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 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.

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

Objective:To analyze the association between zygomatic change and bone setback or resection and propose a quantitative guidance for L-shaped reduction malarplasty by linear regression analysis based on computed tomographic (CT) scan images.Methods:A retrospective observational study was conducted on patients who underwent L-shaped reduction malarplasty with mortice and tenon joint at the zygomatic arch in Department of Orthognathic and Temporomandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University from January 2017 to September 2022. Bone setback and resection were performed in cases required a classical L-shaped osteoectomy with oblique bone resection (Group Ⅰ). Bone setback was performed in cases required a modified L-shaped osteotomy without bone resection (Group Ⅱ). Wound healing and the occurrence of complications were followed up after operation. The amount of bone setback and resection were calculated by using preoperative and postoperative CT scan images. The unilateral width changes of the anterior, middle, and posterior zygomatic regions(ΔZBP-MFP, ΔZMP-MFP, ΔZAP-MFP, respectively) as well as zygomatic protrusion change(Δzygomatic protrusion) were also evaluated. SPSS 20.0 software was used for statistical analysis. The measurement data was expressed as Mean±SD. Zygomatic width and protrusion change of the two groups was compared by independent t-test. Comparison of complication rates between the two groups was performed using the χ2 test. Correlation analysis using Pearson correlation coefficients was performed between bone resection or setback and zygomatic width or protrusion change. Linear regression analysis was also performed. Results:A total of 80 patients were enrolled. Group Ⅰ consisted of 40 patients [6 males and 34 females; aged (25.2±3.8) years, ranging from 19 to 33 years] who underwent a classical L-shaped osteotomy with both bone setback and resection, while Group Ⅱ consisted of 40 patients [10 males and 30 females; aged (26.0±3.0) years, ranging from 20 to 35 years] who underwent a modified L-shaped osteotomy with bone setback but without bone resection. All patients healed uneventfully during the follow-up period[(12.5±3.3) months, ranging from 5 to 20 months]. There was no significant difference in the incidence of complications such as infection, transient paresthesia, severe swelling and hematoma between the two groups ( P>0.05). No severe complications, such as facial asymmetry, sagging cheek, bone nonunion, were observed. All patients significantly improved facial contours. There was a statistically significant difference (all P<0.01) in ΔZBP-MFP [ (2.52±0.76) mm vs. (1.85±0.40) mm], ΔZMP-MFP [ (3.30±0.54) mm vs. (2.94±0.51) mm] and Δzygomatic protrusion [ (4.42±1.20) mm vs. (3.59±0.84) mm] between Group Ⅰ and Group Ⅱ. No statistical difference was found in ΔZAP-MFP ( P>0.05). Significant correlation was observed between the bone setback or resection and the changes of anterior, middle zygomatic width as well as protrusion in both the two groups ( r=0.60-0.92, all P<0.01), and the linear regression equation was established. The correlation between bone setback or resection and the posterior zygomatic width change was not significant ( P>0.05). Conclusion:There are linear correlations between the unilateral anterior, middle zygomatic width change, zygomatic protrusion change and the unilateral bone setback or resection. The linear regression equations can be used as a quantitative guidance for preoperative surgical planning.

Objective:To evaluate the stability of the zygomatic complex in reduction malarplasty (RM) with different fixation method.Methods:The clinical data of patients with zygomatic arch protrusion at the Department of Orthognathic and Temporomandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University from January 2018 to January 2021 were analyzed retrospectively. All patients underwent L-shaped osteotomy reduction malarplasty which were divided into zygomatic body fixation (ZBF) and zygomatic arch fixation (ZAF) according to fixation technique. As for ZBF, there were 4 different groups including two bicortical screws (2LS), an L-shaped plate with one bicortical screw (LPLS), an L-shaped plate with short-wing on the zygoma (LPwZ) and an L-shaped plate with short-wing on the maxilla (LPwM). As for ZAF, there were 3 different groups including mortice-tenon (MT), 3-hole plate (3HP) and short screw (SS). CT imaging data of two postoperative periods (1 week later; 6 months later) were collected. ITK-SNAP and 3D Slicer software were applied to evaluate the difference in the displacement distance of relevant landmarks of the zygomatic complex, so as to compare the postoperative stability of RM under different fixation methods. Statistical analyses were performed using IBM SPSS Statistics, version 25.0, and Kruskal-Wallis method was used to compare the difference of relevant landmarks displacement distance between ZBF group and ZAF group. P<0.05 was considered statistically significant. Results:60 patients (120 zygomatic arches) who were composed of 21 men and 39 women, aged (27.1±4.9) years were included. There were 30 samples in each group of ZBF and 40 samples in each group of ZAF. Compared with the single L-shaped plate (LPwZ, LPwM) group, the displacement distance of zygomatic complex in 2LS and LPLS groups was shorter ( P<0.05). The three fixation method of zygomatic arch (MT, 3HP, SS) had similar effects on the displacement of zygomatic complex ( P>0.05). Conclusion:After RM, the "two-bridge" fixation method (2LS and LPLS) provides better stability than the single L-shaped plate. The stability of all ZAF is similar when combined with 2LS or LPLS.

Objective:To analyze the association between zygomatic change and bone setback or resection and propose a quantitative guidance for L-shaped reduction malarplasty by linear regression analysis based on computed tomographic (CT) scan images.Methods:A retrospective observational study was conducted on patients who underwent L-shaped reduction malarplasty with mortice and tenon joint at the zygomatic arch in Department of Orthognathic and Temporomandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University from January 2017 to September 2022. Bone setback and resection were performed in cases required a classical L-shaped osteoectomy with oblique bone resection (Group Ⅰ). Bone setback was performed in cases required a modified L-shaped osteotomy without bone resection (Group Ⅱ). Wound healing and the occurrence of complications were followed up after operation. The amount of bone setback and resection were calculated by using preoperative and postoperative CT scan images. The unilateral width changes of the anterior, middle, and posterior zygomatic regions(ΔZBP-MFP, ΔZMP-MFP, ΔZAP-MFP, respectively) as well as zygomatic protrusion change(Δzygomatic protrusion) were also evaluated. SPSS 20.0 software was used for statistical analysis. The measurement data was expressed as Mean±SD. Zygomatic width and protrusion change of the two groups was compared by independent t-test. Comparison of complication rates between the two groups was performed using the χ2 test. Correlation analysis using Pearson correlation coefficients was performed between bone resection or setback and zygomatic width or protrusion change. Linear regression analysis was also performed. Results:A total of 80 patients were enrolled. Group Ⅰ consisted of 40 patients [6 males and 34 females; aged (25.2±3.8) years, ranging from 19 to 33 years] who underwent a classical L-shaped osteotomy with both bone setback and resection, while Group Ⅱ consisted of 40 patients [10 males and 30 females; aged (26.0±3.0) years, ranging from 20 to 35 years] who underwent a modified L-shaped osteotomy with bone setback but without bone resection. All patients healed uneventfully during the follow-up period[(12.5±3.3) months, ranging from 5 to 20 months]. There was no significant difference in the incidence of complications such as infection, transient paresthesia, severe swelling and hematoma between the two groups ( P>0.05). No severe complications, such as facial asymmetry, sagging cheek, bone nonunion, were observed. All patients significantly improved facial contours. There was a statistically significant difference (all P<0.01) in ΔZBP-MFP [ (2.52±0.76) mm vs. (1.85±0.40) mm], ΔZMP-MFP [ (3.30±0.54) mm vs. (2.94±0.51) mm] and Δzygomatic protrusion [ (4.42±1.20) mm vs. (3.59±0.84) mm] between Group Ⅰ and Group Ⅱ. No statistical difference was found in ΔZAP-MFP ( P>0.05). Significant correlation was observed between the bone setback or resection and the changes of anterior, middle zygomatic width as well as protrusion in both the two groups ( r=0.60-0.92, all P<0.01), and the linear regression equation was established. The correlation between bone setback or resection and the posterior zygomatic width change was not significant ( P>0.05). Conclusion:There are linear correlations between the unilateral anterior, middle zygomatic width change, zygomatic protrusion change and the unilateral bone setback or resection. The linear regression equations can be used as a quantitative guidance for preoperative surgical planning.

Objective:To evaluate the stability of the zygomatic complex in reduction malarplasty (RM) with different fixation method.Methods:The clinical data of patients with zygomatic arch protrusion at the Department of Orthognathic and Temporomandibular Joint Surgery, West China Hospital of Stomatology, Sichuan University from January 2018 to January 2021 were analyzed retrospectively. All patients underwent L-shaped osteotomy reduction malarplasty which were divided into zygomatic body fixation (ZBF) and zygomatic arch fixation (ZAF) according to fixation technique. As for ZBF, there were 4 different groups including two bicortical screws (2LS), an L-shaped plate with one bicortical screw (LPLS), an L-shaped plate with short-wing on the zygoma (LPwZ) and an L-shaped plate with short-wing on the maxilla (LPwM). As for ZAF, there were 3 different groups including mortice-tenon (MT), 3-hole plate (3HP) and short screw (SS). CT imaging data of two postoperative periods (1 week later; 6 months later) were collected. ITK-SNAP and 3D Slicer software were applied to evaluate the difference in the displacement distance of relevant landmarks of the zygomatic complex, so as to compare the postoperative stability of RM under different fixation methods. Statistical analyses were performed using IBM SPSS Statistics, version 25.0, and Kruskal-Wallis method was used to compare the difference of relevant landmarks displacement distance between ZBF group and ZAF group. P<0.05 was considered statistically significant. Results:60 patients (120 zygomatic arches) who were composed of 21 men and 39 women, aged (27.1±4.9) years were included. There were 30 samples in each group of ZBF and 40 samples in each group of ZAF. Compared with the single L-shaped plate (LPwZ, LPwM) group, the displacement distance of zygomatic complex in 2LS and LPLS groups was shorter ( P<0.05). The three fixation method of zygomatic arch (MT, 3HP, SS) had similar effects on the displacement of zygomatic complex ( P>0.05). Conclusion:After RM, the "two-bridge" fixation method (2LS and LPLS) provides better stability than the single L-shaped plate. The stability of all ZAF is similar when combined with 2LS or LPLS.

Objective To compare the external morphological characteristics and movement patterns between Schistosoma japonicum and S. sinensis cercariae. Methods S. japonicum and S. sinensis cercariae were heat-fixed, and well-extended cercariae, of 50 each species, were randomly selected for measurement of body length, body width, tail stem length, and tail fork length. The external morphological characteristics of S. japonicum and S. sinensis cercariae were compared. In addition, S. japonicum-infected Oncomelania snails and S. sinensis-infected Tricula snails were observed under a microscope and the movement patterns of S. japonicum and S. sinensis cercariae were compared. Results The mean body length, body width, tail stem length, and tail fork length were (0.16 ± 0.01), (0.05 ± 0.01), (0.14 ± 0.01) mm and (0.06 ± 0.01) mm for S. japonicum cercariae, and (0.13 ± 0.01), (0.05 ± 0.01), (0.13 ± 0.01) mm and (0.06 ± 0.01) mm for S. sinensis cercariae, respectively, and there were significant differences in terms of cercaria body length (t = 14.583, P < 0.05) and tail stem length (t = 3.861, P < 0.05), while no significant differences were seen in terms of body width (t = 0.896, P > 0.05) or tail fork length (t = −0.454, P > 0.05). Microscopy revealed that the tails of both S. japonicum and S. sinensis cercariae swung from side to side and there was no significant difference in their movement pattern. Conclusion S. sinensis and S. japonicum cercariae share highly similar external external morphological characteristics and movement patterns.

Schistosomiasis was once hyper-endemic in Yunnan Province. Following concerted efforts for over 70 years, remarkable achievements have been made for schistosomiasis control in the province. In 2004, the Mid- and Long-term Plan for Schistosomiasis Prevention and Control in Yunnan Province was initiated in Yunnan Province, and the target for transmission control of schistosomiasis was achieved in the province in 2009. Following the subsequent implementation of the Outline for Key Projects in Integrated Schistosomiasis Control Program (2009—2015) and the 13th Five - year Plan for Schistosomiasis Control in Yunnan Province, no acute schistosomiasis had been identified in Yunnan Province for successive 12 years, and no local Schistosoma japonicum infections had been detected in humans, animals or Oncomelania hupensis snails for successive 6 years in the province by the end of 2020. The transmission of schistosomiasis was interrupted in Yunnan Province in 2020. This review summarizes the history of schistosomiasis, changes in schistosomiasis prevalence and progress of schistosomiasis control in Yunnan Province, and proposes the future priorities for schistosomiasis control in the province.

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.