Prediction of function in ABCA4-related retinopathy using ensemble machine learning

• Verfasst von: Müller, Philipp Lothar [VerfasserIn]
• Verfasst von: Treis, Tim [VerfasserIn]
• Verfasst von: Odainic, Alexandru [VerfasserIn]
• Verfasst von: Pfau, Maximilian [VerfasserIn]
• Verfasst von: Herrmann, Philipp [VerfasserIn]
• Verfasst von: Tufail, Adnan [VerfasserIn]
• Verfasst von: Holz, Frank G. [VerfasserIn]
• Titel: Prediction of function in ABCA4-related retinopathy using ensemble machine learning
• Verf.angabe: Philipp L. Müller, Tim Treis, Alexandru Odainic, Maximilian Pfau, Philipp Herrmann, Adnan Tufail and Frank G. Holz
• E-Jahr: 2020
• Jahr: 29 July 2020
• Umfang: 15 S.
• Fussnoten: Gesehen am 22.10.2020
• Titel Quelle: Enthalten in: Journal of Clinical Medicine
• Ort Quelle: Basel : MDPI, 2012
• Jahr Quelle: 2020
• Band/Heft Quelle: 9(2020,8) Artikel-Nummer 2428, 15 Seiten
• ISSN Quelle: 2077-0383
• DOI: doi:10.3390/jcm9082428
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: artificial intelligence
• Sach-SW: electroretinogram
• Sach-SW: hereditary retinal disease
• Sach-SW: optical coherence tomography
• Sach-SW: retina
• Sach-SW: Stargardt disease
• Sach-SW: visual acuity
• K10plus-PPN: 1736236628

Abstract

Full-field electroretinogram (ERG) and best corrected visual acuity (BCVA) measures have been shown to have prognostic value for recessive Stargardt disease (also called “ABCA4-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.