Objective:To explore the clinical efficacy of super-selective ophthalmic artery thrombolysis in the treatment of central retinal artery occlusion (CRAO).Methods:This is a retrospective case series study,based on the analysis of clinical data of 50 non-arteritic CRAO patients. The patients were advised to be treated with super-selective intra-ocular arterial thrombolysis at the Neurosurgery Department, Shenyang No. 4 People′s Hospital from May to December 2024, and treated with intra-arterial thrombolysis and postoperative management guidance by the Department of Neurosurgery, General Hospital of the Northern Theater Command. There were 36 males and 14 females, aged (59.5±10.2)years (range: 41 to 75 years). There were 5 cases of complete obstruction of the central retinal artery and 45 cases of subtotal obstruction.Before the operation, all patients underwent optical coherence tomography angiography (OCTA)+ocular vascular ultrasonography, and their visual acuity was measured using a standard visual acuity logarithmic scale, visual field was measured using the contrast visual field examination method;One week after the operation, all patients were rechecked for OCTA, visual acuity and visual field. The patients′ preoperative and postoperative visual field recovery status were compared. Significant effect was defined as an improvement of more than 3 lines of visual acuity or a complete improvement of visual field defects after treatment compared with pretreatment visual acuity; effectiveness was defined as an improvement of 1 to 2 lines of visual acuity or an improvement of visual field defects after treatment compared with pretreatment visual acuity.Results:The overall effective rate of 50 patients with CRAO treated with super-selective ophthalmic artery urokinase thrombolysis was 94.0% (47/50), with 29 very effective, 18 effective and 3 ineffective. The time from onset to surgery was 0 to 6 hours in 5 patients, with an effective rate of 5/5; >6 to 24 hours in 11 patients, with an effective rate of 10/11; >1 to 7 days in 21 patients, with an effective rate of 90.5%(19/21); >7 to 14 days in 9 patients, with an effective rate of 9/9; and >14 to 21 days in 4 patients, with an effective rate of 4/4, and the difference in effective rate between the different time windows of thrombolytic therapy was not statistically significant ( P=0.961). There were 3 cases of intraoperative and postoperative complications, including 1 case of ophthalmic artery entrapment, 1 case of femoral artery pseudoaneurysm and 1 case of fundus hemorrhage, but all of them were cured after symptomatic treatment. Conclusions:Intra-arterial thrombolysis for CRAO patients has a high effective rate and a low complication rate. The surgical time window can be extended to 21 days after the onset, which is of positive significance for the recovery and improvement of the patient′s final visual acuity.

Objective:To explore the clinical efficacy of super-selective ophthalmic artery thrombolysis in the treatment of central retinal artery occlusion (CRAO).Methods:This is a retrospective case series study,based on the analysis of clinical data of 50 non-arteritic CRAO patients. The patients were advised to be treated with super-selective intra-ocular arterial thrombolysis at the Neurosurgery Department, Shenyang No. 4 People′s Hospital from May to December 2024, and treated with intra-arterial thrombolysis and postoperative management guidance by the Department of Neurosurgery, General Hospital of the Northern Theater Command. There were 36 males and 14 females, aged (59.5±10.2)years (range: 41 to 75 years). There were 5 cases of complete obstruction of the central retinal artery and 45 cases of subtotal obstruction.Before the operation, all patients underwent optical coherence tomography angiography (OCTA)+ocular vascular ultrasonography, and their visual acuity was measured using a standard visual acuity logarithmic scale, visual field was measured using the contrast visual field examination method;One week after the operation, all patients were rechecked for OCTA, visual acuity and visual field. The patients′ preoperative and postoperative visual field recovery status were compared. Significant effect was defined as an improvement of more than 3 lines of visual acuity or a complete improvement of visual field defects after treatment compared with pretreatment visual acuity; effectiveness was defined as an improvement of 1 to 2 lines of visual acuity or an improvement of visual field defects after treatment compared with pretreatment visual acuity.Results:The overall effective rate of 50 patients with CRAO treated with super-selective ophthalmic artery urokinase thrombolysis was 94.0% (47/50), with 29 very effective, 18 effective and 3 ineffective. The time from onset to surgery was 0 to 6 hours in 5 patients, with an effective rate of 5/5; >6 to 24 hours in 11 patients, with an effective rate of 10/11; >1 to 7 days in 21 patients, with an effective rate of 90.5%(19/21); >7 to 14 days in 9 patients, with an effective rate of 9/9; and >14 to 21 days in 4 patients, with an effective rate of 4/4, and the difference in effective rate between the different time windows of thrombolytic therapy was not statistically significant ( P=0.961). There were 3 cases of intraoperative and postoperative complications, including 1 case of ophthalmic artery entrapment, 1 case of femoral artery pseudoaneurysm and 1 case of fundus hemorrhage, but all of them were cured after symptomatic treatment. Conclusions:Intra-arterial thrombolysis for CRAO patients has a high effective rate and a low complication rate. The surgical time window can be extended to 21 days after the onset, which is of positive significance for the recovery and improvement of the patient′s final visual acuity.

Objective:To investigate the application value of joint friction sounds in diagnosing meniscus injury of the knee based on machine learning models.Methods:A case-control study was conducted to analyze the clinical data of 17 patients with meniscus injury of the knee (meniscus injury group) admitted to Sir Run Run Shaw Hospital Affiliated to Nanjing Medical University from August 2020 to October 2022, as well as 75 recruited healthy subjects without knee joint diseases (healthy group). The knee joint friction sounds of the subjects were collected in a relatively quiet environment (peak value below 40 dB). The sounds collected in a flexion-extension-flexion mode of exercise were split and divided randomly with a ratio of 4∶1 into the training set (125 segments from the meniscal injury group and 187 segments from the healthy group) and the test set (33 segments from the meniscal injury group and 47 segments from the healthy group). The sounds obtained in a sit-stand-sit mode of exercise were split and divided randomly with a ratio of 4∶1 into the training set (81 segments from the meniscal injury group and 164 segments from the healthy group) and the test set (20 segments from the meniscal injury group and 40 segments from the healthy group). Four machine learning models were built, including support vector machine with linear kernels, radial basis function support vector machine, random forest, and extremely randomized trees. The learning training of the model was performed on the training set, and its model performance was verified with the test set. The time required in a single collection of joint friction sound from the subjects and the interpretation of data analysis was recorded. Knee function of the subjects were scored according to the Lysholm Score before and at 1 day after the test. The accuracy rates of diagnosis of meniscus injury with friction sounds under the two modes of exercise were compared based on the test results to yield an optimal one. The effectiveness of the four models was compared to find the best machine learning model fitting the data frame of this study according to the test results such as accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) obtained with the optimal mode of exercise. The diagnostic accuracy, misdiagnosis rate and missed diagnosis rate of joint friction sound for meniscal injury under the optimal machine learning model with the optimal mode of exercise were observed.Results:The time required in a single collection of joint friction sound ranged from 5 to 10 minutes [(7.1±1.3)minutes], when the time required for interpretation of data analysis was approximately 1 minute. The Lysholm Score before and after the test was (75.6±4.0)points and (77.7±3.7)points respectively in the meniscal injury group ( P>0.05), and (99.6±0.9)points and (99.5±1.0)points respectively in the healthy group ( P>0.05). The diagnosing accuracy rates for flexion-extension-flexion of exercise and sit-stand-sit modes of exercise were 0.775 and 0.817 under the support vector machine model with linear kernels; 0.813 and 0.900 under the radial basis function support vector machine model; 0.800 and 0.867 under the random forest model; 0.800 and 0.900 under the extremely randomized tree model. The accuracy rates for sit-stand-sit mode of exercise were all higher than those for flexion-extension-flexion mode of exercise. In the sit-stand-sit mode of exercise, the extremely randomized tree model had an accuracy rate of 0.900, sensitivity of 0.900, specificity of 0.950, F1 score of 0.900, and AUC of 0.942, which were higher than those under the remaining 3 models, showing better machine learning efficacy. Under the extremely randomized tree model in the sit-stand-sit mode of exercise, 22 (18 true positive and 4 false positive) were diagnosed as meniscal injury and 38 (36 true negative and 2 false negative) as healthy out of 60 segments in the test set (20 from the meniscal injury group and 40 from the healthy group). The diagnostic accuracy of joint friction sounds in diagnosing meniscus injury of the knee was 0.900, with the misdiagnosis rate of 0.100 and the missed diagnosis rate of 0.100. Conclusion:Diagnosis of meniscus injury of the knee with joint friction sounds can shorten time and enhance safety during the examination process. The diagnostic model using machine learning-based artificial intelligence is faster and more stable, which can be used as a diagnostic marker for such injury.