The medical image registration between preoperative three-dimensional (3D) scan data and intraoperative two-dimensional (2D) image is a key technology in the surgical navigation. Most previous methods need to generate 2D digitally reconstructed radiographs (DRR) images from the 3D scan volume data, then use conventional image similarity function for comparison. This procedure includes a large amount of calculation and is difficult to archive real-time processing. In this paper, with using geometric feature and image density mixed characteristics, we proposed a new similarity measure function for fast 2D-3D registration of preoperative CT and intraoperative X-ray images. This algorithm is easy to implement, and the calculation process is very short, while the resulting registration accuracy can meet the clinical use. In addition, the entire calculation process is very suitable for highly parallel numerical calculation by using the algorithm based on CUDA hardware acceleration to satisfy the requirement of real-time application in surgery.
Algorithms ; Humans ; Imaging, Three-Dimensional ; Tomography, X-Ray Computed ; X-Rays

Objective To construct a traditional Chinese medicine(TCM)knowledge base using knowl-edge graph based on deep learning methods,and to explore the application of joint models in intelligent question answering systems for TCM. Methods Textbooks Prescriptions of Chinese Materia Medica and Chinese Materia Medicawere applied to construct a comprehensive knowledge graph serving as the founda-tion for the intelligent question answering system.In the study,a BERT+Slot-Gated(BSG)deep learning model was applied for the identification of TCM entities and question inten-tions presented by users in their questions.Answers retrieved from the knowledge graph based on the identified entities and intentions were then returned to the user.The Flask framework and BSG model were utilized to develop the intelligent question answering sys-tem of TCM. Results A TCM knowledge map encompassing 3 149 entities and 6 891 relational triples based on the prescriptions and Chinese materia medica was drawn.In the question answer-ing test assisted by a question corpus,the F1 value for recognizing entities when answering 20 types of TCM questions was 0.996 9,and the accuracy rate for identifying intentions was 99.75%.This indicates that the system is both feasible and practical.Users can interact with the system through the WeChat Official Account platform. Conclusion The BSG model proposed in this paper achieved good results in experiments by increasing the vector dimension,indicating the effectiveness of the joint model method and providing new research ideas for the implementation of intelligent question answering sys-tems in TCM.

Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clini-cally interpretable and highly accurate. Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,includ-ing three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaun-dice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification per-formance was selected to extract the diagnostic rule,and its clinical significance was thor-oughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model's clinical in-terpretability. Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaun-dice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the com-mon features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublin-gual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory. Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accura-cy on clinical data characterized by small sample sizes and a class imbalance.

With the widespread use of Internet, the amount of data in the field of traditional Chinese medicine (TCM) is growing exponentially. Consequently, there is much attention on the collection of useful knowledge as well as its effective organization and expression. Knowledge graphs have thus emerged, and knowledge reasoning based on this tool has become one of the hot spots of research. This paper first presents a brief introduction to the development of knowledge graphs and knowledge reasoning, and explores the significance of knowledge reasoning. Secondly, the mainstream knowledge reasoning methods, including knowledge reasoning based on traditional rules, knowledge reasoning based on distributed feature representation, and knowledge reasoning based on neural networks are introduced. Then, using stroke as an example, the knowledge reasoning methods are expounded, the principles and characteristics of commonly used knowledge reasoning methods are summarized, and the research and applications of knowledge reasoning techniques in TCM in recent years are sorted out. Finally, we summarize the problems faced in the development of knowledge reasoning in TCM, and put forward the importance of constructing a knowledge reasoning model suitable for the field of TCM.

Objective: To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases (Shang Han Lun,《伤寒论》) dataset and explore an optimal learning method represented with node attributes based on graph convolutional network (GCN). Methods: Clauses that contain symptoms, formulas, and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs, which were used to propose a node representation learning method based on GCN − the Traditional Chinese Medicine Graph Convolution Network (TCM-GCN). The symptom-formula, symptom-herb, and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes, and thus acquiring the nodes’ sum-aggregations of symptoms, formulas, and herbs to lay a foundation for the downstream tasks of the prediction models. Results: Comparisons among the node representations with multi-hot encoding, non-fusion encoding, and fusion encoding showed that the Precision@10, Recall@10, and F1-score@10 of the fusion encoding were 9.77%, 6.65%, and 8.30%, respectively, higher than those of the non-fusion encoding in the prediction studies of the model. Conclusion Node representations by fusion encoding achieved comparatively ideal results, indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.