Effect of the prism and Maddox rod test as the surgical target for type III acute acquired comitant esotropia.

Introduction: This study aims to explore more accurate and efficient examination methods to provide precise target surgical measurements for patients with type III acute acquired comitant esotropia (AACE). Methods: The study conducted a retrospective analysis of 108 patients diagnosed with AACE who received surgical treatment at the Department of Ophthalmology, the First Affiliated Hospital of Fujian Medical University, from January 2018 to September 2023. All patients underwent examinations of the deviation angle, including the Hirschberg test, prism and Maddox rod test (PMT), and prism and alternate cover test (PACT). For the PACT, the minimum value (PACTmin) and maximum value (PACTmax) were obtained based on differences in examination methods, as well as the deviation angle range (PACT range), which represents the difference between PACTmax and PACTmin. Postoperatively, these patients were followed up for at least 6 months to assess changes in eye position and whether diplopia symptoms recurred. Results: In both near and distant examinations, the results of PACTmax were significantly greater than those of PACTmin (p < 0.001), while the deviation angles obtained from PACTmax and PMT showed no significant statistical difference [p = 0.689 (33 cm), p = 0.436 (5 m)]. There was a strong linear correlation between PACTmin and PMT at both near (R = 0.8887) and distant (R = 0.8950) distances, but each PACTmin corresponded to multiple PMT values. There was no significant difference between the results of PACT range at near and distant distances (p = 0.531). The deviation angles obtained by PMT and PACTmin significantly decreased postoperatively compared to preoperative values, and diplopia disappeared in all patients, with alternative cover test showing no movement or presenting as an esophoria state. Conclusion: The PMT can provide precise target surgical measurements for type III AACE, making it a fast, effective, and cost-efficient examination method. It is worthy of being promoted and applied in clinical practice.

Comparing forceps and self-assembled intraocular rare earth magnet in removing metallic intraocular foreign bodies in 25-guage vitrectomy.

PURPOSE: To compare the efficacy and efficiency of self-assembled intraocular rare earth magnet and forceps in removing intraocular foreign bodies(IOFBs) undergoing 25-gauge(G) pars plana vitrectomy. METHODS: A total of 30 patients with metallic IOFB underwent 25-G PPV were enrolled into this study. Self-assembled intraocular rare earth magnet were used in 15 patients(bar group), and forceps were used in 15 patients(forceps group). Success rate of removing IOFB, time taken to remove IOFB, incidence of IOFB slippage and fall, iatrogenic retinal damages were compared between the two groups. RESULTS: There was no significant difference in success rate of removing IOFBs between the groups(93.3% and 100%, P > 0.99). The median time taken of removing FB was significantly shorter in bar group than in forceps group(112 and 295 s, P = 0.001). None of the patients in bar group had IOFB slippage and fall, or related iatrogenic retinal damage in the process of removal. In forceps group, IOFB slippage and fall during removal were observed in 7 of 15(47.6%) patients, related iatrogenic retinal injuries were recorded in 6 of 15(40.0%) patients, both were significantly higher than bar group(P = 0.003 and P = 0.017, respectively). CONCLUSIONS: Compared with forceps, the assembled intraocular magnet can greatly reduce the possibility of IOFB slippage and fall, prevent related iatrogenic retinal damage, and shorten the time taken to remove IOFB. The assembled intraocular magnet can be an useful tool in removing metallic IOFBs in PPV.

A Deep Learning Model for Detecting Rhegmatogenous Retinal Detachment Using Ophthalmologic Ultrasound Images.

INTRODUCTION: Rhegmatogenous retinal detachment (RRD) is one of the most common fundus diseases. Many rural areas of China have few ophthalmologists, and ophthalmologic ultrasound examination is of great significance for remote diagnosis of RRD. Therefore, this study aimed to develop and evaluate a deep learning (DL) model, to be used for automated RRD diagnosis based on ophthalmologic ultrasound images, in order to support timely diagnosis of RRD in rural and remote areas. METHODS: A total of 6,000 ophthalmologic ultrasound images from 1,645 participants were used to train and verify the DL model. A total of 5,000 images were used for training and validating DL models, and an independent testing set of 1,000 images was used to test the performance of eight DL models trained using four different DL model architectures (fully connected neural network, LeNet5, AlexNet, and VGG16) and two preprocessing techniques (original, original image augmented). Receiver operating characteristic (ROC) curves were used to analyze their performance. Heatmaps were generated to visualize the process of the best DL model in the identification of RRD. Finally, five ophthalmologists were invited to diagnose RRD independently on the same test set of 1,000 images for performance comparison with the best DL model. RESULTS: The best DL model for identifying RRD achieved an area under the ROC curve (AUC) of 0.998 with a sensitivity and specificity of 99.2% and 99.8%, respectively. The best preprocessing method in each model architecture was the application of original image augmentation (average AUC = 0.982). The best model architecture in each preprocessing method was VGG16 (average AUC = 0.998). CONCLUSION: The best DL model determined in this study has higher accuracy, sensitivity, and specificity than the ophthalmologists' diagnosis in identifying RRD based on ophthalmologic ultrasound images. This model may provide support for timely diagnosis in locations without access to ophthalmologic care.

Investigation of Accuracy and Influence Factors of Predicting Lenticule Thickness in Small Incision Lenticule Extraction by Machine Learning Models.

Small-incision lenticule extraction (SMILE) is a safe and effective surgical procedure for refractive correction. However, the nomogram from the VisuMax femtosecond laser system often overestimates the achieved lenticule thickness (LT), leading to inaccurate estimation of residual central corneal thickness in some patients. In order to improve the accuracy of predicting achieved LT, we used machine learning models to make predictions of LT and analyze the influencing factors of LT estimation in this study. We collected nine variables of 302 eyes and their LT results as input variables. The input variables included age, sex, mean K reading of anterior corneal surface, lenticule diameter, preoperative CCT, axial length, the eccentricity of the anterior corneal surface (E), diopter of spherical, and diopter of the cylinder. Multiple linear regression and several machine learning algorithms were employed in developing the models for predicting LT. According to the evaluation results, the Random Forest (RF) model achieved the highest performance in predicting the LT with an R2 of 0.95 and found the importance of CCT and E in predicting LT. To validate the effectiveness of the RF model, we selected additional 50 eyes for testing. Results showed that the nomogram overestimated LT by 19.59% on average, while the RF model underestimated LT by -0.15%. In conclusion, this study can provide efficient technical support for the accurate estimation of LT in SMILE.