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Purpose: This study aimed to evaluate the accuracy of 12 intraocular lens (IOL) power calculation formulae for eyes that have undergone both radial keratotomy (RK) and laser assisted in situ keratomileusis (LASIK) surgery to determine the efficacy of various IOL calculations for this unique patient group. Currently, research on this surgical topic is limited. Methods: In this retrospective study, 11 eyes from 7 individuals with a history of RK and LASIK who underwent cataract surgery at Hoopes Vision were analyzed. Preoperative biometric and corneal topographic measurements were performed. Subjective refraction was obtained postoperatively. Twelve different intraocular lens (IOL) power calculations were used: Barrett True K No History, Barrett True K (prior LASIK, Prior RK history), Barrett Universal 2, Camellin-Calossi-Camellin (3C), Double K-Modified Holladay, Haigis-L, Galilei, OCT, PEARL-DGS, Potvin-Hill, Panacea, and Shammas. Results: The rankings of mean arithmetic error (MAE), from least to greatest, were as follows: 3C (0.088), Haigis-L-L (-0.508), Shammas (-0.516), OCT Average (-0.538), Barrett True K (-0.557), OCT RK (-0.563), Galilei (-0.570), IOL Master (-0.571), OCT LASIK (-0.583), Barrett True K No History (-0.597), Pearl-DGS (-0.606), Potvin-Hill SF (-0.770), Potvin-Hill TNP (-0.778), Panacea (-0.876), and Barrett Universal 2 (-1.522). The 3C formula achieved the greatest percentage of eyes within ±0.25 D of target range (91%), while Haigis-L, Shammas, Galilei, Potvin Hill, Barrett True K, IOL Master, PEARL-DGS, and OCT formulae performed similarly, achieving 45% of eyes within ±0.75D of target refraction. Conclusion: This study demonstrates the accuracy of the lesser known 3C formula in IOL calculation, particularly for patients who have undergone both RK and LASIK. Well-known formulae, such as Haigis-L, Shammas, and Galilei, which are used by the American Society of Cataract and Refractive Surgery (ASCRS), are viable options, although 3C formulae should be considered in this patient population. Furthermore, larger studies can confirm the best IOL power formulas for post-RK and LASIK cataract patients.
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INTRODUCTION: This study aims to evaluate the accuracy of 12 different intraocular lens (IOL) power calculation formulas for post-radial keratotomy (RK) eyes. The investigation utilizes recent advances in topography/tomography devices and artificial intelligence (AI)-based calculators, comparing the results to those reported in current literature to assess the efficacy and predictability of IOL calculations for this patient group. METHODS: In this retrospective study, 37 eyes from 24 individuals with a history of RK who underwent cataract surgery at Hoopes Vision Center were analyzed. Biometry and corneal topography measurements were taken preoperatively. Subjective refraction was obtained 6 months postoperatively. Twelve different IOL power calculations were used, including the American Society of Cataract and Refractive Surgery (ASCRS) post-RK online formula, and the Barrett True K, Double K modified-Holladay 1, Haigis-L, Panacea, Camellin-Calossi, Emmetropia Verifying Optical (EVO) 2.0, Kane, and Prediction Enhanced by Artificial Intelligence and output Linearization-Debellemanière, Gatinel, and Saad (PEARL-DGS) formulas. Outcome measures included median absolute error (MedAE), mean absolute error (MAE), arithmetic mean error (AME), and percentage of eyes achieving refractive prediction errors (RPE) within ± 0.50 D, ± 0.75 D, and ± 1 D for each formula. A search of the literature was also performed by two independent reviewers based on relevant formulas. RESULTS: Overall, the best performing IOL power calculations were the Camellin-Calossi (MedAE = 0.515 D), the ASCRS average (MedAE = 0.535 D), and the EVO (MedAE = 0.545 D) and Kane (MedAE = 0.555 D) AI-based formulas. The EVO and Kane formulas along with the ASCRS calculation performed similarly, with 48.65% of eyes scoring within ± 0.50 D of the target range, while the Equivalent Keratometry Reading (EKR) 65 Holladay formula achieved the greatest percentage of eyes scoring within ± 0.25 D of the target range (35.14%). Additionally, the EVO 2.0 formula achieved 64.86% of eyes scoring within the ± 0.75 D RPE category, while the Kane formula achieved 75.68% of eyes scoring within the ± 1 D RPE category. There was no significant difference in MAE between the established and newer generation formulas (P > 0.05). The Panacea formula consistently underperformed when compared to the ASCRS average and other high-performing formulas (P < 0.05). CONCLUSION: This study demonstrates the potential of AI-based IOL calculation formulas, such as EVO 2.0 and Kane, for improving the accuracy of IOL power calculation in post-RK eyes undergoing cataract surgery. Established calculations, such as the ASCRS and Barrett True K formula, remain effective options, while under-utilized formulas, like the EKR65 and Camellin-Calossi formulas, show promise, emphasizing the need for further research and larger studies to validate and enhance IOL power calculation for this patient group.
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OBJECTIVE: We report a case of a 77-year-old male with metastatic melanoma who developed immune-checkpoint-inhibitor (ICI) induced type 1 diabetes mellitus (T1DM) after seven months of pembrolizumab treatment and required life-long insulin use. This prompted a literature review of best practice guidelines for long-term management of checkpoint-inhibitor induced T1DM including oral steroids as a treatment option similar to other ICI adverse effects. DATA SOURCES AND SUMMARY: A literature search on PubMed was conducted to evaluate the efficacy of steroid treatment ICI-induced T1DM in any cancer type. Search terms consisted of "ipilimumab" OR "nivolumab" OR & "pembrolizumab" OR "immune checkpoint" AND "diabetes" OR "type 1 diabetes" AND "cancer" OR "melanoma" OR "carcinoma OR "sarcoma". Inclusion criteria were case reports published after 2015 in which the patient was diagnosed with ICI-induced T1DM or diabetic ketoacidosis where oral steroids were part of the treatment. Exclusion criteria included oral steroids not used as a treatment modality for T1DM, multiple endocrine comorbidities, no response recorded, and previous history of T1DM. 284 abstracts were found with these search terms of which 33 full-text articles were concluded to be eligible and screened and from which 8 records were included. From these 8 articles, there were 12 cases included. CONCLUSION: This literature search suggests that ICI-induced T1DM cannot be reversed by steroids and that insulin must be used permanently for treatment management.
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Importance Chat Generative Pre-Trained Transformer (ChatGPT) has shown promising performance in various fields, including medicine, business, and law, but its accuracy in specialty-specific medical questions, particularly in ophthalmology, is still uncertain. Purpose This study evaluates the performance of two ChatGPT models (GPT-3.5 and GPT-4) and human professionals in answering ophthalmology questions from the StatPearls question bank, assessing their outcomes, and providing insights into the integration of artificial intelligence (AI) technology in ophthalmology. Methods ChatGPT's performance was evaluated using 467 ophthalmology questions from the StatPearls question bank. These questions were stratified into 11 subcategories, four difficulty levels, and three generalized anatomical categories. The answer accuracy of GPT-3.5, GPT-4, and human participants was assessed. Statistical analysis was conducted via the Kolmogorov-Smirnov test for normality, one-way analysis of variance (ANOVA) for the statistical significance of GPT-3 versus GPT-4 versus human performance, and repeated unpaired two-sample t-tests to compare the means of two groups. Results GPT-4 outperformed both GPT-3.5 and human professionals on ophthalmology StatPearls questions, except in the "Lens and Cataract" category. The performance differences were statistically significant overall, with GPT-4 achieving higher accuracy (73.2%) compared to GPT-3.5 (55.5%, p-value < 0.001) and humans (58.3%, p-value < 0.001). There were variations in performance across difficulty levels (rated one to four), but GPT-4 consistently performed better than both GPT-3.5 and humans on level-two, -three, and -four questions. On questions of level-four difficulty, human performance significantly exceeded that of GPT-3.5 (p = 0.008). Conclusion The study's findings demonstrate GPT-4's significant performance improvements over GPT-3.5 and human professionals on StatPearls ophthalmology questions. Our results highlight the potential of advanced conversational AI systems to be utilized as important tools in the education and practice of medicine.
