[ARTIFICIAL INTELLIGENCE IN OPHTHALMOLOGY].

INTRODUCTION: Artificial intelligence (AI) was first introduced in 1956, and effectively represents the fourth industrial revolution in human history. Over time, this medium has evolved to be the preferred method of medical imagery interpretation. Today, the implementation of AI in the medical field as a whole, and the ophthalmological field in particular, is diverse and includes diagnose, follow-up and monitoring of the progression of ocular diseases. For example, AI algorithms can identify ectasia, and pre-clinical signs of keratoconus, using images and information computed from various corneal maps. Machine learning (ML) is a specific technique for implementing AI. It is defined as a series of automated methods that identify patterns and templates in data and leverage these to perform predictions on new data. This technology was first applied in the 1980s. Deep learning is an advanced form of ML inspired by and designed to imitate the human brain process, constructed of layers, each responsible for identifying patterns, thereby successfully modeling complex scenarios. The significant advantage of ML in medicine is in its' ability to monitor and follow patients with efficiency at a low cost. Deep learning is utilized to monitor ocular diseases such as diabetic retinopathy, age-related macular degeneration, glaucoma, cataract, and retinopathy of prematurity. These conditions, as well as others, require frequent follow-up in order to track changes over time. Though computer technology is important for identifying and grading various ocular diseases, it still necessitates additional clinical validation and does not entirely replace human diagnostic skill.

Ophthalmology practice during the COVID-19 pandemic.

OBJECTIVE: To present an established practice protocol for safe and effective hospital-setting ophthalmic practice during the coronavirus disease 2019 (COVID-19) pandemic. METHODS AND ANALYSIS: Literature was reviewed to identify articles relevant to COVID-19 pandemic and ophthalmology. The following keywords were used: COVID-19, SARS-CoV-2 and telemedicine, combined with eye, ophthalmology, conjunctivitis and tears. Data were extracted from the identified manuscripts and discussed among subspecialists to obtain consensus evidence-based practice. RESULTS: A protocol for ophthalmic practice in the era of COVID-19 pandemic was established. The protocol covered patient screening, clinic flow, required personal protective equipment and modifications of ophthalmic equipment for improved safety. CONCLUSION: Important literature emerged with respect to the practice of ophthalmology in the era of COVID-19. An evidence-based ophthalmic practice protocol was established and should be modified in the future to accommodate new insights on the COVID-19 pandemic.

Enhanced depth imaging in swept-source optical coherence tomography: Improving visibility of choroid and sclera, a masked study.

PURPOSE: To compare enhanced depth imaging in swept-source optical coherence tomography and non-enhanced depth imaging optical coherence tomography in their ability to capture choroidal and scleral details. METHODS: Averaged foveal B-Scans were obtained from 40 eyes of 20 healthy volunteers by swept-source optical coherence tomography with and without enhanced depth imaging. Visibility and contrast of vascular details within the choroid, choroidoscleral junction, and sclera were evaluated by masked readers using an ordinal scoring scale. Outcomes were analyzed using the Wilcoxon signed rank-sum test. RESULTS: Visibility of the choroidal vascular details (Z = 5.94, p < .001), the choroidoscleral junction (Z = 5.85, p < .001), and the sclera (Z = 6.80, p < .001) was significantly higher with enhanced depth imaging than with non-enhanced depth imaging swept-source optical coherence tomography. Similarly, image contrast was significantly higher with enhanced depth imaging than with non-enhanced depth imaging swept-source optical coherence tomography for the choroidal vascular details (Z = 9.47, p < .001), for the choroidoscleral junction (Z = 9.28, p < .001), and for the sclera (Z = 9.42, p < .001). CONCLUSION: Enhanced depth imaging applied to swept-source optical coherence tomography-averaged foveal B-scans enhances visualization of the choroidal details, of the choroidoscleral junction, and of the sclera. This novel modality can easily be implemented in clinics and could improve our understanding of conditions involving the choroid or the sclera.

Vitreous hemorrhage as an early sign of acute bacterial endophthalmitis following intravitreal ranibizumab injection.

PURPOSE: To report a case of acute bacterial endophthalmitis after antivascular endothelial growth factor injection with a rare presentation of vitreous hemorrhage. METHODS: An 84-year-old woman presented with sudden painless vision loss in her left eye, 3 days after intravitreal ranibizumab injection for cystoid macular edema due to neovascular age-related macular degeneration. The patient was otherwise asymptomatic. Dense vitreous hemorrhage was observed. At follow-up the next day, the patient complained on severe left eye pain. After examination, acute endophthalmitis was diagnosed. RESULTS: Intravitreal injection of vancomycin, ceftazidime and dexamethasone was performed. Vitreous and aqueous cultures grew Enterococcus faecalis. After treatment, the inflammation subsided but it took 3 months for the vitreous hemorrhage to totally resorb. Visual acuity was reduced to light perception. CONCLUSIONS: Vitreous hemorrhage may be an atypical presentation of acute bacterial endophthalmitis occurring after intravitreal injection.