Prediction of Function in ABCA4-Related Retinopathy Using Ensemble Machine Learning

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.

Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications

“…These prospective studies would allow to further evaluate the predictive value, which might give more insights into the pathophysiology of AMD and allow for effective study design as presented before for different parameters in AMD or other retinopathies. 42 , 44 , 59 , 61 , 70 , 71 …”

“…These prospective studies would allow to further evaluate the predictive value, which might give more insights into the pathophysiology of AMD and allow for effective study design as presented before for different parameters in AMD or other retinopathies. 42,44,59,61,70,71 A further limitation of this study is the application of OCT imaging devices by a single manufacturer. Different OCT imaging devices might provide different scanning artifacts or image quality.…”

Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

“…OCT is a non-invasive imaging technology that utilizes low-coherence laser light to produce cross-sectional images in biological tissues 8 – 10 . There have been successful reports with regard to implementation of ML-based analysis of OCT data and its diagnostic accuracy for neovascular age-related macular degeneration 11 – 13 , diabetic retinopathy 14 – 18 , or retinal vein occlusion 19 – 21 , among others 22 . Machine learning increased OCT information throughput and showed similar performance as human graders in annotation of complex OCT images 23 – 26 .…”

Inferred retinal sensitivity in recessive Stargardt disease using machine learning

“…In the last decade, machine learning techniques have entered visual science, including analysis in the context of retinal imaging 27 . It has recently been shown to offer great potentials in the detection and classification of pathological features 28 , and in the prediction of retinal function 22,29,30 . Based on these developments, this study investigated the possibility of machine learning algorithms to predict spatially-resolved retinal function in STGD1 based on (1) OCT imaging data, (2) indicators of retest variability, (3) functional along with patients' demographic measures, and (4) brief FCP testing for the first time.…”