Objective:To investigate the preventive efficacy of interferon α -2b on scar formation after glaucoma filtering opera-tion. Methods:Totally 62 patients with primary glaucoma(68 eyes)were randomly divided into the observation group(31 cases with 36 eyes)and the control group(31 cases with 32 eyes). The patients in the observation group were given wet compress with a piece of cotton infiltrated by interferonα-2b,while the control group was given wet compress with a piece of cotton infiltrated by mitomycin C. After 4-month postoperative follow-up,the type of filtering bleb,complications and the changes of intraocular pressure were compared between the two groups. Results:The observation group was mainly with type II bleb(58. 33%),while the control group was mainly with type I bleb(53. 13%),and there was significant difference in the filtering bleb type between the two groups(P<0. 05). After the operation,the intraocular pressure at each time point in the two groups was decreased significantly when compared with that before the operation(P<0. 05),while the difference between the two groups was not statistically significant(P>0. 05). The percentage of visual invariableness or improvement in the observation group was significantly higher than that in the control group(P<0. 05),and the occurrence of complications after the operation in the observation group was significantly lower than that in the control group( P<0. 05). Conclusion:Interferon α-2b is an ideal drug for anti-scarring formation after glaucoma filtration surgery.

Objective To propose a classification method for ultrasound images of pedicle screw canals based on radiomics analysis,and to evaluate the integrity of the screw canal.Methods With thoracolumbar spine specimens from 4 fresh cadavers,50 pedicle screw canals were pre-established and ultrasound images of the canals were acquired.A total of 2 000 images(1 000 intact and 1 000 damaged canal samples)were selected.The dataset was randomly divided in a 4∶1 ratio using 5-fold cross-validation to form training and testing sets(consisting of 1 600 and 400 samples,respectively).Firstly,the optimal radius of the region of interest was identified using the Otsu's thresholding method,followed by feature extraction using pyradiomics.Principal component analysis and the least absolute shrinkage and selection operator algorithm were employed for dimensionality reduction and feature selection,respectively.Subsequently,3 machine learning models(support vector machine[SVM],logistic regression,and random forest)and 3 deep learning models(visual geometry group[VGG],ResNet,and Transformer)were used to classify the ultrasound images.The performance of each model was evaluated using accuracy.Results With a region of interest radius of 230 pixels,the SVM model achieved the highest classification accuracy of 96.25%.The accuracy of the VGG model was only 51.29%,while the accuracies of the logistic regression,random forest,ResNet,and Transformer models were 85.50%,80.75%,80.17%,and 75.18%,respectively.Conclusion For ultrasound images of pedicle screw canals,the machine learning model performs better than the deep learning model as a whole,and the SVM model has the best classification performance,which can be used to assist physicians in diagnosis.