Purpose. To determine whether subfoveal choroidal blood flow is altered in retinitis pigmentosa (RP) and whether this alteration is associated with central cone–mediated dysfunction. Methods. In 31 RP patients (age range, 15–72 years) with preserved visual acuity (range: 20/30–20/20), subfoveal choroidal blood flow was measured by real-time, confocal laser Doppler flowmetry, and focal macular (18°) electroretinograms (FERGs) were elicited by 41 Hz flickering stimuli. Twenty normal subjects served as controls. The following average blood flow parameters were determined based on three 60-second recordings: volume (ChBVol), velocity (ChBVel), and flow (ChBF), the last being proportional to blood flow if the hematocrit remains constant. The amplitude and phase of the FERG first harmonic component were measured. Results. On average, ChBF and ChBVel were reduced by 26% (P ≤ 0.02) in RP patients compared to controls, whereas ChBVol was similar in the two groups. FERG amplitudes were reduced by 60% (P < 0.01) in patients compared with controls. FERG phases of patients tended to be delayed (P < 0.08) compared with their values in the controls. In patients, FERG phase delays were correlated (r = 0.50, P < 0.01) with ChBF and ChBVel values. FERG amplitudes were correlated (r = 0.49, P < 0.01) with ChBVol values. Conclusions. These data indicate significant alterations of subfoveal choroidal hemodynamic in RP and suggest a link between the alteration of ChBF and the RP-associated central cone–mediated dysfunction as assessed by the FERG.

Bardet–Biedl syndrome (BBS) is a rare inherited multisystemic disorder with autosomal recessive or complex digenic triallelic inheritance. There is currently no treatment for BBS, but some morbidities can be managed. Accurate molecular diagnosis is often crucial for the definition of appropriate patient management and for the development of a potential personalized therapy. We developed a next-generation-sequencing (NGS) protocol for the screening of the 18 most frequently mutated genes to define the genotype and clarify the mutation spectrum of a cohort of 20 BBS Italian patients. We defined the causative variants in 60% of patients; four of those are novel. 33% of patients also harboured variants in additional gene/s, suggesting possible oligogenic inheritance. To explore the function of different genes, we looked for correlations between genotype and phenotype in our cohort. Hypogonadism was more frequently detected in patients with variants in BBSome proteins, while renal abnormalities in patients with variations in BBSome chaperonin genes. NGS is a powerful tool that can help understanding BBS patients’ phenotype through the identification of mutations that could explain differences in phenotype severity and could provide insights for the development of targeted therapy. Furthermore, our results support the existence of additional BBS loci yet to be identified.

Background: An increasing amount of people are globally affected by retinal diseases, such as diabetes, vascular occlusions, maculopathy, alterations of systemic circulation, and metabolic syndrome. Aim: This review will discuss novel technologies in and potential approaches to the detection and diagnosis of retinal diseases with the support of cutting-edge machines and artificial intelligence (AI). Methods: The demand for retinal diagnostic imaging exams has increased, but the number of eye physicians or technicians is too little to meet the request. Thus, algorithms based on AI have been used, representing valid support for early detection and helping doctors to give diagnoses and make differential diagnosis. AI helps patients living far from hub centers to have tests and quick initial diagnosis, allowing them not to waste time in movements and waiting time for medical reply. Results: Highly automated systems for screening, early diagnosis, grading and tailored therapy will facilitate the care of people, even in remote lands or countries. Conclusion: A potential massive and extensive use of AI might optimize the automated detection of tiny retinal alterations, allowing eye doctors to perform their best clinical assistance and to set the best options for the treatment of retinal diseases.

Background: Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions. Aim: This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions. Methods: A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included. Results: The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care. Conclusions: Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.