Objective To explore the value of ultrasound deep learning(DL)model for diagnosis and classification of cystocele.Methods Totally 696 female patients who underwent pelvic floor ultrasound were retrospectively collected and divided into model development dataset(n=576)and test set(n=120).The former included 432 cases of cystocele and 144 cases of non-cystocele,while the latter included 90 cases of cystocele and 30 cases of non-cystocele.Patients in model development dataset were randomly divided into training set(n=460,including 345 cases of cystocele and 115 cases of non-cystocele)and validation set(n=116,including 87 cases of cystocele and 29 cases of non-cystocele)at the ratio of 8∶2.DL model was trained and established using Vision Transformer architecture based on pelvic floor ultrasound data in training and validation sets for diagnosis and classification of cystocele(non-or Green Ⅰ,Ⅱ and Ⅲ type).Taken diagnostic results of senior ultrasound physicians as standard,the diagnostic efficacy of DL model was evaluated,and its diagnostic efficacy and efficiency were compared with those of 2 junior ultrasound physicians.Results The macro average precision,F1 score,area under the curve(AUC)and overall accuracy of DL model for diagnosis and classification of cystocele in validation set was 90.84％,89.28％,0.97 and 89.66％,respectively,while in test set was 80.85％,79.92％,0.92 and 80.00％,respectively.The overall diagnostic accuracy of 2 junior ultrasound physicians for diagnosis and classification of cystocele in test set was 70.00％(84/120)and 68.33％(82/120),respectively,both lower than that of DL model(P=0.023,0.011).The diagnostic time of DL model was 0.098 s for each case,of junior ultrasound physicians was 46(36,56)s for each case,the former had better diagnostic efficacy(P＜0.001).Conclusion Ultrasound DL model could be used for diagnosis and classification of cystocele.

Objective To investigate alterations in plasma complement levels and white matter imaging characteristics,along with their relationship in patients with first-episode schizophrenia(SCZ).Methods Thirty-eight patients with first-episode schizophrenia and 42 healthy controls were enrolled.Whole-brain diffusion tensor imaging(DTI)was performed using a Philips 3.0 T MRI scanner.Tract-based spatial statistics(TBSS)combined with the Johns Hopkins University(JHU)white matter labels atlas was used to extract and compare white matter characteristics between the two groups.Plasma levels of complement components(C1q,C3,C4,factor B,factor H,and factor P)were measured using the MILLIPLEX? human complement assay kit via multiplex analysis.Spearman correlation analysis was conducted to examine the association between plasma complement levels and white matter features.Results The radial diffusivity(RD)of the left fornix/stria terminalis was significantly higher in the patient group compared to the control group[(0.62±0.04)×10-3mm2/s vs.(0.60±0.03)×10-3mm2/s,PFDR=0.048)].Factor H[677.71(551.58,846.21)ng/mL vs.582.76(513.93,729.71)ng/mL,P=0.041]and factor P[71.36(57.30,95.99)ng/mL vs.60.08(46.67,80.03)ng/mL,P=0.011]were both significantly elevated compared to the control group.Moreover,RD values in the left fornix/stria terminalis were negatively correlated with plasma C3 levels in the patient group(r=-0.362,P=0.025).Conclusion Patients with first-episode schizophrenia exhibit white matter microstructural abnormalities in left fornix/stria terminalis,which are significantly associated with plasma complement levels.

Objective To investigate alterations in plasma complement levels and white matter imaging characteristics,along with their relationship in patients with first-episode schizophrenia(SCZ).Methods Thirty-eight patients with first-episode schizophrenia and 42 healthy controls were enrolled.Whole-brain diffusion tensor imaging(DTI)was performed using a Philips 3.0 T MRI scanner.Tract-based spatial statistics(TBSS)combined with the Johns Hopkins University(JHU)white matter labels atlas was used to extract and compare white matter characteristics between the two groups.Plasma levels of complement components(C1q,C3,C4,factor B,factor H,and factor P)were measured using the MILLIPLEX? human complement assay kit via multiplex analysis.Spearman correlation analysis was conducted to examine the association between plasma complement levels and white matter features.Results The radial diffusivity(RD)of the left fornix/stria terminalis was significantly higher in the patient group compared to the control group[(0.62±0.04)×10-3mm2/s vs.(0.60±0.03)×10-3mm2/s,PFDR=0.048)].Factor H[677.71(551.58,846.21)ng/mL vs.582.76(513.93,729.71)ng/mL,P=0.041]and factor P[71.36(57.30,95.99)ng/mL vs.60.08(46.67,80.03)ng/mL,P=0.011]were both significantly elevated compared to the control group.Moreover,RD values in the left fornix/stria terminalis were negatively correlated with plasma C3 levels in the patient group(r=-0.362,P=0.025).Conclusion Patients with first-episode schizophrenia exhibit white matter microstructural abnormalities in left fornix/stria terminalis,which are significantly associated with plasma complement levels.

Objective To explore the value of ultrasound deep learning(DL)model for diagnosis and classification of cystocele.Methods Totally 696 female patients who underwent pelvic floor ultrasound were retrospectively collected and divided into model development dataset(n=576)and test set(n=120).The former included 432 cases of cystocele and 144 cases of non-cystocele,while the latter included 90 cases of cystocele and 30 cases of non-cystocele.Patients in model development dataset were randomly divided into training set(n=460,including 345 cases of cystocele and 115 cases of non-cystocele)and validation set(n=116,including 87 cases of cystocele and 29 cases of non-cystocele)at the ratio of 8∶2.DL model was trained and established using Vision Transformer architecture based on pelvic floor ultrasound data in training and validation sets for diagnosis and classification of cystocele(non-or Green Ⅰ,Ⅱ and Ⅲ type).Taken diagnostic results of senior ultrasound physicians as standard,the diagnostic efficacy of DL model was evaluated,and its diagnostic efficacy and efficiency were compared with those of 2 junior ultrasound physicians.Results The macro average precision,F1 score,area under the curve(AUC)and overall accuracy of DL model for diagnosis and classification of cystocele in validation set was 90.84％,89.28％,0.97 and 89.66％,respectively,while in test set was 80.85％,79.92％,0.92 and 80.00％,respectively.The overall diagnostic accuracy of 2 junior ultrasound physicians for diagnosis and classification of cystocele in test set was 70.00％(84/120)and 68.33％(82/120),respectively,both lower than that of DL model(P=0.023,0.011).The diagnostic time of DL model was 0.098 s for each case,of junior ultrasound physicians was 46(36,56)s for each case,the former had better diagnostic efficacy(P＜0.001).Conclusion Ultrasound DL model could be used for diagnosis and classification of cystocele.