AJO International最新文献

{"title":"A comprehensive review of portable retinal cameras: Technical features, ai integration, and clinical potential","authors":"Luis Filipe Nakayama ,&nbsp;Lucas Zago Ribeiro ,&nbsp;Cindy Lie Tabuse ,&nbsp;Fernando Korn Malerbi ,&nbsp;Caio Regatieri","doi":"10.1016/j.ajoint.2025.100194","DOIUrl":"10.1016/j.ajoint.2025.100194","url":null,"abstract":"<div><div>Purpose: To evaluate and compare commercially available portable retinal cameras with a focus on technical specifications, clinical applications, and the integration of artificial intelligence (AI) for ophthalmic screening, especially in low- and middle-income countries (LMICs). Design: Systematic review of the literature. Methods: Systematic searches of PubMed and OpenAlex were conducted up to September 2025, without language restrictions, using terms such as portable retinal camera, handheld retinal camera, and smartphone-based fundus camera. Devices were included if they were commercially available and described in peer-reviewed publications with technical or clinical data. Prototypes and systems relying solely on external smartphone lenses without integrated optics were excluded. Data extracted included imaging specifications, ergonomics, power sources, AI functionalities, quality control features, and reported clinical applications. Devices were categorized as smartphone-attached or standalone handheld systems. Results: The search retrieved 870 records (PubMed = 277; OpenAlex = 593). After removing duplicates and screening, 509 articles were included in the review, collectively reporting on 38 portable retinal cameras, of which 17 were commercially available. The most frequently reported devices were the Volk Pictor Plus, Phelcom Eyer, Optomed Aurora, ZEISS VISUSCOUT 100, and Remidio FOP. Smartphone attached systems offered greater portability and affordability, whereas standalone handheld systems provided integrated functionality, higher imaging stability, and smoother clinical integration. AI features varied across devices, encompassing referable diabetic retinopathy detection, abnormality triage, systemic risk prediction, and automated image-quality assessment. Clinical applications extended beyond diabetic retinopathy and retinopathy of prematurity to include glaucoma, AMD, and exploratory use in systemic conditions such as sepsis and COVID-19. Conclusion: Portable retinal cameras already demonstrate clear utility in extending ophthalmic screening and diagnostic services, particularly for diabetic retinopathy and retinopathy of prematurity, while also showing potential in broader clinical and systemic applications. Their portability and cost-effectiveness make them valuable for outreach and telemedicine programs, especially in LMICs. The integration of artificial intelligence further enhances their functionality, though variability in device design, regional availability, and regulatory status highlights the need for standardized validation, recurring local assessments, and head-to-head comparative studies. Real-world evaluations remain essential to ensure effective, safe, and equitable deployment.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical characteristics and management of glaucoma following congenital cataract surgery and IOL implantation","authors":"Aïda Lachelah ,&nbsp;Thibaut Chapron ,&nbsp;Ismael Chehaibou ,&nbsp;Florence Metge ,&nbsp;Pascal Dureau ,&nbsp;Georges Caputo ,&nbsp;Youssef Abdelmassih","doi":"10.1016/j.ajoint.2025.100193","DOIUrl":"10.1016/j.ajoint.2025.100193","url":null,"abstract":"<div><h3>Purpose</h3><div>To present the clinical characteristics, management and outcomes of glaucoma following congenital cataract surgery with primary intraocular lens (IOL) implantation.</div></div><div><h3>Design</h3><div>Retrospective, monocentric consecutive case series</div></div><div><h3>Methods</h3><div>We included patients with glaucoma following congenital cataract surgery and IOL implantation before the age of one year. Therapeutic success was defined as intraocular pressure (IOP) &lt;21 mmHg at last follow-up without sight threatening complications, with or without medication.</div></div><div><h3>Results</h3><div>Sixty-one eyes of 42 patients were included. Mean age at cataract surgery was 121.4 ± 82.1 days. The mean interval between cataract surgery and glaucoma diagnosis was 3.9 ± 3.9 years. During follow-up, 34 eyes were treated medically (55 %) while 27 (45 %) needed surgery. The glaucoma surgeries included: trabeculectomy in 17 eyes, laser diode in 14 eyes, Ahmed glaucoma valve in 6 eyes and MicroShunt PRESERFLO in 2 eyes. Glaucoma was treated successfully in 48 eyes (80 %). Need for glaucoma surgery was associated with: early cataract surgery (<em>p</em> = 0.02), early glaucoma diagnosis (<em>p</em> = 0.04), use of trypan blue (<em>p</em> = 0.03) and associated anomalies (<em>p</em> = 0.01). BCVA at last follow-up was worse in eyes undergoing surgery than eyes receiving medical treatment (1.4 ± 1.0 vs. 0.63±0.57 logMAR, <em>p</em> = 0.001).</div></div><div><h3>Conclusion</h3><div>Eyes that underwent cataract surgery earlier and eyes associated with other anomalies were more likely to require surgery. The earlier glaucoma developed after cataract surgery, the more likely surgery was needed in the follow-up. Age at cataract surgery and time to glaucoma diagnosis did not affect surgical success. However, visual prognosis remains poor despite IOP control.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between diabetic retinopathy and neurodegenerative diseases in the All of Us research program","authors":"Ha-Neul Yu ,&nbsp;Jocelyn He ,&nbsp;Benjamin Kim ,&nbsp;Gui-Shuang Ying","doi":"10.1016/j.ajoint.2025.100190","DOIUrl":"10.1016/j.ajoint.2025.100190","url":null,"abstract":"<div><h3>Purpose</h3><div>While diabetic retinopathy (DR) has previously been linked to neurodegenerative diseases, it remains unclear whether DR independently reflects neurodegenerative diseases beyond those attributable to diabetes itself. In this study, we leveraged data from the All of Us Research Program to assess whether DR serves as an independent marker of neurodegenerative disease among individuals with diabetes.</div></div><div><h3>Methods</h3><div>A matched case-control, cross-sectional study was conducted using data from the All of Us Research Program (US-based EHR database). Three groups (exactly matched by age, sex, and race) were created and compared: individuals with both DR and DM (DR+DM, <em>n</em> = 7629), individuals with DM but no DR (DM-only, <em>n</em> = 22,887), and individuals without DM (<em>n</em> = 22,887). Outcomes included dementia, Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS). Univariate and multivariate logistic regression analyses were performed, adjusting for demographics, comorbidities and diabetes-related mediators.</div></div><div><h3>Results</h3><div>In multivariable analysis, DM-only was associated with increased odds of dementia (adjusted odds ratio [aOR] 1.28, 95 % CI: 1.08–1.51; <em>p</em> = 0.004). However, DR in the setting of DM (DM+DR vs. DM-only) was not associated with further increased odds of neurodegenerative disease outcome in multivariate models (aOR for dementia 1.18, 95 % CI: 0.94–1.49). No significant associations were identified for AD, PD, or MS (all <em>p</em> ≥ 0.10).</div></div><div><h3>Conclusion</h3><div>Diabetic retinopathy was not associated with increased rates of neurodegenerative diseases beyond that conferred by diabetes itself, and the relationship may be mediated by diabetes severity and related comorbidities.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100190"},"PeriodicalIF":0.0,"publicationDate":"2025-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epidemiology, risk factors, clinical outcomes, and prognostic factors of infectious keratitis: A tertiary center experience in Qatar","authors":"M. Basil Marchi ,&nbsp;Adeya Al Harami ,&nbsp;Ahmed Maher ,&nbsp;Omar Al-Qahtani ,&nbsp;Joenie Datingaling Anilao ,&nbsp;Javed Iqbal ,&nbsp;Hashem Abu Serhan","doi":"10.1016/j.ajoint.2025.100191","DOIUrl":"10.1016/j.ajoint.2025.100191","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the infectious keratitis profile, causative agents, underlying risk factors, and clinical outcomes reported over 5 years in Qatar.</div></div><div><h3>Design</h3><div>A retrospective cohort study.</div></div><div><h3>Methods</h3><div>A retrospective analysis of all patients who received treatment for infectious keratitis at Hamad Medical Corporation, Qatar, between January 2019 and December 2023 was performed. Data regarding culture results, risk factors for infectious keratitis, visual acuity at the time of presentation, and the clinical course were obtained from medical records and microbiological reports.</div></div><div><h3>Results</h3><div>A total of 143 patients with microbial keratitis were included. The median age was 38 years (30–47.5). 93 (65 %) of patients were male. The mean follow-up duration was 18 ± 3 months. 34 (21.1 %) were culture-positive, and 107 (79.9 %) were culture-negative. Pseudomonas aeruginosa and Staphylococci spp. were the most common pathogens in culture-positive patients (<em>N</em> = 18, 53 %). Most patients (<em>N</em> = 104, 72.7 %) had at least one risk factor. Of these, 25 (17.5 %) had two risk factors, and 14 (9.8 %) had three or more. Ocular trauma was the most common risk factor (<em>N</em> = 45, 31.5 %), followed by contact lens use (<em>N</em> = 34, 23.8 %) and ocular surface disease (<em>N</em> = 24, 16.8 %). 133 (93 %) of patients improved with medical treatment alone, while 10 (7 %) required surgical interventions. The mean CDVA (logMAR) improved from 0.78 at presentation to 0.48 at the final follow-up (<em>p</em> &lt; 0.001). Multivariable regression analysis showed that culture positivity was significantly associated with poorer corneal healing (OR 1.91; 95 % CI 1.49–2.33). While this indicates a robust association, causal inference cannot be established due to the observational study design.</div></div><div><h3>Conclusions</h3><div>Ocular trauma is the major risk factor for infectious keratitis in Qatar. Pseudomonas aeruginosa and Staphylococci spp. were the most common organisms isolated. Awareness campaigns about occupational hazards and ocular safety equipment could help decrease the burden of trauma-related keratitis in the country.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100191"},"PeriodicalIF":0.0,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing consistency of AI chatbot responses in ophthalmology medical exams","authors":"Elad Shvartz ,&nbsp;Regina Raiyter ,&nbsp;Amelia Goldshtein ,&nbsp;Omri Zur ,&nbsp;Eyal Margalit ,&nbsp;Daniel Bahir","doi":"10.1016/j.ajoint.2025.100189","DOIUrl":"10.1016/j.ajoint.2025.100189","url":null,"abstract":"<div><h3>Purpose</h3><div>Large language models (LLMs) are increasingly evaluated in ophthalmology, often using single test iterations that overlook whether responses remain consistent under repeated conditions. We aim to assess commonly used AI models under multiple testing iterations with varying conditions, including time of day, user credentials, and input type to determine their stability in ophthalmology contexts.</div></div><div><h3>Design</h3><div>Comparative analysis study.</div></div><div><h3>Methods</h3><div>We tested GPT-4o, GPT-4, and Gemini 2.0 Experimental Advanced on 111 multiple-choice questions from the “fundamentals” section of the Israeli ophthalmology residency exams. Each underwent 12 testing iterations, alternating daily testing time and user accounts to assess for potential biases. Iteration-level consistency was assessed by variation in accuracy across runs, while question-level consistency measured agreement in answers per question. Mixed-effects logistic regression estimated the effects of time of day, user account, and question modality. Question-level agreement was further analysed with Fleiss’ κ and response-pattern distributions.</div></div><div><h3>Results</h3><div>Gemini achieved the highest overall accuracy with smallest variation (84.5 %, SD 1.54), followed by GPT-4o (81.2 %, SD 1.75) and GPT-4 (72.4 %, SD 2.98). Mixed-effects models showed significant evening performance decline in GPT-4 (OR 1.61, <em>p</em> = 0.0045). No account-related differences were observed. All models performed markedly worse on image-based items than text (<em>p</em> &lt; 0.001). Question-level analysis revealed high raw consistency but lower corrected consistency, especially for GPT-4.</div></div><div><h3>Conclusions</h3><div>LLMs tested demonstrated stable outputs across repeated questioning, though with notable model-specific variability and consistent challenges in image-based items. Future consistency testing should complement accuracy assessments when evaluating LLMs for potential integration into ophthalmology education and practice.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative evaluation of five AI chatbots in pediatric ophthalmology: A multidomain expert-based appraisal”","authors":"Shweta Dhiman ,&nbsp;Chitaranjan Mishra ,&nbsp;Savleen Kaur ,&nbsp;Paromita Dutta ,&nbsp;Prolima Thacker ,&nbsp;Ashini Maniar ,&nbsp;Raj Kenia ,&nbsp;Logesh Balakrishnan","doi":"10.1016/j.ajoint.2025.100188","DOIUrl":"10.1016/j.ajoint.2025.100188","url":null,"abstract":"<div><h3>Aim</h3><div>To evaluate and compare the performance of five AI chatbots—ChatGPT 3.5 (OpenAI), Google Gemini, Grok (xAI), DeepSeek, and Perplexity AI—in delivering accurate, clear, educational, and safe responses to pediatric ophthalmology-related queries.</div></div><div><h3>Methods</h3><div>Sixteen standardized caregiver-facing questions were posed to each chatbot in separate fresh sessions. Five pediatric ophthalmologists independently rated the responses across four domains—Accuracy, Clarity, Educational Value, and Safety—using a 5-point Likert scale (1–5). This produced 400 ratings per domain (5 chatbots × 16 questions × 5 raters). Inter-rater reliability was assessed using ICC(2,1), ICC(2,5), quadratic-weighted Fleiss’ κ, Gwet’s AC1, and percent agreement. Between-chatbot comparisons were analyzed with cumulative-link mixed models (CLMMs), reporting odds ratios (OR) with 95 % confidence intervals. Post-hoc pairwise contrasts were corrected using Holm adjustment.</div></div><div><h3>Results</h3><div>ChatGPT achieved the highest scores for Accuracy, while Google Gemini and Grok (xAI) showed modest advantages in Clarity and Educational Value. Safety ratings were similar across platforms and clustered at “adequate,” with limited probability of top scores. CLMM analyses confirmed significant between-chatbot differences in Accuracy, Clarity, and Educational Value, but not Safety. Inter-rater reliability was poor-to-fair for single raters [ICC(2,1) = 0.08–0.24], improving to moderate when averaging across all five raters [ICC(2,5) = 0.29–0.61]. Weighted Fleiss’ κ indicated only slight agreement (0.14), but Gwet’s AC1 (0.86) and high percent agreement (94 %) suggested stronger underlying consensus.</div></div><div><h3>Conclusion</h3><div>Performance of AI chatbots varied across domains: ChatGPT led in Accuracy, Gemini and Grok in Clarity and Educational Value, while no system excelled in Safety. Low agreement reflects the difficulty of scoring nuanced AI-generated responses rather than a lack of expert consensus. These findings support the potential of AI chatbots as educational adjuncts in pediatric ophthalmology, while underscoring the need for expert oversight, standardized rubrics, and domain-specific fine-tuning to improve reliability and safety</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying exudative recurrence in neovascular age-related macular degeneration using large language models","authors":"Mariapaola Giordano,&nbsp;Claudio Xompero,&nbsp;Carl-Joe Mehanna,&nbsp;Eric H. Souied","doi":"10.1016/j.ajoint.2025.100186","DOIUrl":"10.1016/j.ajoint.2025.100186","url":null,"abstract":"<div><h3>Purpose</h3><div>To evaluate the ability of a large language model (LLM) to identify exudative recurrence in neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans and compare its performance with retina specialists.</div></div><div><h3>Design</h3><div>Retrospective, single-center, observational study.</div></div><div><h3>Methods</h3><div>Two consecutive OCT scans of patients with nAMD under <em>pro re nata</em> (PRN) regimen were collected. Screen recordings of the complete OCT scan comparisons (previous vs. current) were analyzed by two retina specialists versus ChatGPT-4o after providing a standardized prompt. Main outcome measure was agreement between the LLM and the ophthalmologists in detecting exudative activity. Statistical measures included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen’s kappa for agreement.</div></div><div><h3>Results</h3><div>A total of 102 eyes were included. Among 71 confirmed recurrences, ChatGPT-4o correctly identified 63 and missed 8; of 31 non-recurrences, it correctly identified 10 and misclassified 21 as positive. Overall accuracy was 71.57 %, with sensitivity of 88.73 % and specificity of 32.26 %. Agreement with ophthalmologists was fair (Cohen’s <em>k</em> = 0.238).</div></div><div><h3>Conclusion</h3><div>ChatGPT-4o demonstrated strong sensitivity in detecting exudative recurrences in nAMD, but limited specificity. Given the small, single-center cohort, the lack of external or same-dataset validation, these results should be considered preliminary, but highlights as LLM-assisted analysis, while not yet capable of replacing clinical expertise, may serve as a valuable adjunct for screening exudative changes in nAMD. Further refinements in the LLM could improve specificity and clinical utility.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Text-to-image model for prostaglandin-associated periorbitopathy counseling: a proof-of-concept study","authors":"Ryan S. Huang ,&nbsp;Michael Balas ,&nbsp;David J. Mathew","doi":"10.1016/j.ajoint.2025.100184","DOIUrl":"10.1016/j.ajoint.2025.100184","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the feasibility and utility of a text-to-image artificial intelligence (AI) model in enhancing patient counseling on the cosmetic side effects of prostaglandin analogue (PGA) therapy.</div></div><div><h3>Design</h3><div>Cross-sectional study.</div></div><div><h3>Methods</h3><div>Pre- and post-treatment periocular photographs of PGA-treated patients were collected. To simulate bilateral pre-treatment appearance, untreated eyes were mirrored. The Generative Fill feature powered by Adobe Firefly was applied to masked orbital regions, using descriptive text prompts to generate visualizations of prostaglandin-associated periorbitopathy (PAP), including upper eyelid ptosis, enophthalmos, and hypertrichosis. Prompts were iteratively refined to closely replicate known treatment-related changes.</div></div><div><h3>Results</h3><div>The AI model successfully produced visually realistic images within two minutes that closely resembled the actual post-treatment appearance of PAP. Key manifestations such as eyelash hypertrichosis, enophthalmos, deepened upper lid sulcus, and ptosis were effectively simulated using tailored prompts.</div></div><div><h3>Conclusion</h3><div>This proof-of-concept study demonstrates that text-to-image AI may serve as a novel, rapid, and personalized tool for visualizing potential cosmetic side effects of PGA therapy. By enabling patients to preview changes on their own faces, this technology may enhance informed consent, set realistic expectations, and improve treatment adherence. Future research should evaluate patient perceptions, the accuracy of AI-generated outcomes, and integration into clinical workflows.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to “Comments on “Effectiveness of intracameral antibiotics in reducing postoperative endophthalmitis risk after cataract surgery: A meta-analysis””","authors":"Mohamed Nasser Elshabrawi ,&nbsp;Hashem Abu Serhan","doi":"10.1016/j.ajoint.2025.100187","DOIUrl":"10.1016/j.ajoint.2025.100187","url":null,"abstract":"","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100187"},"PeriodicalIF":0.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating changes in pro- and anti-inflammatory mediators in keratoconus following corneal collagen cross-linking: A systematic review","authors":"Ryan S. Huang ,&nbsp;Andrew Mihalache ,&nbsp;Marko M. Popovic ,&nbsp;Clara C. Chan","doi":"10.1016/j.ajoint.2025.100185","DOIUrl":"10.1016/j.ajoint.2025.100185","url":null,"abstract":"<div><h3>Purpose</h3><div>To investigate the changes in pro- and anti-inflammatory mediators following corneal collagen cross-linking (CXL) in patients with keratoconus (KC).</div></div><div><h3>Design</h3><div>Systematic review.</div></div><div><h3>Methods</h3><div>A comprehensive literature search was conducted using Ovid MEDLINE, Embase, and the Cochrane Library from inception to September 2024. Eligibility criteria included English studies reporting pre- and post-CXL changes in cytokine, chemokine, matrix metalloproteinase (MMP), and growth factor concentrations in adult patients with KC. The primary endpoint was the change in pro- and anti-inflammatory mediator concentrations following CXL. The secondary endpoint focused on correlations between post-CXL changes in inflammatory mediators and corneal tomography measurements. Descriptive statistics were used to report findings.</div></div><div><h3>Results</h3><div>Six prospective cohort studies were included, encompassing 173 eyes from 168 patients diagnosed with progressive KC. The mean age of participants was 25.3 years, and 69 % were male. Three studies reported on cytokine and chemokine levels, demonstrating a significant increase in interleukin-6 (IL-6) concentrations up to three months post-CXL, followed by a decline to levels below pre-CXL at 12 months. Additionally, MMP-9 levels and growth factors, such as nerve growth factor (NGF) and epidermal growth factor (EGF), significantly decreased 12 months post-CXL in two studies, respectively. Significant positive correlations were observed between changes in cytokine levels and tomographic parameters, including keratometry (Ks and Kf) and corneal thickness.</div></div><div><h3>Conclusion</h3><div>IL-6 levels may rise within the first three months post-CXL, followed by a decline to below baseline levels by 12 months. In contrast, MMP-13 concentrations may decrease within days of treatment, and MMP-9 levels may decline after one year. Similarly, both EGF and NGF may show post-CXL reductions, with NGF levels potentially reflecting underlying KC severity. Altogether, these patterns suggest an initial acute inflammatory response, followed by subsequent changes likely associated with the promotion of corneal healing. However, interpretation may be limited by the relatively small number of included studies and heterogeneity in patient severity, sample sizes, sampling methods, assays, and follow-up timepoints. Further research is required to elucidate their role in monitoring treatment response in patients with KC undergoing CXL.</div></div>","PeriodicalId":100071,"journal":{"name":"AJO International","volume":"2 4","pages":"Article 100185"},"PeriodicalIF":0.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145320621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}