Background: Incomplete eyelid closure and lagophthalmos due to facial nerve palsy are significant functional and aesthetic concerns often requiring surgical correction. The aim of this systematic review is to quantitatively assess the efficacy, safety, and patient satisfaction associated with gold or platinum weight implantation, autologous fat grafting (lipofilling), and müllerectomy. Methods: A systematic review was performed following PRISMA guidelines, searching PubMed, Embase, Cochrane Library, Web of Science, and Scopus up to March 2025. Studies included clinical data on surgical correction for incomplete eyelid closure in facial palsy, reporting functional, anatomical, and satisfaction outcomes. Quality was assessed using the Newcastle–Ottawa Scale (NOS) and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Results: Twenty-six studies including a total of 1205 patients were included. Gold/platinum weight implantation achieved complete or near-complete eyelid closure in 83–92% of cases, with a reduction in lagophthalmos to <1 mm. Complication rates ranged from 5–15% (mainly extrusion/migration), and patient satisfaction averaged 7.9/10. Lipofilling showed persistent benefit in 77% of cases, with 9–20% requiring repeat procedures and 10–12% experiencing minor complications. Müllerectomy yielded symptomatic improvement or resolution in 92% of cases, with a mean lagophthalmos reduction of 1.18 mm. Conclusions: Gold or platinum weight implantation provides the most reliable improvement for severe upper eyelid dysfunction in facial palsy. Lipofilling is a viable autologous alternative, while müllerectomy is effective in selected cases. Further prospective comparative trials are needed to refine surgical selection and optimize outcomes.

Background/Objectives: Accurate intraocular lens (IOL) power calculation is vital for achieving the desired postoperative spherical equivalent (SE) in cataract surgery. Generative Artificial-Intelligence (AI) systems are increasingly being used in ophthalmology to refine diagnostic and surgical planning. However, it is still unknown whether a low-cost, easily accessible generative AI model like DeepSeek can match the accuracy of conventional biometric formulas. To evaluate the accuracy of DeepSeek, an open-source generative artificial intelligence (AI), in predicting postoperative refractive spherical equivalent compared to the Barrett Universal II formula in uncomplicated cataract surgeries. Methods: This study analyzed biometric data from 50 eyes of 50 patients who underwent cataract surgery between July 2024 and January 2025 at Humanitas Research Hospital in Milan, Italy. Only uncomplicated cases of emmetropia with Alcon AcrySof^® SA60WF IOL implantation were included. 30–40 days postoperative subjective refraction was measured with a calibrated trial-frame and 6 m logMAR chart by an experienced optometrist. Prediction error (PE), median absolute error (MedAE), standard deviation (SD), and cumulative frequency of PE diopters range were calculated. A Wilcoxon signed-rank test was performed to assess statistical significance. Results: Barrett showed MedAE 0.36 D [0.16–0.64] and MAE 0.43 D (95% CI, 0.34–0.52) while DeepSeek-R1 showed MedAE 0.76 D [0.52–1.01] and MAE 0.77 D (95% CI, 0.67–0.87). Cumulative accuracy (AE threshold) at ±0.25/±0.50/±0.75/±1.00/±1.25/±1.50/±1.75 D was 37.7/71.7/81.1/92.5/100.0/100.0/100.0% for Barrett Universal II and 11.1/25.9/50.0/74.1/88.9/96.3/100.0% for DeepSeek-R1 (McNemar p < 0.01 at each threshold). The paired comparison of per-eye absolute errors favored Barrett (Wilcoxon signed-rank test, p < 0.0001). Conclusions: In this cohort, Barrett Universal II outperformed DeepSeek-R1 across MedAE/MAE and cumulative accuracy thresholds, with a significant paired difference. A general-purpose generative model used off-the-shelf (fixed A-constant, no ophthalmology-specific tuning) did not match the accuracy of a validated vergence-based formula; established formulas remain the reference standard for clinical IOL power calculation.

Purpose: To identify and harmonize preoperative inclusion and exclusion criteria for corneal neurotization (CN) in neurotrophic keratopathy (NK), describe how consistently they are reported, and summarize practical, clinic-ready guidance for selection and follow-up. Methods: Systematic review (PRISMA-2020; PROSPERO CRD420251117787) of human studies on CN for NK (databases searched July 18, 2025). Study-level eligibility criteria, disease stage, esthesiometry, chronicity thresholds, donor-nerve requirements, and stated exclusions were evaluated; risk of bias (ROBINS-I) and certainty (GRADE) were recorded. Results: Thirty-six studies (380 patients) were included; of these, 26 (72.2%) explicitly reported eligibility criteria. Most common inclusion criteria were refractory NK (92.3%), severe/objective corneal anaesthesia or Mackie III severity stage (69.2%), NK with intact donor territory (50.0%), and corneal epithelial instability (46.2%). Exclusion criteria were often not stated (76.9%); when present, they most often listed active infection/non-NK disease (38.5%), systemic unsuitability (34.6%), absent/abnormal donor nerve(s) (15.4%), and severe limbal stem cell deficiency (15.4%). Across studies, CN improved corneal protection and surface stability with sensory recovery over ~6– 18 months and in vivo confocal microscopy evidence of nerve regrowth; visual acuity changes were variable. No randomized clinical trials were found; ROBINS-I was commonly moderate to serious, and overall certainty by GRADE was low. Conclusions: This systematic review defined and harmonized preoperative inclusion and exclusion criteria for CN in NK. A minimum preoperative dataset, including disease stage, mapped corneal sensitivity, denervation timeline and etiology, donor-dermatome integrity, feasibility of tension-free coaptation, and ocular surface status, may guide referral timing, donor-feasibility–driven technique selection, and structured follow-up.

BACKGROUND Intraocular lens (IOL) power calculation is crucial for achieving optimal refractive outcomes in cataract surgery. Several formulas have been developed to predict postoperative refraction, with varying degrees of accuracy. Recent advancements in artificial intelligence (AI) have led to the exploration of AI-driven tools as potential alternatives or adjuncts to traditional formulas. DeepSeek, a generative AI model, is one such tool that has been proposed for biometric calculations. However, its performance in predicting refractive outcomes compared to conventional methods remains unclear. This study aims to assess the accuracy of DeepSeek in predicting postoperative refraction and compare it with a widely used formula, the Barrett Universal II. OBJECTIVE To evaluate the accuracy of DeepSeek, an open-source generative artificial intelligence (AI), in predicting postoperative refractive outcomes for intraocular lens (IOL) power calculation compared to the Barrett Universal II formula. METHODS This study analyzed biometric data from 100 eyes of 50 patients who underwent cataract surgery between July 2024 and January 2025 at Humanitas Research Hospital in Milan, Italy. Only uncomplicated cases of emmetropia with Alcon AcrySof® SA60WF IOL implantation were included. Postoperative objective refraction was measured with Nidek TONOREF III. Predicted spherical equivalents were obtained using the Barrett Universal II formula and DeepSeek AI. Mean refractive error (MRE), standard deviation (SD), and cumulative frequency of target refractive error were calculated. A Wilcoxon signed-rank test was performed to assess statistical significance. RESULTS The MRE was significantly lower for Barrett (0.262 D) than for DeepSeek (0.659 D) (P < 0.0001). The SD was 0.386 D for Barrett and 0.424 D for DeepSeek. Barrett achieved refractive accuracy within ±0.25 D in 37% of cases, ±0.50 D in 75%, ±0.75 D in 85%, and ±1.0 D in 90%. DeepSeek performed worse at each threshold, reaching 10%, 30%, 55%, and 80%, respectively. CONCLUSIONS DeepSeek AI demonstrated significantly higher refractive errors and lower predictive accuracy than the Barrett Universal II formula. Although AI is a valuable tool already integrated into some biometric formulas, current widely accessible generative models remain inadequate for accurate biometric calculations, emphasizing the need for human-validated formulas to ensure optimal refractive outcomes in cataract surgery.