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Purpose: To report the clinical course and the retinal imaging features of a case of cytology-proven primary vitreoretinal lymphoma (PVRL) presenting with a transient bacillary layer detachment (BALAD) during the disease course. Methods: Observational case report. Results: A 50 year-old woman was referred to us with a 2-month history of vitritis in both eyes, poorly responding to oral prednisolone. After discontinuation of oral prednisolone, worsening of vitritis and the appearance of multiple creamy-like subretinal infiltrates in the mid-peripheral retina of both eyes, along with the exclusion of common causes of intermediate/posterior uveitis, made us consider PVRL. Aqueous humor sampling detected MYD88 L265P mutation, and subsequent diagnostic pars plana vitrectomy in the left eye yielded a positive cytology for large B cell lymphoma consistent with PVRL. During the disease course, optical coherence tomography of the macula showed a BALAD in the right eye, which resolved during follow-up. Conclusion: Our case indicates that BALAD is a possible rare manifestation of PVRL, and this should be considered in the differential diagnosis process in order to avoid diagnostic delays. [ABSTRACT FROM AUTHOR]

Purpose: Corneal neurotization (CN) is a novel, potentially curative surgical procedure for the treatment of neurothophic keratopathy (NK). Patients with severe NK can present with corneal opacification requiring optical keratoplasty, which would likely fail without a proper trophic support of corneal nerves in the recipient cornea. Methods: This is a pilot study on 4 patients undergoing keratoplasty after CN. Pre- and postoperative data at 12, 24 months and at the last follow-up were collected for the examination of (i) best corrected visual acuity (BCVA), (ii) slit lamp examination and photograph acquisition with and without fluorescein staining, (iii) corneal aesthesiometry, (iv) in vivo confocal microscopy of the central cornea. Neurophysiological study of the corneal reflex before corneal graft and at last follow up was performed. Results: Four female patients (47.25 ± 5.06 y.o.) underwent keratoplasty after CN (3 penetrating keratoplasty, 1 deep anterior lamellar keratoplasty). The mean interval between CN and keratoplasty was 22 (± 12) months. The mean graft survival time was 42 (± 25) months. Graft follow-up ranged from 72 to 132 months. At the final follow-up, BCVA was improved in 2 out of 4 patients. The mean corneal sensitivity was 11.9 ± 8.3 mm at last follow-up. In vivo confocal microscopy confirmed the presence of functioning nerves at the last follow-up in all patients. NK-related complications occurred in 3 eyes (2 persistent epithelial defect, 1 corneal melting). The former complication was successfully treated by autologous serum eye drops while the latter required repeated keratoplasty. Conclusions: Keratoplasty is a viable strategy to improve visual acuity in patients with corneal opacity who underwent CN for the treatment of NK. Even in the presence of functioning corneal nerves before keratoplasty, surgeons should be aware of the increased rate of NK-related complications that could require the need for repeated procedure. [ABSTRACT FROM AUTHOR]

Background/Objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare systems. Predicting injections needs at each monitoring session could optimize treatment outcomes and reduce unnecessary interventions. Methods: To achieve these aims, machine learning (ML) models were evaluated using different combinations of clinical variables, including retinal thickness and volume, best-corrected visual acuity, and features derived from macular optical coherence tomography (OCT). A "Leave Some Subjects Out" (LSSO) nested cross-validation approach ensured robust evaluation. Moreover, the SHapley Additive exPlanations (SHAP) analysis was employed to quantify the contribution of each feature to model predictions. Results: Results demonstrated that models incorporating both structural and functional features achieved high classification accuracy in predicting injection necessity (AUC = 0.747 ± 0.046, MCC = 0.541 ± 0.073). Moreover, the explainability analysis identified as key predictors both subretinal and intraretinal fluid, alongside central retinal thickness. Conclusions: These findings suggest that session-by-session prediction of injection needs in nAMD patients is feasible, even without processing the entire OCT image. The proposed ML framework has the potential to be integrated into routine clinical workflows, thereby optimizing nAMD therapeutic management. [ABSTRACT FROM AUTHOR]

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD. [ABSTRACT FROM AUTHOR]