Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning
Full-field electroretinogram (ERG) and best corrected visual acuity (BCVA) measures have been shown to have prognostic value for recessive Stargardt disease (also called “<i>ABCA4</i>-related retinopathy”). These functional tests may serve as a performance-outcome-measure (PerfO) in emer...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/9/8/2428 |
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| author | Philipp L. Müller Tim Treis Alexandru Odainic Maximilian Pfau Philipp Herrmann Adnan Tufail Frank G. Holz |
| author_facet | Philipp L. Müller Tim Treis Alexandru Odainic Maximilian Pfau Philipp Herrmann Adnan Tufail Frank G. Holz |
| author_sort | Philipp L. Müller |
| collection | DOAJ |
| description | Full-field electroretinogram (ERG) and best corrected visual acuity (BCVA) measures have been shown to have prognostic value for recessive Stargardt disease (also called “<i>ABCA4</i>-related retinopathy”). These functional tests may serve as a performance-outcome-measure (PerfO) in emerging interventional clinical trials, but utility is limited by variability and patient burden. To address these limitations, an ensemble machine-learning-based approach was evaluated to differentiate patients from controls, and predict disease categories depending on ERG (‘inferred ERG’) and visual impairment (‘inferred visual impairment’) as well as BCVA values (‘inferred BCVA’) based on microstructural imaging (utilizing spectral-domain optical coherence tomography) and patient data. The accuracy for ‘inferred ERG’ and ‘inferred visual impairment’ was up to 99.53 ± 1.02%. Prediction of BCVA values (‘inferred BCVA’) achieved a precision of ±0.3LogMAR in up to 85.31% of eyes. Analysis of the permutation importance revealed that foveal status was the most important feature for BCVA prediction, while the thickness of outer nuclear layer and photoreceptor inner and outer segments as well as age of onset highly ranked for all predictions. ‘Inferred ERG’, ‘inferred visual impairment’, and ‘inferred BCVA’, herein, represent accurate estimates of differential functional effects of retinal microstructure, and offer quasi-functional parameters with the potential for a refined patient assessment, and investigation of potential future treatment effects or disease progression. |
| first_indexed | 2024-03-10T18:06:59Z |
| format | Article |
| id | doaj.art-71f5908ed0bc49158df99d5c0e9bee21 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T18:06:59Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-71f5908ed0bc49158df99d5c0e9bee212023-11-20T08:25:19ZengMDPI AGJournal of Clinical Medicine2077-03832020-07-0198242810.3390/jcm9082428Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine LearningPhilipp L. Müller0Tim Treis1Alexandru Odainic2Maximilian Pfau3Philipp Herrmann4Adnan Tufail5Frank G. Holz6Department of Ophthalmology, University of Bonn, 53127 Bonn, GermanyBioQuant, University of Heidelberg, 69120 Heidelberg, GermanyDepartment of Ophthalmology, University of Bonn, 53127 Bonn, GermanyDepartment of Ophthalmology, University of Bonn, 53127 Bonn, GermanyDepartment of Ophthalmology, University of Bonn, 53127 Bonn, GermanyMoorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UKDepartment of Ophthalmology, University of Bonn, 53127 Bonn, GermanyFull-field electroretinogram (ERG) and best corrected visual acuity (BCVA) measures have been shown to have prognostic value for recessive Stargardt disease (also called “<i>ABCA4</i>-related retinopathy”). These functional tests may serve as a performance-outcome-measure (PerfO) in emerging interventional clinical trials, but utility is limited by variability and patient burden. To address these limitations, an ensemble machine-learning-based approach was evaluated to differentiate patients from controls, and predict disease categories depending on ERG (‘inferred ERG’) and visual impairment (‘inferred visual impairment’) as well as BCVA values (‘inferred BCVA’) based on microstructural imaging (utilizing spectral-domain optical coherence tomography) and patient data. The accuracy for ‘inferred ERG’ and ‘inferred visual impairment’ was up to 99.53 ± 1.02%. Prediction of BCVA values (‘inferred BCVA’) achieved a precision of ±0.3LogMAR in up to 85.31% of eyes. Analysis of the permutation importance revealed that foveal status was the most important feature for BCVA prediction, while the thickness of outer nuclear layer and photoreceptor inner and outer segments as well as age of onset highly ranked for all predictions. ‘Inferred ERG’, ‘inferred visual impairment’, and ‘inferred BCVA’, herein, represent accurate estimates of differential functional effects of retinal microstructure, and offer quasi-functional parameters with the potential for a refined patient assessment, and investigation of potential future treatment effects or disease progression.https://www.mdpi.com/2077-0383/9/8/2428retinaStargardt diseaseoptical coherence tomographyvisual acuityelectroretinogramhereditary retinal disease |
| spellingShingle | Philipp L. Müller Tim Treis Alexandru Odainic Maximilian Pfau Philipp Herrmann Adnan Tufail Frank G. Holz Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning Journal of Clinical Medicine retina Stargardt disease optical coherence tomography visual acuity electroretinogram hereditary retinal disease |
| title | Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning |
| title_full | Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning |
| title_fullStr | Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning |
| title_full_unstemmed | Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning |
| title_short | Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning |
| title_sort | prediction of function in abca4 related retinopathy using ensemble machine learning |
| topic | retina Stargardt disease optical coherence tomography visual acuity electroretinogram hereditary retinal disease |
| url | https://www.mdpi.com/2077-0383/9/8/2428 |
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