Showing papers by "Luciano Quaranta published in 2022"

RPE65-Associated Retinopathies in the Italian Population: A Longitudinal Natural History Study

Abstract: Purpose To investigate the course of inherited retinal degenerations (IRD) due to mutations in the RPE65 gene. Methods This longitudinal multicentric retrospective chart-review study was designed to collect best corrected visual acuity (BCVA), Goldman visual field, optical coherence tomography (OCT), and electroretinography (ERG) measurements. The data, including imaging, were collected using an electronic clinical research form and were reviewed at a single center to improve consistency. Results From an overall cohort of 60 Italian patients with RPE65-associated IRD, 43 patients (mean age, 27.8 ± 19.7 years) were included and showed a mean BCVA of 2.0 ± 1.0 logMAR. Time-to-event analysis revealed a median age of 33.8 years and 41.4 years to reach low vision and blindness based on BCVA, respectively. ERG (available for 34 patients) showed undetectable responses in most patients (26; 76.5%). OCT (available for 31 patients) revealed epiretinal membranes in five patients (16.1%). Central foveal thickness significantly decreased with age at a mean annual rate of −0.6%/y (P = 0.044). We identified 43 different variants in the RPE65 gene in the entire cohort. Nine variants were novel. Finally, to assess genotype-phenotype correlations, patients were stratified according to the number of RPE65 loss-of-function (LoF) alleles. Patients without LoF variants showed significantly (P < 0.05) better BCVA compared to patients with one or two LoF alleles. Conclusions We described the natural course of RPE65-associated IRD in an Italian cohort showing for the first time a specific genotype-phenotype association. Our findings can contribute to a better management of RPE65-associated IRD patients.

Assessment of a Vision-Based Technique for an Automatic Van Herick Measurement System

Abstract: The adoption of artificial intelligence (AI) methods within the instrumentation and measurements field is nowadays an attractive research area. On the one hand, making machines learn from data how to perform an activity, rather than hard code sequential instructions, is a convenient and effective solution in many modern research areas. On the other hand, AI allows for the compensation of inaccurate or not complete models of specific phenomena or systems. In this context, this article investigates the possibility to exploit suitable machine learning (ML) techniques in a vision-based ophthalmic instrument to perform automatic anterior chamber angle (ACA) measurements. In particular, two convolutional neural network (CNN)-based networks have been identified to automatically classify acquired images and select the ones suitable for the Van Herick procedure. Extensive clinical trials have been conducted by clinicians, from which a realistic and heterogeneous image dataset has been collected. The measurement accuracy of the proposed instrument is derived by extracting measures from the images of the aforementioned dataset, as well as the system performances have been assessed with respect to differences in patients’ eye color. Currently, the ACA measurement procedure is performed manually by appropriately trained medical personnel. For this reason, ML and vision-based techniques may greatly improve both test objectiveness and diagnostic accessibility, by enabling an automatic measurement procedure.

Training of an artificial intelligence algorithm for automatic detection of the Van Herick grade

Abstract: Van Herick technique is a qualitative tool for assessing the anterior chamber angle and can be exploited as a simple screening alternative to gonioscopy. In our previous papers, we presented a novel instrument able to automatically perform the Van Herick manoeuvre. Therefore, to fully automate the screening method from the acquired images, it is still necessary to automatically determine the Van Herick grade. In this paper, we present a deep learning algorithm for automatically determining the Van Herick grade. In particular, the performances of three different Convolutional Neural Networks have been verified by acquiring the eye images of 80 patients. All the networks return the Van Herick grade classification with sufficient accuracy for a screening system and, after proper training, can offer a real-time response.

Physiology-Enhanced Data Analytics to Evaluate the Effect of Altitude on Intraocular Pressure and Ocular Hemodynamics

Abstract: Altitude affects intraocular pressure (IOP); however, the underlying mechanisms involved and its relationship with ocular hemodynamics remain unknown. Herein, a validated mathematical modeling approach was used for a physiology-enhanced (pe-) analysis of the Mont Blanc study (MBS), estimating the effects of altitude on IOP, blood pressure (BP), and retinal hemodynamics. In the MBS, IOP and BP were measured in 33 healthy volunteers at 77 and 3466 m above sea level. Pe-retinal hemodynamics analysis predicted a statistically significant increase (p < 0.001) in the model predicted blood flow and pressure within the retinal vasculature following increases in systemic BP with altitude measured in the MBS. Decreased IOP with altitude led to a non-monotonic behavior of the model predicted retinal vascular resistances, with significant decreases in the resistance of the central retinal artery (p < 0.001) and retinal venules (p = 0.003) and a non-significant increase in the resistance in the central retinal vein (p = 0.253). Pe-aqueous humor analysis showed that a decrease in osmotic pressure difference (OPD) may underlie the difference in IOP measured at different altitudes in the MBS. Our analysis suggests that venules bear the significant portion of the IOP pressure load within the ocular vasculature, and that OPD plays an important role in regulating IOP with changes in altitude.