The construction of standardized and high-quality clinical research database is an important link for clinicians or researchers to carry out clinical medical research,and also an important guarantee for obtaining high-quality scientific research results and publication. A review on the types and challenges of traditional clinical research database creation is provided,focusing on the key technical solutions of constructing high-quality clinical research database based on electronic data capture (EDC) system,including 6 modules of technological innovation program:the combination of interactive web response system and EDC system,logical check,intelligent follow-up management,data dictionary,trace tracing,statistical analysis of research data,so as to solve the deficiency of traditional software system in constructing clinical research database. In addition,the applications of EDC system in clinical research database construction are summarized and its development prospects are discussed for providing references for clinicians or researchers to carry out high-quality clinical researches.

A cascaded 3D pancreas segmentation network (CPS-Net) is proposed to address the challenges in pancreas segmentation caused by its small size and complex anatomical structure. CPS-Net is composed of two components:the first part utilizes ResUNet to quickly localize the pancreas region,while the second part uses a network that fuses depth-wise convolution block and tri-orientated spatial attention module to refine the segmentation results. Specifically,depth-wise convolution block significantly enhances the differentiation between the pancreas and surrounding tissues by extracting multi-scale features layer by layer,while tri-orientated spatial attention module combines axial attention,planar attention and window attention mechanisms to comprehensively capture the detailed structure of the pancreas in a complex background. CPS-Net achieved Dice similarity coefficient,positive predictive value,sensitivity,and Hausdorff distance of 87.42％±1.58％,87.42％±3.52％,87.74％±4.58％,and (0.22±0.08) mm,respectively,on the NIH public dataset,demonstrating its higher pancreas segmentation accuracy and superior performance compared with the current state-of-the-art segmentation networks.

OBJECTIVES: We propose a Contrastive Regional Attention and Prior Knowledge-Infused U-Transformer model (CRAKUT) to address the challenges of imbalanced text distribution, lack of contextual clinical knowledge, and cross-modal information transformation to enhance the quality of generated radiology reports. METHODS: The CRAKUT model comprises 3 key components, including an image encoder that utilizes common normal images from the dataset for extracting enhanced visual features, an external knowledge infuser that incorporates clinical prior knowledge, and a U-Transformer that facilitates cross-modal information conversion from vision to language. The contrastive regional attention in the image encoder was introduced to enhance the features of abnormal regions by emphasizing the difference between normal and abnormal semantic features. Additionally, the clinical prior knowledge infuser within the text encoder integrates clinical history and knowledge graphs generated by ChatGPT. Finally, the U-Transformer was utilized to connect the multi-modal encoder and the report decoder in a U-connection schema, and multiple types of information were used to fuse and obtain the final report. RESULTS: We evaluated the proposed CRAKUT model on two publicly available CXR datasets (IU-Xray and MIMIC-CXR). The experimental results showed that the CRAKUT model achieved a state-of-the-art performance on report generation with a BLEU-4 score of 0.159, a ROUGE-L score of 0.353, and a CIDEr score of 0.500 in MIMIC-CXR dataset; the model also had a METEOR score of 0.258 in IU-Xray dataset, outperforming all the comparison models. CONCLUSIONS The proposed method has great potential for application in clinical disease diagnoses and report generation.
Humans ; Radiology Information Systems ; Radiology

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

The construction of standardized and high-quality clinical research database is an important link for clinicians or researchers to carry out clinical medical research,and also an important guarantee for obtaining high-quality scientific research results and publication. A review on the types and challenges of traditional clinical research database creation is provided,focusing on the key technical solutions of constructing high-quality clinical research database based on electronic data capture (EDC) system,including 6 modules of technological innovation program:the combination of interactive web response system and EDC system,logical check,intelligent follow-up management,data dictionary,trace tracing,statistical analysis of research data,so as to solve the deficiency of traditional software system in constructing clinical research database. In addition,the applications of EDC system in clinical research database construction are summarized and its development prospects are discussed for providing references for clinicians or researchers to carry out high-quality clinical researches.

A cascaded 3D pancreas segmentation network (CPS-Net) is proposed to address the challenges in pancreas segmentation caused by its small size and complex anatomical structure. CPS-Net is composed of two components:the first part utilizes ResUNet to quickly localize the pancreas region,while the second part uses a network that fuses depth-wise convolution block and tri-orientated spatial attention module to refine the segmentation results. Specifically,depth-wise convolution block significantly enhances the differentiation between the pancreas and surrounding tissues by extracting multi-scale features layer by layer,while tri-orientated spatial attention module combines axial attention,planar attention and window attention mechanisms to comprehensively capture the detailed structure of the pancreas in a complex background. CPS-Net achieved Dice similarity coefficient,positive predictive value,sensitivity,and Hausdorff distance of 87.42％±1.58％,87.42％±3.52％,87.74％±4.58％,and (0.22±0.08) mm,respectively,on the NIH public dataset,demonstrating its higher pancreas segmentation accuracy and superior performance compared with the current state-of-the-art segmentation networks.

METHODS: We retrospectively collected CT scan data from 276 patients with pathologically confirmed primary bone tumors from 4 medical centers in Guangdong Province between January, 2010 and August, 2021. A convolutional neural network (CNN) was employed as the deep learning architecture. The optimal baseline deep learning model (R-Net) was determined through transfer learning, and an optimized model (S-Net) was obtained through algorithmic improvements. Multivariate logistic regression analysis was used to screen the clinical features such as sex, age, mineralization location, and pathological fractures, which were then connected with the imaging features to construct the deep learning fusion model (SC-Net). The diagnostic performance of the SC-Net model and machine learning models were compared with radiologists' diagnoses, and their classification performance was evaluated using the area under the receiver operating characteristic curve (AUC) and F1 score. RESULTS: In the external test set, the fusion model (SC-Net) achieved the best performance with an AUC of 0.901 (95% CI: 0.803-1.00), an accuracy of 83.7% (95% CI: 69.3%-93.2%) and an F1 score of 0.857, and outperformed the S-Net model with an AUC of 0.818 (95% CI: 0.694-0.942), an accuracy of 76.7% (95% CI: 61.4%-88.2%), and an F1 score of 0.828. The overall classification performance of the fusion model (SC-Net) exceeded that of radiologists' diagnoses. CONCLUSIONS The deep learning fusion model based on multi-center CT images and clinical features is capable of accurate classification of osseous and chondroid matrix mineralization and may potentially improve the accuracy of clinical diagnoses of osteogenic versus chondrogenic primary bone tumors.
Humans ; Deep Learning ; Bone Neoplasms/diagnostic imaging* ; Retrospective Studies ; Tomography, X-Ray Computed/methods* ; Neural Networks, Computer ; Male ; Female ; ROC Curve ; Algorithms

Objective To minimize variations in treatment outcomes of L5/S1 percutaneous intervertebral radiofrequency thermocoagulation(PIRFT)arising from physician proficiency and achieve precise quantitative risk assessment of the puncture paths.Methods We used a self-developed deep neural network DWT-UNet for automatic segmentation of the magnetic resonance(MR)images of the L5/S1 segments into 7 key structures:L5,S1,Ilium,Disc,N5,Dura mater,and Skin,based on which a needle insertion path planning environment was modeled.Six hard constraints and 6 soft constraints were proposed based on clinical criteria for needle insertion,and the physician's experience was quantified into weights using the analytic hierarchy process and incorporated into the risk function for needle insertion paths to enhance individual case adaptability.By leveraging the proposed skin entry point sampling sub-algorithm and Kambin's triangle projection area sub-algorithm in conjunction with the analytic hierarchy process,and employing various technologies such as ray tracing,CPU multi-threading,and GPU parallel computing,a puncture path was calculated that not only met clinical hard constraints but also optimized the overall soft constraints.Results A surgical team conducted a subjective evaluation of the 21 needle puncture paths planned by the algorithm,and all the paths met the clinical requirements,with 95.24%of them rated excellent or good.Compared with the physician's planning results,the plans generated by the algorithm showed inferior DIlium,DS1,and Depth(P<0.05)but much better DDura,DL5,DN5,and AKambin(P<0.05).In the 21 cases,the planning time of the algorithm averaged 7.97±3.73 s,much shorter than that by the physicians(typically beyond 10 min).Conclusion The multi-constraint optimal puncture path planning algorithm offers an efficient automated solution for PIRFT of the L5/S1 segments with great potentials for clinical application.

Objective To minimize variations in treatment outcomes of L5/S1 percutaneous intervertebral radiofrequency thermocoagulation(PIRFT)arising from physician proficiency and achieve precise quantitative risk assessment of the puncture paths.Methods We used a self-developed deep neural network DWT-UNet for automatic segmentation of the magnetic resonance(MR)images of the L5/S1 segments into 7 key structures:L5,S1,Ilium,Disc,N5,Dura mater,and Skin,based on which a needle insertion path planning environment was modeled.Six hard constraints and 6 soft constraints were proposed based on clinical criteria for needle insertion,and the physician's experience was quantified into weights using the analytic hierarchy process and incorporated into the risk function for needle insertion paths to enhance individual case adaptability.By leveraging the proposed skin entry point sampling sub-algorithm and Kambin's triangle projection area sub-algorithm in conjunction with the analytic hierarchy process,and employing various technologies such as ray tracing,CPU multi-threading,and GPU parallel computing,a puncture path was calculated that not only met clinical hard constraints but also optimized the overall soft constraints.Results A surgical team conducted a subjective evaluation of the 21 needle puncture paths planned by the algorithm,and all the paths met the clinical requirements,with 95.24%of them rated excellent or good.Compared with the physician's planning results,the plans generated by the algorithm showed inferior DIlium,DS1,and Depth(P<0.05)but much better DDura,DL5,DN5,and AKambin(P<0.05).In the 21 cases,the planning time of the algorithm averaged 7.97±3.73 s,much shorter than that by the physicians(typically beyond 10 min).Conclusion The multi-constraint optimal puncture path planning algorithm offers an efficient automated solution for PIRFT of the L5/S1 segments with great potentials for clinical application.

Rosae Radix et Rhizoma is a herbal medicine in a variety of famous Chinese patent medicines, while the quality standard for this medicine remains to be developed due to the insufficient research on the quality of Rosae Radix et Rhizoma from different sources. Therefore, this study comprehensively analyzed the components in Rosae Radix et Rhizoma of different sources from the aspects of extract, component category content, identification based on thin-lay chromatography, active component content determination, and fingerprint, so as to improve the quality control. The results showed that the content of chemical components varied in the samples of different sources, while there was little difference in the chemical composition among the samples. The content of components in the roots of Rosa laevigata was higher than that in the other two species, and the content of components in the roots was higher than that in the stems. The fingerprints of triterpenoids and non-triterpenoids were established, and the content of five main triterpenoids including multiflorin, rosamultin, myrianthic acid, rosolic acid, and tormentic acid in Rosae Radix et Rhizoma was determined. The results were consistent with those of major component categories. In conclusion, the quality of Rosae Radix et Rhizoma is associated with the plant species, producing area, and medicinal parts. The method established in this study lays a foundation for improving the quality standard of Rosae Radix et Rhizoma and provides data support for the rational use of the stem.
Drugs, Chinese Herbal/chemistry* ; Rhizome/chemistry* ; Plant Roots/chemistry* ; Plants, Medicinal ; Quality Control