Efficacy and effect of intracameral adrenaline infusion on pulse rate and blood pressure during phacoemulsification in patients with dark irides.

OBJECTIVE: Phacoemulsification is the most common cataract surgery that needs optimum circumstances in the field of surgery. This comparative pre- and postoperative study assessed the efficacy and safety of using adrenaline in the irrigating solution as an adjunct to preoperative topical mydriatics in dark irides during Phaco surgery. PATIENTS AND METHODS: This was a prospective observational study that enrolled 421 cataract patients (421 eyes) with dark irides, who were scheduled for Phaco surgery from January 2019 to August 2020. All patients received intraoperative irrigation of a balanced salt solution containing adrenaline. The pulse rate and systolic and diastolic blood pressure of all patients were recorded pre- and postoperatively. In addition, the presence of intraoperative floppy-iris syndrome (IFIS), need for pupil mechanical dilatation, and incidence of posterior capsular rupture were recorded. RESULTS: The sample consisted of 421 patients (421 eyes) all had dark irides. Pulse rate and systolic and diastolic blood pressure did not significantly increase post-operatively (p <0.001). Mechanical dilatation of the pupil was performed in one patient (0.24%) and seven eyes (1.66%) were found to have IFIS. There was no case of posterior capsule rupture. CONCLUSIONS: In comparison with the use of preoperative topical mydriatics alone, adding intracameral adrenaline to the irrigation fluid maintains better pupillary dilatation throughout Phacoemulsification surgery, thereby providing better clinical outcomes in dark irides, even in those with IFIS. Its use has no incremental effect on either blood pressure or pulse rate.

Automated detection of diabetic retinopathy using machine learning classifiers.

OBJECTIVE: Diabetic Retinopathy (DR) is a highly threatening microvascular complication of diabetes mellitus. Diabetic patients must be screened annually for DR; however, it is practically not viable due to the high volume of patients, lack of resources, economic burden, and cost of the screening procedure. The use of machine learning (ML) classifiers in medical science is an emerging frontier and can help in assisted diagnosis. The few available proposed models perform best when used in similar population cohorts and their external validation has been questioned. Therefore, the purpose of our research is to classify the DR using different ML methods on Saudi diabetic data, propose the best method based on accuracy and identify the most discriminative interpretable features using the socio-demographic and clinical information. PATIENTS AND METHODS: This cross-sectional study was conducted among 327 diabetic patients in Almajmaah, Saudi Arabia. Socio-demographic and clinical data were collected using a systematic random sampling technique. For DR classification, ML algorithm including, linear discriminant analysis, support vector machine, K nearest neighbor, random forest and its variate ranger random forest classifiers were used through cross-validation resampling procedure. RESULTS: In classifying DR, ranger random forest outperforms the other methods by accurately classifying 86% of the DR patients on the test data. HbA1c (p<0.001) and duration of diabetes (p<0.001) were the most influential risk factor that best discriminated the DR patients. Other influential risk factors were the body mass index (p<0.001), age-onset (p<0.001), age (p<0.001), systolic blood pressure (p<0.05), and the use of medication (p<0.05) that significantly discriminated the DR patients. CONCLUSIONS: Based on the present study findings, integrating ophthalmology and ML can transform diagnosing the disease pattern that can help generate a compelling clinical effect. ML can be used as an added tool for clinical decision-making and must not be the sole substitute for a clinician. We will work to examine the classification performance of multi-class data using more sophisticated ML methods.