Comparison of the refractive prediction errors of artificial intelligence formula with 3 conventional formulas and 11 combination methods in cataract surgery on eyes with short axial length.

PURPOSE: To compare the refractive prediction error of Hill-radial basis function 3.0 with those of 3 conventional formulas and 11 combination methods in eyes with short axial lengths. METHODS: The refractive prediction error was calculated using 4 formulas (Hoffer Q, SRK-T, Haigis, and Hill-RBF) and 11 combination methods (average of two or more methods). The absolute error was determined, and the proportion of eyes within 0.25-diopter (D) increments of absolute error was analyzed. Furthermore, the intraclass correlation coefficients of each method were computed to evaluate the agreement between target refractive error and postoperative spherical equivalent. RESULTS: This study included 87 eyes. Based on the refractive prediction error findings, Hoffer Q formula exhibited the highest myopic errors, followed by SRK-T, Hill-RBF, and Haigis. Among all the methods, the Haigis and Hill-RBF combination yielded a mean refractive prediction error closest to zero. The SRK-T and Hill-RBF combination showed the lowest mean absolute error, whereas the Hoffer Q, SRK-T, and Haigis combination had the lowest median absolute error. Hill-radial basis function exhibited the highest intraclass correlation coefficient, whereas SRK-T showed the lowest. Haigis and Hill-RBF, as well as the combination of both, demonstrated the lowest proportion of refractive surprises (absolute error >1.00 D). Among the individual formulas, Hill-RBF had the highest success rate (absolute error ≤0.50 D). Moreover, among all the methods, the SRK-T and Hill-RBF combination exhibited the highest success rate. CONCLUSIONS: Hill-radial basis function showed accuracy comparable to or surpassing that of conventional formulas in eyes with short axial lengths. The use and integration of various formulas in cataract surgery for eyes with short axial lengths may help reduce the incidence of refractive surprises.

Effect of Corneal and Lens Eccentricity Difference on Rigid Corneal Lens Treatment in Patients With Keratoconus.

OBJECTIVE: To evaluate the effects of rigid corneal lenses (RCL) in patients with keratoconus, based on eccentricity. METHODS: Eighty-four eyes from 84 patients were included in this retrospective comparative study. Based on the median value of eccentricity difference between the cornea and back surface of the RCL, the patients were divided into groups 1 (<0.08) and 2 (≥0.08). Visual acuity, refractive index, and corneal topography indices were compared before and three months after lens use. RESULTS: Visual acuity, astigmatism, refractive error, corneal curvature, and corneal thickness improved significantly in both groups. Apical power and anterior elevation improved significantly in group 1, with small differences in eccentricity, but not in group 2. Changes in apical power before and after lens use were significantly different between the two groups. CONCLUSION: The RCL was effective for the cornea of keratoconus, especially when the prescription was made with a small difference in eccentricity.

Compressive optic neuropathy due to posterior ethmoid mucocele.

Mucoceles are cystic formations characterized by the presence of mucus-secreting epithelial cells, which enlarge when the excretory duct becomes obstructed. Posterior ethmoidal mucoceles are rare conditions that can lead to severe ocular complications requiring immediate intervention. The close anatomical proximity of posterior ethmoidal mucoceles to the optic nerve underscores their significance. In this case report, we present a case of rapidly progressing compressive optic neuropathy secondary to a posterior ethmoidal mucocele. A previously healthy forty-six-year-old woman presented with sudden visual loss in her left eye, preceded by left-sided headache and periorbital pain. Clinical examination and imaging studies revealed an oval-shaped mass within the posterior ethmoid cell compressing the left optic nerve. Emergency surgery was performed to alleviate optic nerve compression, which successfully relieved periocular pain. However, the patient's visual acuity and visual field defect remained unchanged postoperatively. Thinning of the ganglion cell layer in the macula region was observed during follow-up examinations. The role of corticosteroids and antibiotics in visual rehabilitation and the impact of delayed surgical decompression on visual outcome remain subjects of debate. Additional cases of mucocele-associated optic neuropathy should be published and analyzed to establish optimal treatment approaches.

Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease.

Dry eye disease (DED) is one of the most common diseases worldwide that can lead to a significant impairment of quality of life. The diagnosis and treatment of the disease are often challenging because of the lack of correlation between the signs and symptoms, limited reliability of diagnostic tests, and absence of established consensus on the diagnostic criteria. The advancement of machine learning, particularly deep learning technology, has enabled the application of artificial intelligence (AI) in various anterior segment disorders, including DED. Currently, many studies have reported promising results of AI-based algorithms for the accurate diagnosis of DED and precise and reliable assessment of data obtained by imaging devices for DED. Thus, the integration of AI into clinical approaches for DED can enhance diagnostic and therapeutic performance. In this review, in addition to a brief summary of the application of AI in anterior segment diseases, we will provide an overview of studies regarding the application of AI in DED and discuss the recent advances in the integration of AI into the clinical approach for DED.