[Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis].

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.

[Development and Application of Deep Learning-Based Model for Quality Control of Children Pelvic X-Ray Images].

Objective: A deep learning-based method for evaluating the quality of pediatric pelvic X-ray images is proposed to construct a diagnostic model and verify its clinical feasibility. Methods: Three thousand two hundred and forty-seven children with anteroposteric pelvic radiographs are retrospectively collected and randomly divided into training datasets, validation datasets and test datasets. Artificial intelligence model is conducted to evaluate the reliability of quality control model. Results: The diagnostic accuracy, area under ROC curve, sensitivity and specificity of the model are 99.4%, 0.993, 98.6% and 100.0%, respectively. The 95% consistency limit of the pelvic tilt index of the model is -0.052-0.072. The 95% consistency threshold of pelvic rotation index is -0.088-0.055. Conclusion: This is the first attempt to apply AI algorithm to the quality assessment of children's pelvic radiographs, and has significantly improved the diagnosis and treatment status of DDH in children.

A novel approach for screening standard anteroposterior pelvic radiographs in children.

Anteroposterior pelvic radiography is the first-line imaging modality for diagnosing developmental dysplasia of the hip (DDH). Nonstandard radiographs with pelvic malposition make the correct diagnosis of DDH challenging. However, as the only method available for screening standard pelvic radiographs, traditional manual assessment is relatively laborious and potentially erroneous. We retrospectively collected 3,247 pelvic radiographs. There were 2,887 radiographs randomly selected to train and optimize the AI model. Then 362 radiographs were used to test the model's diagnostic performance. Its diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves and measurement consistency using Bland-Altman plots. In 362 radiographs, the AI model's area under ROC curves, accuracy, sensitivity, and specificity for quality assessment was 0.993, 99.4% (360/362), 98.6% (138/140), and 100.0% (222/222), respectively. Compared with clinicians, the 95% limits of agreement (Bland-Altman analysis) for pelvic tilt index (PTI) and pelvic rotation index (PRI), as determined by the model, were -0.052-0.072 and -0.088-0.055, respectively. CONCLUSIONS: The artificial intelligence-assisted method was more efficient and highly consistent with clinical experts. This method can be used for real-time validation of the quality of pelvic radiographs in current picture archiving and communications systems (PACS). WHAT IS KNOWN: • Nonstandard pediatric radiographs with pelvic malposition make the correct diagnosis of developmental dysplasia of the hip (DDH) challenging. • Traditional manual assessment remains the only method available for screening standard pediatric pelvic radiographs, which is relatively laborious and potentially erroneous. WHAT IS NEW: • This study proposed an artificial intelligence-assisted model to assess the quality of pediatric pelvic radiographs accurately and efficiently. • We recommend the integration of the model into current picture archiving and communications systems (PACS) for real-time screening of standard pediatric pelvic radiographs.

Clinical thought-based software for diagnosing developmental dysplasia of the hip on pediatric pelvic radiographs.

Background: The common methods of radiographic diagnosis of developmental dysplasia of the hip (DDH) include measuring hip parameters and quantifying the degree of hip dislocation. However, clinical thought-based analysis of hip parameters may be a more effective way to achieve expert-like diagnoses of DDH. This study aims to develop a diagnostic strategy-based software for pediatric DDH and validate its clinical feasibility. Methods: In total, 543 anteroposterior pelvic radiographs were retrospectively collected from January 2017 to December 2021. Two independent clinicians measured four diagnostic indices to compare the diagnoses made by the software and conventional manual method. The diagnostic accuracy was evaluated using the receiver operator characteristic (ROC) curves and confusion matrix, and the consistency of parametric measurements was assessed using Bland-Altman plots. Results: In 543 cases (1,086 hips), the area under the curve, accuracy, sensitivity, and specificity of the software for diagnosing DDH were 0.988-0.994, 99.08%-99.72%, 98.07%-100.00%, and 99.59%, respectively. Compared with the expert panel, the Bland-Altman 95% limits of agreement for the acetabular index, as determined by the software, were -2.09°-2.91° (junior orthopedist) and -1.98°-2.72° (intermediate orthopedist). As for the lateral center-edge angle, the 95% limits were -3.68°-5.28° (junior orthopedist) and -2.94°-4.59° (intermediate orthopedist). Conclusions: The software can provide expert-like analysis of pelvic radiographs and obtain the radiographic diagnosis of pediatric DDH with great consistency and efficiency. Its initial success lays the groundwork for developing a full-intelligent comprehensive diagnostic system of DDH.

Clinical reliability and validity of a video-based markerless gait evaluation method.

Objective: To explore the reliability and validity of gait parameters obtained from gait assessment system software employing a human posture estimation algorithm based on markerless videos of children walking in clinical practice. Methods: Eighteen typical developmental (TD) children and ten children with developmental dysplasia of the hip (DDH) were recruited to walk along a designated sidewalk at a comfortable walking speed. A 3-dimensional gait analysis (3D GA) and a 2-dimensional markerless (2D ML) gait evaluation system were used to extract the gait kinematics parameters twice at an interval of 2 h. Results: The two measurements of the children's kinematic gait parameters revealed no significant differences (P > 0.05). Intra-class correlation coefficients (ICC) were generally high (ICC >0.7), showing moderate to good relative reliability. The standard error of measurement (SEM) values of all gait parameters measured by the two walks were 1.26°-2.91°. The system software had good to excellent validity compared to the 3D GA, with ICC values between 0.835 and 0.957 and SEM values of 0.87°-1.71° for the gait parameters measured by both methods. The Bland-Altman plot analysis indicated no significant systematic errors. Conclusions: The feasibility of the markerless gait assessment method using the human posture estimation-based algorithm may provide reliable and valid gait analysis results for practical clinical applications.

A facile method to modify polypropylene membrane by polydopamine coating via inkjet printing technique for superior performance.

Membrane surface functionalization based on mussel-inspired polydopamine (PDA) deposition for enhancing antifouling ability has attracted considerable attention. However, high cost of dopamine (DA) and long-time of reaction during self-polymerization of DA in aqueous solution remain the major problems impeding its practical application. This study provided a first report on a low-cost and facile membrane modification approach based on inkjet printing of DA and sodium periodate (SP) to rapidly deposit PDA on polypropylene (PP) membrane. Compared with the pristine PP membrane and DA printed PP membrane, the PDA-SP coated PP membrane demonstrated superior hydrophilicity (67.2°), high pure water permeability (2156.8 L·m-2·h-1) and antifouling property, due to the improved oxidation degree of PDA. Moreover, the modified membrane possesses good chemical stability in aqueous solution over the wide range of pH 2-9. The inkjet printing integrated oxidant-induced mussel-inspired modification proposed in this study is substrate-independent, and can be applied to various geometries and materials, showing broad application prospects in membrane fabrication.