RESUMEN
We aimed to develop and validate a deep convolutional neural network (DCNN) model capable of accurately identifying spondylolysis or spondylolisthesis on lateral or dynamic X-ray images. A total of 2449 lumbar lateral and dynamic X-ray images were collected from two tertiary hospitals. These images were categorized into lumbar spondylolysis (LS), degenerative lumbar spondylolisthesis (DLS), and normal lumbar in a proportional manner. Subsequently, the images were randomly divided into training, validation, and test sets to establish a classification recognition network. The model training and validation process utilized the EfficientNetV2-M network. The model's ability to generalize was assessed by conducting a rigorous evaluation on an entirely independent test set and comparing its performance with the diagnoses made by three orthopedists and three radiologists. The evaluation metrics employed to assess the model's performance included accuracy, sensitivity, specificity, and F1 score. Additionally, the weight distribution of the network was visualized using gradient-weighted class activation mapping (Grad-CAM). For the doctor group, accuracy ranged from 87.9 to 90.0% (mean, 89.0%), precision ranged from 87.2 to 90.5% (mean, 89.0%), sensitivity ranged from 87.1 to 91.0% (mean, 89.2%), specificity ranged from 93.7 to 94.7% (mean, 94.3%), and F1 score ranged from 88.2 to 89.9% (mean, 89.1%). The DCNN model had accuracy of 92.0%, precision of 91.9%, sensitivity of 92.2%, specificity of 95.7%, and F1 score of 92.0%. Grad-CAM exhibited concentrations of highlighted areas in the intervertebral foraminal region. We developed a DCNN model that intelligently distinguished spondylolysis or spondylolisthesis on lumbar lateral or lumbar dynamic radiographs.
RESUMEN
Flow-electrode capacitive deionization (FCDI) is a promising electromembrane technology for wastewater treatment and materials recovery. In this study, we used low-cost Na-modified zeolite (Na-zeolite) to prepare a composite flow-electrode (FE) suspension with a small amount of highly conductive carbon black (CB) to remove and recover NH4+ from synthetic and actual wastewater (200 mg-N/L). Compared with conventional activated carbon (AC), the Na-zeolite electrode exhibited a 56.2-88.5% decrease in liquid-phase NH4+ concentration in the FE suspension due to its higher NH4+ adsorption capacity (6.0 vs. 0.2 mg-N/g). The resulting enhancement of NH4+ diffusion to the electrode chamber contributed to the improved performance of FCDI under both constant current (CC) and constant voltage (CV) conditions. The addition of CB to the FE suspension increased the conductivity and facilitated Na-zeolite charging for NH4+ electrosorption, especially in CV mode. NH4+-rich zeolite can be easily separated by sedimentation from CB in the FE suspension, producing a soil conditioner with a high N-fertilizer content suitable for soil improvement and agricultural applications. Overall, our study demonstrates that the novel Na-zeolite-based FCDI can be developed as an effective wastewater treatment technology for both NH4+ removal and recovery as a valuable fertilizer resource.