Objective To investigate the factors affecting the vision recovery after transnasal transsphenoidal pituitary adenoma resection.Methods The vision of the left eyes and the right eyes were compared respectively.The vision after and before the operation was also compared by using paired t-test,related factors were analyzed by multiple variable logistic regression model.ResultsThe paired t-test indicated that both eyes' visions were improved after transnasal transsphenoidal pituitary adenoma resection( P ＜0.01 ).The tumor diameter and the vision before operation were two factors affecting the post-operative visions by cumulative logistic regression model.The larger the diameters of the tumors were,the better the vision recoveries after operation( Wald x2 =0.047,OR =2.240,P =0.047).Worse vision lose before operation came up with a better recovery after operation ( Wald x2 =9.684,OR =0.010,P =0.001 ) ; however,age,gender,pathological category,the extent of tumor removal and the time of the vision loss have no significant affects on the vision recovery after operation.ConclusionThe transnasal transsphenoidal pituitary adenoma resection can improve the vision of the patients,and the degree which would to be improved is correlated with the tumor diameter and the vision lose before operation.

Objective To observe efifcacy and safety of catheter ablation for atrial ifbrillation (AF) occurring after surgical valve replacement in patients with rheumatic heart disease (RHD). Methods A total of 23 RHD patients with atrial ifbrillation after surgical valve replacement were enrolled in this study from 2008 to 2013. The clinical characteristics, ablation strategies and successful rate were investigated. Results All the cases included 8 males and 15 females (age, 51.0 ± 9.2 years). Valves replaced were isolated mitral valves (13/23, 56.5%) and multiple valves (10/23, 43.5%). Postoperative AF after cardiac surgery was paroxysmal in 14 patients (60.9%) and nonparoxysmal in 9 cases. Nine patients (39.1%) was in sinus rhythm before cardiac surgery, 4 in paroxysmal AF and 10 in non-paroxysmal AF. The mean interval between the catheter ablation AF and the surgical intervention was (6.9±5.8) years. The postoperative AF duration was (3.1±3.2) years, left and right atrial diameters were (44.1±5.9) mm and (48.1±9.0) mm respectively, left ventricular ejection fraction was 64.0%±8.3%, the mean ablation procedure duration was (156.8±46.6) min, and lfuoroscopy exposure averaged (27.3±11.2) min. Standard pulmonary vein isolation was performed in all cases by using ipsilateral circumferential ablation technique. Additional ablation, including complex fractionated atrial electrograms, mitral and tricuspid isthmus, and left atrial roof, was applied in most of the cases. After a mean follow-up of (29.7±21.2) months (median, 24 months), 60.9%of the patients remained free of AF, 1 died, and 2 lost to follow-up. Conclusions Catheter ablation for AF is effective and safe in patients with RHD after surgical valve replacement. Stepwise ablation strategy may be better for these patients.

Objective:To explore the application value of artificial intelligence (AI) model based on deep learning in breast nodules classification of Breast Imaging Reporting and Data System of ultrasound (BI-RADS-US).Methods:The ultrasound images of 2 426 breast nodules from 1 558 female patients with breast diseases at Beijing Tongren Hospital, Capital Medical University between December 2006 and December 2019 were collected . The image data sets were divided into training (63%), verification (7%), and test (30%) subsets for the construction of AI model. The diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were analyzed by using receiver operating characteristic (ROC) curve. The Cohen weighted Kappa statistic was used to compare the consistency of BI-RADS-US classification among 5 ultrasound doctors' diagnosis with or without AI model assistance. And the changes of BI-RADS-US classification were analyzed before and after each doctor adopted AI model assistance.Results:The differences in diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were statistically significant (all P > 0.05). The consistency among 5 ultrasound doctors was improved due to AI model assistance and Kappa value was increased from 0.433 (category 3), 0.600 (category 4a), 0.614 (category 4b), 0.570 (category 4c) and 0.495 (category 5) to 0.812, 0.704, 0.823, 0.690 and 0.509 (all P < 0.05), respectively. The upgrade and downgrade of BI-RADS-US classification occurred in 5 doctors after the classification of AI model assistance. Downgrade from category 4 to 3 in benign nodules of 56.6% (47/76) and upgrade from category 4 to 5 in malignant nodules of 69.4% (34/49) were mostly observed. Conclusions:AI-assisted BI-RADS-US classification can effectively improve the consistency of classification among the doctors without reducing the diagnostic efficiency. AI model shows clinical values in reducing unnecessary biopsy of partial benign lesions and increasing diagnostic accuracy of partial malignant lesions through the adjustment of breast nodule classification.