Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

Cataract is the world's leading cause of blindness, and surgery is the most effective treatment for cataract. With the development of femtosecond laser technology and ophthalmic surgical equipment, the application of femtosecond laser systems in cataract surgery is becoming increasingly widespread. It can be used in cataract surgery for corneal incisions, anterior capsulotomy, lens fragmentation, arcuate incisions and other key operations. Compared to traditional surgery, femtosecond laser assisted cataract surgery(FLACS)offers significant advantages in precision, safety and postoperative visual outcomes. Its clinical benefits have garnered growing recognition among ophthalmologists. However, the key technologies and high-precision equipment for FLACS remain predominantly controlled by Western countries. In China, the research in this field began later. This article reviews the technological advancements in FLACS, with a focus on femtosecond laser technology, optical coherence tomography(OCT), artificial intelligence, and clinical application progress. The objective is to provide theoretical foundations and practical insights for the development of ophthalmic medical technology in China.

Objective: To investigate the clinical characteristics, diagnostic approach, and multidisciplinary treatment strategy for a rare case of congenital defect presenting as a complex of hemifacial microsomia with cardiac and spinal deformities, in order to provide a reference for the clinical management of such cases Methods : The clinical data of a 9-year-old patient with hemifacial microsomia (HFM) complicated by post-operative Tetralogy of Fallot and scoliosis were retrospectively analyzed. A definitive diagnosis was established through specialized examinations, imaging studies, bone age assessment, and intellectual evaluation. The patient presented with right-sided HFM (with 3 accessory auricles, a transverse facial cleft, a microform median cleft of the upper lip, hypoplasia of the mandible and facial soft tissues, and agenesis of the right parotid gland and coronoid process), increased orbital distance, dental malalignment, congenital absence of one lateral incisor, and rampant caries in both primary and permanent dentition. The patient had undergone open-heart surgery for Tetralogy of Fallot with a patent foramen ovale four years prior and also presented with scoliosis and systemic developmental delay (bone age approximately 7 years). A retrospective analysis of the diagnosis and treatment of this type of case was conducted in conjunction with a literature review. Results: A multi-disciplinary treatment (MDT) model was adopted. The patient first received treatment for dental caries, followed by excision of the right accessory auricles, repair of the transverse facial cleft, and correction of the microform upper lip cleft under general anesthesia. A 6-month follow-up showed significant improvement in facial appearance and good recovery of oral function. The literature review indicated that hemifacial microsomia is a congenital disease characterized by the hypoplasia of multiple tissue structures on one side of the face. Its etiology may be related to impaired blood supply to the first and second branchial arches during early pregnancy. It often affects the craniofacial bones, ears, and soft tissues, leading to functional impairments in respiration, feeding, speech, and hearing, as well as psychological issues, severely impacting the quality of life in serious cases. The combination with cardiac and spinal deformities is relatively rare and requires individualized sequential treatment plans based on clinical evaluation and surgical indications. This typically includes cardiac surgical correction, spinal orthopedics, early soft and hard tissue reconstruction (e.g., distraction osteogenesis, facial cleft repair, and accessory auricle excision), orthodontic and dental management during the growth period, and final facial contouring in adulthood. Conclusion HFM can be associated with cardiac and spinal deformities, presenting with complex clinical manifestations. Early diagnosis, MDT collaboration, and sequential treatment plans are key to improving patients’ prognosis and quality of life.

Digital technologies have become an integral part of complete denture restoration. With advancement in computer-aided design and computer-aided manufacturing (CAD/CAM), tools such as intraoral scanning, facial scanning, 3D printing, and numerical control machining are reshaping the workflow of complete denture restoration. Unlike conventional methods that rely heavily on clinical experience and manual techniques, digital technologies offer greater precision, predictability, and efficacy. They also streamline the process by reducing the number of patient visits and improving overall comfort. Despite these improvements, the clinical application of digital complete denture restoration still faces challenges that require further standardization. The major issues include appropriate case selection, establishing consistent digital workflows, and evaluating long-term outcomes. To address these challenges and provide clinical guidance for practitioners, this expert consensus outlines the principles, advantages, and limitations of digital complete denture technology. The aim of this review was to offer practical recommendations on indications, clinical procedures and precautions, evaluation metrics, and outcome assessment to support digital restoration of complete denture in clinical practice.
Humans ; Denture, Complete ; Computer-Aided Design ; Denture Design/methods* ; Consensus ; Printing, Three-Dimensional

Image 1.

Objective To analyze dynamic contrast-enhanced MRI (DCE-MRI) radiomic features using machine learning algorithms, and to develop and validate a predictive model for HER-2 status in breast cancer. Methods The DCE-MRI images of 272 treatment-naive female patients with breast cancer between 2020 and 2022 were included in this study. Regions of interest (ROIs) were manually segmented using 3d-Slicer software, and radiomic features were extracted. All patients were randomly divided into training sets or validation sets at a ratio of 4∶1. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature screening on the training set, followed by the development of predictive models using six machine learning algorithms. Internal cross-validation was performed to compare the performance differences between the models. The best-performing model was selected, trained on the training set, and evaluated on the validation set. Evaluation metrics included area under the curve (AUC), sensitivity, specificity, precision, and recall rate. Results The clinical data of patients in the training set and validation set showed no significant differences. Five features were identified by the LASSO algorithm. With these features, six machine learning models were developed on the training set, and their predictive performance was internally cross-validated using the bagging method. XGBoost model had the highest mean AUC (0.696), followed by RF model (0.690); XGBoost model had the highest mean precision (0.756), followed by LR and RF models. Therefore, XGBoost was the optimal model. An HER-2 predictive model was built using the XGBoost algorithm on the training set and applied to the validation set. The AUC, precision, sensitivity, and specificity of the predictive model on the validation set were calculated, and ROC curves, precision-recall curves, calibration curves, and decision-making curves were plotted. Conclusion This study constructed and evaluated different DCE-MRI radiomics-based machine learning models for predicting HER-2 status in breast cancer. Among them, XGBoost algorithm performed the best and has the potential to become a new non-invasive method for preoperative prediction of HER-2 status, providing reliable evidence for personalized clinical diagnosis and treatment.

Fibromyalgia syndrome （FMS） is a refractory， chronic non-articular rheumatic disease characterized by widespread pain throughout the body， for which there are no satisfactory therapeutic drugs or options. There are rich Chinese medical therapies， and some non-drug therapies， such as acupuncture， Tai Chi， and Ba-Duan-Jin， have shown satisfactory efficacy and safety and definite advantages of simultaneously adjusting mind and body. FMS is taken as a disease responding specifically to traditional Chinese medicine （TCM） by the National Administration of Traditional Chinese Medicine in 2018. In order to clarify the research progress in FMS and the clinical advantages of TCM/integrated Chinese and Western medicine， the China Academy of Chinese Medicine organized a seminar for nearly 20 experts in Chinese and Western medicine， including rheumatology， psychology， acupuncture and moxibustion， and encephalopathy， with the topic of difficulties in clinical diagnosis and treatment of FMS and advantages of TCM and Western medicine. The recommendations were reached on the difficulties in early diagnosis and solutions of FMS， mitigation of common non-specific symptoms， preferential analgesic therapy， TCM pathogenesis and treatment advantages， and direction of treatment with integrated Chinese and Western medicine. FMS is currently facing the triple dilemma of low early correct diagnosis， poor patient participation， and unsatisfactory benefit from pure Western medicine treatment. To solve the above problems， this paper suggests that rheumatologists should serve as the main diagnostic force of this disease， and they should improve patient participation in treatment decision-making， implement exercise therapy， and fully utilize the holistic and multidimensional features of TCM， which is effective in alleviating pain， improving mood， and decreasing adverse events. In addition， it is suggested that FMS treatment should rely on both TCM and Western medicine and adopt multidisciplinary joint treatment， which is expected to improve the standard of diagnosis and treatment of FMS in China.

Objective To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden’s index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden’s index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ² = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ² = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ² = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden’s index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

Objective To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. Methods The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Results Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Conclusions Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Objective To compare the clinical efficacy of abdominal ultrasound-guided endoscopic retrograde appendicitis therapy(ERAT)with laparoscopic appendectomy(LA)for acute uncomplicated appendicitis using propensity score matching.Methods The clinical data of 441 patients with acute uncomplicated appendicitis admitted to the Third People's Hospital of Yunnan Province from March 2020 to April 2023 were collected.The cases were classified based on the differences in surgical method and divided into the ERAT group(n = 30)and LA group(n = 411).The clinical efficacy of patients was compared between the two groups after reducing confounding bias by propensity score matching(PSM).Results After PSM,a total of 30 pairs of patients in the two groups were successfully matched,and the baseline data of the two groups met the requirements for comparability.At 24 hours after the operation,the ERAT group exhibited lower white blood cells,neutrophil counts,and C-reactive protein levels compared to the LA group,and these differences were statistically significant(P<0.05).There was no significant difference in the operation time and total effective rate between the ERAT group and the LA group(P>0.05).However,the ERAT group had lower intraoperative blood loss and shorter pain relief time compared to the LA group,and these differences were statistically significant(P<0.05).Conclusion Abdominal ultrasound-guided endoscopic retrograde appendicitis treatment is an effective,safe,and feasible technique with good prospects for the treatment of acute uncomplicated appendicitis.