to CEP290- and NPHP5-associated Leber congenital amaurosis (LCA) to determine the potential for functional improvement. Methods: Patients with RP (n = 20) and LCA due to CEP290 (n = 12) or NPHP5 (n = 6) mutations were studied. A patient with CEP290 mutations but mild retinal degeneration was included. RP patients had cone-mediated macular function. A machine learning technique was used to associate perimetric sensitivities to local structure in RP patients. Models trained on RP data were applied to predict visual function in LCA. Results: The RP and LCA patients had comparable retinal structure. RP patients had peak sensitivity at the fovea surrounded by decreasing sensitivity. Machine learning could successfully predict perimetry results from segmented or unsegmented optical coherence tomography (OCT) input. Application of machine learning predictions to LCA within the residual macular island of photoreceptor structure showed differences between predicted and measured sensitivities defining treatment potential. In patients with retained vision, the treatment potential was 4.6 ± 2.9 dB at the fovea but 16.4 ± 4.4 dB at the parafovea. In patients with limited or no vision, the treatment potential was 17.6 ± 9.4 dB. Conclusions: Cone vision improvement potential in LCA due to CEP290 or NPHP5 mutations is predictable from retinal structure using a machine learning approach. This should allow individual prediction of the maximal efficacy in clinical trials and guide decisions about dosing. Similar strategies can be used in other retinal degenerations to estimate the extent and location of treatment potential. SN - 1552-5783 UR - https://www.unboundmedicine.com/medline/citation/31212307/Treatment_Potential_for_Macular_Cone_Vision_in_Leber_Congenital_Amaurosis_Due_to_CEP290_or_NPHP5_Mutations:_Predictions_From_Artificial_Intelligence L2 - http://iovs.arvojournals.org/article.aspx?doi=10.1167/iovs.19-27156 DB - PRIME DP - Unbound Medicine ER -