Objective To compare the efficacy of proximal femoral anti-rotation nail and artificial femoral head replacement with bone cement in the treatment of intertrochanteric fracture of femur in elderly patients.Methods70 elderly patients with intertrochanteric fracture of femur were selected as the research subjects,and they were randomly divided into the observation group and the control group according to the digital table,35 cases in each group.The observation group was treated with proximal femoral anti-rotation nail,the control group was treated with artificial femoral head replacement with bone cement.The clinical efficacy of the two groups was observed and compared.Results The operation time,intraoperative bleeding volume of the observation group were (52.63±13.72)min,(115.26±35.17)mL,respectively,which were significantly better than those of the control group[(86.69±21.15)min,(328.72±47.62)mL,t=3.98,9.86,all P<0.05].The time of standing and walking after operation in the observation group were (12.36±3.61)d,(17.51±3.60)d,which were longer than those of the control group[(9.42±3.18)d,(15.18±3.55)d],but the differences were not statistically significant(t=1.36,1.08,all P>0.05).There was no significant difference in the Harris hip score 30 months after operation between the two groups(t=0.18,P>0.05),while the Harris hip score of the observation group after 6 and 12 months were higher than those of the control group,the differences were statistically significant(t=9.04,3.75,all P<0.05).ConclusionCompared with artificial femoral head replacement with bone cement,proximal femoral anti-rotation nail for elderly patients with intertrochanteric fracture of femur has more significant effect,it is worthy of clinical promotion.

Objective:To propose a method of cervical cytology screening based on deep convolutional neural network and compare it with the diagnosis of cytologists.Method:The deep segmentation network was used to extract 618 333 regions of interest (ROI) from 5, 516 cytological pathological images. Combined with the experience of physicians, the deep classification network with the ability to analyze ROI was trained. The classification results were used to construct features, and the decision model was used to complete the classification of cytopathological images.Results:The sensitivity and specificity were 89.72%, 58.48%, 33.95% and 95.94% respectively. Among the smears derived from four different preparation methods, this algorithm had the best effect on natural fallout with a sensitivity of 91.10%, specificity of 69.32%, positive predictive rate of 41.41%, and negative predictive rate of 97.03%.Conclusion:Deep convolutional neural network image recognition technology can be applied to cervical cytology screening.