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Investigation of Artificial Intelligence Techniques for the Management of Cataract Disease: A Systematic Review
Zahra Karbasi, Michaeel Motaghi Niko, Maryam Zahmatkeshan,
Volume 18, Issue 3 (7-2024)
Abstract
Background and Aim: Cataracts are recognized as the cause of 51% of blindness worldwide. Following the promising initial results of artificial intelligence systems in eye diseases, AI algorithms have been applied in the diagnosis of cataracts, grading the severity of cataracts, intraocular lens calculations, and even as an assistive tool in cataract surgery. This study presents a systematic review of AI techniques in the management of cataract disease.
Materials and Methods: This systematic review study was conducted to investigate artificial intelligence techniques to manage cataract disease until November 11, 2023, and based on PRISMA guidelines. We retrieved all relevant articles published in English through a systematic search of PubMed, Scopus, and Web of Science online databases.
Results: In our initial search, 192 records were identified in the databases, and eventually, 23 articles were selected for review. The results indicated that convolutional neural network algorithms (6 articles), recurrent neural networks (1 article), deep convolutional networks (1 article), support vector machines (2 articles), transfer learning (1 article), decision trees (4 articles), random forests (4 articles), logistic regression (3 articles), Bayesian algorithms (3 articles), XGBoost (3 articles), and K-nearest neighbors clustering algorithms (2 articles) were the artificial neural network and machine learning techniques and algorithms utilized. These techniques were employed in the studies for the diagnosis (70%), management (17%), and prediction (13%) of cataract disease.
Conclusion: Various artificial intelligence and machine learning techniques and algorithms can be effective and efficient in diagnosing, grading, managing, and predicting cataracts with high accuracy. In this study, deep learning techniques and convolutional neural networks have made the greatest contribution to cataract diagnosis. Deep learning techniques, decision trees, and Bayesian algorithms were involved in cataract management. Machine learning algorithms such as logistic regression, random forest, artificial neural network, decision tree, K1-nearest neighbor, XGBoost, and adaptive boosting also played a role in cataract prediction. Just as early prediction, diagnosis, and timely referral can reduce future complications of the disease, the use of systems based on artificial intelligence models that have acceptable accuracy can be effective in supporting the decision-making process of physicians and managing this disease.

The Evaluation of Relationship of Preoperative Hypertension with Intraoperative Hemodynamic Changes and Early Post-Operative Cardiovascular and Cerebral Complications in Cataract Surgery
Mahdieh Jafari, Majid Razavi, Sepideh Fanaei Nookar, Mehryar Taghavi Gilani,
Volume 19, Issue 1 (4-2025)
Abstract
Background and Aim: Hypertension is one of the most common comorbidities in cataract surgery and severe hypertension sometimes cause surgery to be postponed. The purpose of this study is to evaluate the relationship between preoperative hypertension and intraoperative hemodynamic changes and postoperative early cardiovascular and cerebral complications on cataract surgery.
Materials and Methods: This study was performed on 160 patients with cataract surgery in Mashhad Khatam-al-anbia hospital. Before induction, the patients were divided into three groups by blood pressure measurement: normotensive (blood pressure <140/90) 100 patients, hypertensive (blood pressure 140/90 to 180/110) 30 patients and hypertensive crisis (blood pressure>180/110) 30 patients. Blood pressure and heart rate were assessed before entering to operating room, before induction and every 5 minutes to the end of surgery, after recovery and ward transfer. Cardiovascular and neurological complications were assessed 24 hours after surgery. Data were analyzed by SPSS software. P<0.05 was considered significant.
Results: There was no significant difference between patients for demographic and preoperative hemodynamic parameters. Preinduction, the blood pressure increased compared to the ward, which was more significant in the hypertensive and hypertensive crisis groups (P=0.001). After induction, systolic blood pressure reduced which was more significant in the hypertensive crisis group than two other groups (P=0.001). The heart rate increased after transferring to the operation room and returned to normal after induction of anesthesia, but in three groups were not statistically significant (P=0.25). Systolic blood pressure < 90 mmHg during the surgery, and also cardiovascular and nervous complications up to 24 hours were not significantly different in three groups (P=0.75 and P=0.08, respectively). 
Conclusion: Blood pressure instability was more common in patients with hypertension crisis, but no early or debilitating complications were observed. Primary hemodynamic changes were rapidly reduced and controlled by induction of anesthesia. According to the findings, preinduction blood pressure alone is not sufficient to cancellation of cataract surgery.

Comparison of the Effects of Dexmedetomidine and Fentanyl with Midazolam and Sufentanil for Anesthesia after Phacoemolsification: A Double-Blind Clinical Trial
Zahra Ataei Barazandeh, Behzad Imani, Erfan Aubi, Elham Soltani, Mohamadreza Ebadian,
Volume 19, Issue 3 (9-2025)
Abstract
Background and Aim: Cataract surgery is one of the most common eye surgeries worldwide. Most individuals undergoing this surgery are elderly patients, and the use of general anesthesia and additional medications can lead to complications during and after the surgery, as well as increased costs for patients. Therefore, the present study aimed to investigate the effects of intravenous Dexmedetomidine and Fentanyl, as well as Midazolam and intravenous Sufentanil, on pain intensity, hemodynamic status, surgeon satisfaction, and the level of sedation in patients undergoing cataract surgery.
Materials and Methods: This double-blind clinical trial was conducted on 80 patients who were candidates for cataract surgery at Farshchian Sina Hospital in Hamadan. Sampling was carried out from March 10, 2023 to August 21, 2024. The intervention group received Dexmedetomidine and intravenous fentanyl, while the control group received midazolam and Sufentanil. Blood pressure, heart rate, and oxygen saturation level, were measured. Pain intensity, surgeon satisfaction, and the level of sedation in patients were measured and recorded. The results of the research utilized qualitative and quantitative variables based on the study groups, using counts (percentages) and means (standard deviations). For the statistical analysis of these variables at baseline, chi-square tests and independent t-tests were employed. To assess the impact of treatment groups on pain throughout the study, repeated measures ANOVA was used, and the interaction between time and group was evaluated. In cases where the outcome variable under study was not normally distributed, non-parametric equivalents such as the Mann-Whitney test were applied. A significance level of P-Value<0.05 was considered. All statistical tests were conducted using Stata software.  
Results: In the intervention group, heart rate and blood pressure decreased over time. Changes in heart rate and blood pressure had no effect on the patients’ recovery process. Oxygen saturation level in the intervention group did not change compared to the control group over time. The medication used in the intervention group did not cause shortness of breath or a decrease in oxygen saturation levels. Additionally, there was no significant difference in pain levels between the two groups. The level of sedation in patients and surgeon satisfaction in the intervention group was higher than the control group.
Conclusion: Based on the results of the research, it is recommended to use Dexmedetomidine in cataract surgery and other surgeries that require sedation.

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