Artificial intelligence in ophthalmology

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Ophthalmic exam and ophthalmology residents' subspecialty preference

AIM

To evaluate residents' perceptions and attitudes toward the various aspects of a comprehensive ophthalmic examination, and to determine if these factors correlate with their choices for fellowship and future career plans.

METHODS

This is a cross-sectional study. A questionnaire on the perception of the ophthalmic exam was sent via SurveyMonkey to ophthalmology residents throughout Israel. Eighty of them (one-half of all ophthalmology residents in 2019) completed it. The first part related to how they perceived each component of the ophthalmic evaluation. The second part related to their future fellowship plans. The Chi-squared test was used to compare categorical variables and Student's t-test and One-way ANOVA were used to compare continuous variables. Multivariate logistic regression analysis was applied as needed to detect interactions between variables and to exclude confounder effects.

RESULTS

Slit-lamp examination of the anterior and posterior segments ranked as the most likeable aspects, whereas gonioscopy, ocular motility examination and visual acuity assessment were least likeable. Anterior segment and retina subspecialties were the ones most sought after. Forty-nine respondents (61.3%) noted a direct correlation between their preferred parts of comprehensive ophthalmic evaluations and their choice of subspecialty. Perceptions of selected components of those evaluations were significantly associated with responders' intentions to pursue anterior segment, retina, or oculoplastic fellowships (P<0.05).

CONCLUSION

Perspectives of residents toward the ophthalmic examination, and most specifically its individual components, play an important role in their choice of subspecialty training.