AIM： To investigate the effects and safety of neodymium： yttrium-aluminium-garnet（Nd：YAG） laser posterior capsulotomy with vitreous strand cuttingMETHODS： A total of 40 eyes of 37 patients with symptomatic poste...AIM： To investigate the effects and safety of neodymium： yttrium-aluminium-garnet（Nd：YAG） laser posterior capsulotomy with vitreous strand cuttingMETHODS： A total of 40 eyes of 37 patients with symptomatic posterior capsular opacity（PCO） were included in this prospective randomized study and were randomly subjected to either cruciate pattern or round pattern Nd：YAG posterior capsulotomy with vitreous strand cutting（modified round pattern）. The best corrected visual acuity（BCVA）, intraocular pressure（IOP）, refractive error, endothelial cell count（ECC）, anterior segment parameters, including anterior chamber depth（ACD） and anterior chamber angle（ACA） were measured before and 1 mo after the laser posterior capsulotomy. RESULTS： In both groups, the BCVA improved significantly（P〈0.001 for the modified round pattern group, P=0.001 for the cruciate pattern group）; the IOP and ECC did not significantly change. The ACD significantly decreased（P〈0.001 for both） and the ACA significantly increased（P=0.001 for the modified round pattern group and P=0.034 for the cruciate group）. The extent of changes in these parameters was not significantly different between the groups.CONCLUSION： Modified round pattern Nd：YAG laser posterior capsulotomy is an effective and safe method for the treatment of PCO. This method significantly changes the ACD and ACA, but the change in refraction is not significant. Modified round pattern Nd：YAG laser posterior capsulotomy can be considered a good alternative procedure in patients with symptomatic PCO.展开更多
Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector ...Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools.This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)model.The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images.Primarily,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is applied.Besides,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors.Moreover,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification model.The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model.The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset.The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.展开更多
文摘AIM： To investigate the effects and safety of neodymium： yttrium-aluminium-garnet（Nd：YAG） laser posterior capsulotomy with vitreous strand cuttingMETHODS： A total of 40 eyes of 37 patients with symptomatic posterior capsular opacity（PCO） were included in this prospective randomized study and were randomly subjected to either cruciate pattern or round pattern Nd：YAG posterior capsulotomy with vitreous strand cutting（modified round pattern）. The best corrected visual acuity（BCVA）, intraocular pressure（IOP）, refractive error, endothelial cell count（ECC）, anterior segment parameters, including anterior chamber depth（ACD） and anterior chamber angle（ACA） were measured before and 1 mo after the laser posterior capsulotomy. RESULTS： In both groups, the BCVA improved significantly（P〈0.001 for the modified round pattern group, P=0.001 for the cruciate pattern group）; the IOP and ECC did not significantly change. The ACD significantly decreased（P〈0.001 for both） and the ACA significantly increased（P=0.001 for the modified round pattern group and P=0.034 for the cruciate group）. The extent of changes in these parameters was not significantly different between the groups.CONCLUSION： Modified round pattern Nd：YAG laser posterior capsulotomy is an effective and safe method for the treatment of PCO. This method significantly changes the ACD and ACA, but the change in refraction is not significant. Modified round pattern Nd：YAG laser posterior capsulotomy can be considered a good alternative procedure in patients with symptomatic PCO.
文摘Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools.This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)model.The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images.Primarily,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is applied.Besides,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors.Moreover,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification model.The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model.The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset.The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.
