Objective To summarize the etiology mechanism and treatment of iatrogenic blepharoptosis after double eyelid surgery in Asia. Methods To extensively review the literature related to iatrogenic blepharoptosis after double eyelid surgery, and to summarize and analyze the related anatomical mechanism, existing treatment options, and indications. ResultsIatrogenic blepharoptosis is a relatively common complication after double eyelid surgery, sometimes it is combined with other eyelid deformities such as sunken upper eyelid and wide double eyelid, which makes it difficult to repair. The etiology is mainly caused by improper adhesion of tissues and scars, improper removal of upper eyelid tissue, and injury of a link of levator muscle power system. Whether blepharoptosis occurs after double eyelid surgery by incision or suture, it should be repaired by incision. The principles of repair include surgical loosening of tissue adhesion, anatomical reduction, and repair of damaged tissues. The key is to use surrounding tissues or transplanted fat to prevent adhesion. ConclusionWhen repairing iatrogenic blepharoptosis clinically, appropriate surgical methods should be selected based on the causes and severity of the blepharoptosis, combined with treatment principles, in order to achieve better repair results.
In order to solve the current problems in medical equipment maintenance, this study proposed an intelligent fault diagnosis method for medical equipment based on long short term memory network(LSTM). Firstly, in the case of no circuit drawings and unknown circuit board signal direction, the symptom phenomenon and port electrical signal of 7 different fault categories were collected, and the feature coding, normalization, fusion and screening were preprocessed. Then, the intelligent fault diagnosis model was built based on LSTM, and the fused and screened multi-modal features were used to carry out the fault diagnosis classification and identification experiment. The results were compared with those using port electrical signal, symptom phenomenon and the fusion of the two types. In addition, the fault diagnosis algorithm was compared with BP neural network (BPNN), recurrent neural network (RNN) and convolution neural network (CNN). The results show that based on the fused and screened multi-modal features, the average classification accuracy of LSTM algorithm model reaches 0.970 9, which is higher than that of using port electrical signal alone, symptom phenomenon alone or the fusion of the two types. It also has higher accuracy than BPNN, RNN and CNN, which provides a relatively feasible new idea for intelligent fault diagnosis of similar equipment.