Background The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated... Show moreBackground The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help address these challenges. We aimed to determine if VA could be estimated using confocal scanning laser ophthalmoscopy (cSLO) imaging and deep learning (DL). Methods Snellen corrected VA and cSLO imaging were obtained retrospectively. The Johns Hopkins University (JHU) dataset was used for 10-fold cross-validations and internal testing. The Amsterdam University Medical Centers (AUMC) dataset was used for external independent testing. Both datasets had the same exclusion criteria: visually significant media opacities and images not centred on the central macula. The JHU dataset included patients with RP with and without molecular confirmation. The AUMC dataset only included molecularly confirmed patients with RP. Using transfer learning, three versions of the ResNet-152 neural network were trained: infrared (IR), optical coherence tomography (OCT) and combined image (CI). Results In internal testing (JHU dataset, 2569 images, 462 eyes, 231 patients), the area under the curve (AUC) for the binary classification task of distinguishing between Snellen VA 20/40 or better and worse than Snellen VA 20/40 was 0.83, 0.87 and 0.85 for IR, OCT and CI, respectively. In external testing (AUMC dataset, 349 images, 166 eyes, 83 patients), the AUC was 0.78, 0.87 and 0.85 for IR, OCT and CI, respectively. Conclusions Our algorithm showed robust performance in predicting visual impairment in patients with RP, thus providing proof-of-concept for predicting structure-function correlation based solely on cSLO imaging in patients with RP. Show less
Murenu, E.; Kostidis, S.; Lahiri, S.; Geserich, A.S.; Imhof, A.; Giera, M.; Michalakis, S. 2021
Photoreceptors are the light-sensing cells of the retina and the major cell type affected in most inherited retinal degenerations. Different metabolic pathways sustain their high energetic demand... Show morePhotoreceptors are the light-sensing cells of the retina and the major cell type affected in most inherited retinal degenerations. Different metabolic pathways sustain their high energetic demand in physiological conditions, particularly aerobic glycolysis. The principal metabolome of the mature retina has been studied, but only limited information is available on metabolic adaptations in response to key developmental events, such as eye opening. Moreover, dynamic metabolic changes due to retinal degeneration are not well understood. Here, we aimed to explore and map the ocular metabolic dynamics induced by eye opening in healthy (wild type) or Pde6b-mutant (retinal degeneration 1, Rd1) mice, in which photoreceptors degenerate shortly after eye opening. To unravel metabolic differences emerging before and after eye opening under physiological and pathophysiological conditions, we performed nuclear magnetic resonance (NMR) spectrosco-py-based metabolome analysis of wild type and Rd1 retina and vitreous/lens. We show that eye opening is accompanied by changes in the concentration of selected metabolites in the retina and by alterations in the vitreous/lens composition only in the retinal degeneration context. As such, we identify N-Acetylaspartate as a potential novel vitreous/lens marker reflecting progressive retinal degeneration. Thus, our data can help elucidating mechanisms underlying key events in retinal physiology and reveal changes occurring in pathology, while highlighting the importance of the vitreous/lens in the characterization of retinal diseases. Show less
