Background:Percutaneous balloon pulmonary valvuloplasty (PBPV) is the preferred therapy for pulmonary valve stenosis (PVS). This study retrospectively reviewed recent PBPV outcomes in infants with PVS. The aim of this study was to evaluate factors associated with immediate therapeutic outcomes and restenosis during medium-term follow-up.Methods:The study included 158 infants with PVS who underwent PBPV from January 2009 to July 2015. Demographic characteristics and patient records were reviewed, including detailed hospitalization parameters, hemodynamic data before and immediately after balloon dilation, cineangiograms, and echocardiograms before PBPV and at each follow-up. All procedures were performed by more than two experienced operators.Results:Immediately after balloon dilation, the pressure gradient across the pulmonary valve decreased from 73.09 ± 21.89 mmHg (range: 43–151 mmHg) to 24.49 ± 17.00 mmHg (range: 3–92 mmHg; P < 0.001) and the right ventricular systolic pressure decreased from 95.34 ± 23.44 mmHg (range: 60–174 mmHg) to 52.07 ± 18.89 mmHg (range: 22–134 mmHg; P < 0.001). Residual transvalvular pressure gradients of 67.31 ± 15.19 mmHg (range: 50–92 mmHg) were found in 8.2% of patients, indicating poor therapeutic effects; 6.4% of patients had variable-staged restenosis at follow-up and 3.8% underwent reintervention by balloon dilation or surgical repairs. Further analysis demonstrated that the balloon/annulus ratio showed statistically significant differences (P < 0.05) among groups with different therapeutic effects and between the restenosis and no-stenosis groups. Binary logistic regression analysis further revealed that higher balloon/annulus ratio (odds ratio: 0.005, 95% confidence interval: 0–0.39) was an independent protective factor for restenosis. The rate of severe complications was 1.9%.Conclusions:PBPV is a definitive therapy for infants with PVS based on its effectiveness, feasibility, and safety. Restenosis upon medium-term follow-up is relatively rare.

Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic “elfin” facial gestalt. The “elfin” facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs.Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs.Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed.Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models.Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

Background Many genetic syndromes (GSs) have distinct facial dysmorphism, and facial gestalts can be used as a diagnostic tool for recognizing a syndrome. Facial recognition technology has advanced in recent years, and the screening of GSs by facial recognition technology has become feasible. This study constructed an automatic facial recognition model for the identification of children with GSs. Results A total of 456 frontal facial photos were collected from 228 children with GSs and 228 healthy children in Guangdong Provincial People's Hospital from Jun 2016 to Jan 2021. Only one frontal facial image was selected for each participant. The VGG-16 network (named after its proposal lab, Visual Geometry Group from Oxford University) was pretrained by transfer learning methods, and a facial recognition model based on the VGG-16 architecture was constructed. The performance of the VGG-16 model was evaluated by five-fold cross-validation. Comparison of VGG-16 model to five physicians were also performed. The VGG-16 model achieved the highest accuracy of 0.8860 ± 0.0211, specificity of 0.9124 ± 0.0308, recall of 0.8597 ± 0.0190, F1-score of 0.8829 ± 0.0215 and an area under the receiver operating characteristic curve of 0.9443 ± 0.0276 (95% confidence interval: 0.9210–0.9620) for GS screening, which was significantly higher than that achieved by human experts. Conclusions This study highlighted the feasibility of facial recognition technology for GSs identification. The VGG-16 recognition model can play a prominent role in GSs screening in clinical practice.