Three-dimensional (3D) printing technology, known as additive manufacturing, is an advanced technology that rapidly developed in the late 20^th century. In the medical field, 3D printing technology has achieved breakthrough progress in medical model manufacturing, implant placement, and prosthodontics and orthodontics. In this paper, the application of 3D printing technology from the perspective of orthodontics, such as dental models, clear aligners, orthodontic brackets, transfer trays, and removable appliances, is reviewed. The application of 3D printing technology is increasingly used in the orthodontic field due to its advantages of high accuracy, high efficiency, simple operation and personalization, and an increasing number of economic applications in the medical field are worth the wait.

As the world becomes older, sustainable healthy aging becomes an important goal of social development. Robotic technologies have been widely considered an effective solution to reduce the labor demand and cost in aged care, thus providing satisfiable services to the elderly while keeping the cost low. The global outbreak of corona virus disease 2019 (COVID-19) has strengthened this trend when it impacted the elderly most because (1) the elderly was generally the most vulnerable population group to pandemics, and (2) the resources available to the elderly significantly declined due to lockdown and quarantines. The observations and experience from COVID-19 inspired us to consider the impact of pandemics on sustainable healthy aging, which was largely missing in existing work, leading to the study of the use of robots in general and telepresence robots in specific to aid sustainable healthy aging. The methodology of systematic review is applied to retrieve and analyze the articles published in nine databases between 2010 and 2020. Based on the review, the paper classifies the applications of robots in pandemics into four main categories, including healthcare, social support, education, and manufacturing. Further analysis of these applications revealed the missing features and challenges in applying them to healthy aging. The discoveries and findings in this paper provide practical guidelines for the future design and development of (telepresence) robots.

Background and Purpose: Malignant melanoma is a high-grade skin cancer with high feasibility to metastasize to both regional and distant sites when detected late. Therefore, it is crucial to diagnose this type of cancer at an early stage to ensure effective treatment. The identification of melanocytic lesions is a difficult issue, even for experienced experts. The current development of information technology, particularly related to image analysis and machine learning, is an opportunity to support the work of specialists and detect malignant melanoma more effectively. The aim of this work is to present a melanocytic skin lesion ontology (MLSO) structure, which serves as a basis for a melanoma diagnosis system and includes the formalization of the experts' and literature knowledge. Subjects and Methods: MLSO describes the most commonly used melanoma assessing strategies: Argenziano's (also known as the 7-point checklist), Menzies', and Stolz's (based on the ABCD rule) strategies as well as Chaos and Clues. Results: In this work, a case study was conducted on the description of a dermatoscopic digital image of a melanocytic skin nevus. The nevus was evaluated according to all of the strategies included in the MLSO, and inferences were made based on these strategies. The analyzed lesion was classified as a benign nevus since no malignancy was indicated by any of the applied strategies. Conclusion: Initial results indicate the usefulness of MLSO in diagnosing skin cancer. A significant advantage of MLSO is that it provides results obtained using four strategies. Therefore, the results are more objective and the possible errors may be avoided. The MLSO structure is still developing and will be implemented into an automated skin cancer diagnosis system.

Rotational osteotomy of the base of the femoral neck involves osteotomy at the intertrochanteric level and rotation of the femoral head. That is to rotate the avascular collapse of the femoral head to the nonweight-bearing area of the hip joint, thus effectively avoiding further collapse of the femoral head necrosis. Clinical has achieved a certain effect. However, there are also some problems in the operation, such as the individual difference of the neck-shaft angle and the anteversion angle of the femoral neck of the affected hip joint among different patients, the nonunion of the osteotomy end, the mismatch of the relationship between the head and the acetabulum, and the direction and angle of the head rotation. The combined three-dimensional printing technology is expected to ensure the accuracy of osteotomy, to solve the adverse complications, and to improve the surgical efficacy.

Background: Immune checkpoint inhibitors (ICIs) have significantly improved the therapeutic efficacy and extended the survival of patients with advanced nonsmall-cell lung cancer (NSCLC); however, the benefits were confined to certain populations. How to optimally select eligible patients for ICI therapy needs to be solved. Materials and Methods: A total of 100 NSCLC patients treated with ICIs from two independent studies were included and then were randomly assigned to the train dataset and the validation dataset. A logistic regression model and a decision tree model were developed to predict the objective response outcome. The area under the receiver operating characteristic curves (AUC) of the two models was evaluated. The confusion matrix for decision tree model was also calculated. Results: The comparison of baseline clinical features between the train and validation datasets showed no significant difference. The multivariable logistic regression identified three variables associated with objective response, i.e, age, tumor mutational burden (TMB), and programmed cell death ligand 1. Moreover, a logistic regression model was developed based on these variables. A decision tree, including age and TMB, was also developed. The AUCs of the two models were both 0.85 in the train dataset. And, that was better than each single variable. The AUC of the decision tree in the validation dataset was inferior to that of the logistic regression model. The performance showed that the decision tree could not effectively identify responders. Conclusion: The performance of the decision tree developed in this study was dismal. Its predictive ability was inferior to the logistic regression model.

Objective: To investigate the effect of zoledronic acid on the remission of bone pain in patients with non-small cell lung cancer (NSCLC) during and after radiotherapy. Materials and Methods: A total of 197 NSCLC patients who received radiotherapy for osteopathy with complete recorded visual analog scale (VAS) scores were enrolled. Of these, 79 patients were treated with radiotherapy alone (radiotherapy group), and 118 patients were treated with radiotherapy combined with zoledronic acid (zoledronic acid group). The differences in VAS scores between the two groups were compared. Logistic regression was used to determine the odds ratio (OR) of the complete response rate for osteodynia between the zoledronic acid group and the radiotherapy group. Univariate and multivariate Cox regression analyses were used to evaluate the independent prognostic factors for overall survival (OS). Results: The complete response rate in patients in the zoledronic acid group was significantly higher than that in patients in the radiotherapy group (odds ratio [OR] = 3.201, 95% confidence interval [CI]: 1.559–6.575, P = 0.002). Except for the baseline VAS score, all VAS scores in the zoledronic acid group at different time points during radiotherapy, at the end of radiotherapy, and 1 month after radiotherapy were significantly lower than those in the radiotherapy group (all P < 0.01). Grade 3 constipation was observed in only one patient in the zoledronic acid group. There were no other Grade 3 adverse events. Multivariate Cox regression analysis showed that only the treatment group (zoledronic acid group vs. radiotherapy group, hazard ratio [HR] = 0.720, 95% CI: 0.530–0.978, P = 0.036) and manner of bone destruction (mixture vs. osteolytic, HR = 0.596, 95% CI: 0.424–0.837, P = 0.003) were independent prognostic factors for OS. Conclusion: Zoledronic acid combined with radiotherapy can not only accelerate bone pain control but also prolong survival in NSCLC patients with bone metastases.

Objective: To compare the dosimetric parameters of step-shoot intensity-modulated radiotherapy (sIMRT), dynamic intensity-modulated radiotherapy (dIMRT), and volume-modulated arc therapy (VMAT) in thymoma and to study the feasibility of the ArcCHECK-3DVH system in three intensity-modulated radiotherapy plans to choose a more appropriate intensity-modulated radiotherapy for thymoma. Materials and Methods: Seventeen patients with thymoma were enrolled in this study. Treatment plans of sIMRT, dIMRT, and VMAT for each patient were based on the Monaco treatment planning system (TPS). Dosimetric verification was performed via the ArcCHECK-3DVH system. We compared and analyzed the 3D γ pass rates of the TPS dose calculation and ArcCHECK-3DVH system dose reconstruction with the three gamma criteria (3 mm/3%, 2 mm/2%, and 1 mm/1%) with a threshold of 10%. Dose–volume histogram analysis was used to compare the dose parameters for target volumes, and organs at risk (OARs), such as D_98%, D_50%, D_2%, D_max, V₂₀, and V_5. Monitor units (MUs) and delivery time were also compared. Results: There were significant differences in the three intensity-modulated radiotherapy plans. For target volume, VMAT showed the highest planning target volume (PTV) D_98% and the lowest PTV D_50% compared with sIMRT or dIMRT. The PTV D_2% of VMAT was lower than that of sIMRT and higher than that of dIMRT, and VAMT demonstrated the highest conformity index and MU, lowest homogeneity index, and shortest treatment delivery time. For the OARs, VMAT is not inferior to sIMRT and dIMRT in OARs protection. For the dosimetric verification, the entire area, PTV, lungs, heart, and spinal cord of VMAT showed the highest γ pass rates than the other two techniques under the gamma 3 mm/3% criteria, which was even more pronounced when the stricter gamma criteria of 2 mm/2% and 1 mm/1% were applied. Conclusion: VMAT can be applied to radiotherapy of thymoma, and the accuracy of treatment plan execution can be guaranteed through the ArcCHECK-3DVH system.

To date, the manual segmentation in radiotherapy contouring is featured with time- and effort-consuming and low efficiency. Therefore, it is imperative to develop novel technology to improve the precision and repeatability about the segmentation of radiotherapy contouring. The use of artificial intelligence (AI) delineation in tumor targets during radiotherapy has shown up, which contains the methods based on template atlas, image segmentation, and deep learning. Intelligent delineation of radiotherapy makes the automatic delineation of organs at risk possible, saves operators' time, and reduces the heterogeneity of contouring, which greatly increases the accuracy and quality of the contouring delineation in radiotherapy. All in all, automatic delineation of radiotherapy based on AI is flourishing. Researchers should further learn to build recognized standards and develop mature technologies to fulfill the clinical application in the near future.

Introduction: Digital health software technologies (DHSTs) have the potential to shift models of care delivery and improve patient outcomes. Health-care students lack training to prepare them for the growing breadth and depth of DHSTs that they will need to identify, assess, and use in their future clinical careers. The implementation of this pilot digital health curriculum aimed to provide health-care trainees skills to identify, assess, and use DHSTs in their future clinical careers. Materials and Methods: From June to August of 2021, the authors implemented a 10 weeks pilot educational internship in partnership with the Accelerated Digital Clinical Ecosystem (ADviCE), a multi-institutional collaborative created by physicians within University of California, San Francisco Health Informatics. Interns completed a customized digital health didactic program developed by program faculty, including a speaker series with clinical informaticists in the field, journal club discussions, and intern-led presentations. In addition, interns were given real-world educational opportunities to directly interact with DHST vendors in the process of creating a DHST registry. Interns also completed a postcourse survey to procure feedback about the pilot program's strengths and weaknesses. Results: Interns learned from a didactic educational curriculum that included more than 10 h of live lectures, 3 supplemental readings, and structured interactions with 74 DHST vendors. Based on a survey to assess interns' satisfaction with the internship, interns ranked the expert speaker series the highest with an average standard deviation ranking on a 5-point Likert scale of 4.6 (0.57) and journal club discussions the lowest with a ranking of 4.31 (0.96). Interns also gave feedback on recommending the internship to future students with a ranking of 4.6 (0.65). Conclusion: This first internship led to the positively favored and successful execution of a remote educational experience exploring digital health in medicine, while also giving interns direct interaction with a broad array of digital health companies.

Objective: This study aimed to compare rehabilitation after lobectomy performed with a Da Vinci robot versus thoracoscopy. Materials and Methods: Patients who underwent lobectomy at the Changzhou First People's Hospital from November 2020 to May 2021 were analyzed retrospectively, including 182 patients in the Da Vinci robot group and 280 patients in the thoracoscopy group. Postoperative hospital stay and postoperative pain were compared between groups. Results: The hospitalization cost of the Da Vinci robot group was $ 10874.06 ± 1757.07, which was higher than that of the thoracoscopy group ($ 7500.96 ± 1704.83). The postoperative hospital stay was significantly shorter in the Da Vinci robot group (4.74 ± 2.104 days) than that in the thoracoscopy group (5.55 ± 2.669 days; t = −3.664, P < 0.001). No significant differences between groups were observed in postoperative intensive care unit admission time and postoperative chest tube indwelling time. The proportion of patients with no pain 2 h after the operation was significantly higher in the Da Vinci robot group than in the thoracoscopy group (26.9% vs. 11.8%; χ² = 17.639, P < 0.001). The proportion of patients with no pain during the first activity was significantly higher in the Da Vinci robot group than in the thoracoscopy group (54.9% vs. 39.6%; χ² = 12.109, P = 0.004). Conclusion: The total cost of Da Vinci robotic surgery was higher than that of thoracoscopy for lobectomy, but the postoperative recovery was better, and the pain was less severe.

Purpose: To clarify the diagnostic performance of reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) and compare prostate cancer (PCa) detection rates of rFOV DWI-targeted biopsy (rFOV DWI-TB) with systemic biopsy (SB). Materials and Methods: Ninety-eight consecutive patients with suspected PCa (mean prostate-specific antigen [PSA]: 17.85 ng/mL, range, 4–28 ng/mL) were prospectively enrolled in this study. All rFOV DWI data were carried out using PI-RADS V 2.0 assessment category. All patients underwent a 10-core SB and a further 2–4 cores of rFOV DWI-TB. The performance of rFOV DWI was analyzed, and the cancer detection rates between two methods were compared. Results: The sensitivity, specificity, positive predictive value, and negative predictive value for detecting PCa with rFOV DWI were 85.11%, 92.16%, 90.91%, and 87.04%, respectively. Area under curve for rFOV DWI was 0.886. In the digital rectal examination (DRE) normal and PSA ≥ 10 ng/mL subgroups, the PCa detection rates were statistically greater for rFOV DWI-TB than for SB (both P < 0.05). The mean Gleason score of cancers detected by rFOV DWI-TB was significantly higher than that detected by SB (P < 0.05). In addition, the detection rate for rFOV DWI-TB cores was significantly better than for SB cores (P < 0.001). Conclusion: RFOV DWI allowed for good diagnostic performance in patients suspected of PCa. It may be useful for clinically significant PCa detecting.

In recent years, the treatment of infected tibial bone defects had been a challenge for orthopaedic surgeon. Ilizarov technique had demonstrated its several advantages to repair tibial bone defects, which was recommended by most studies. However, it takes too much time and makes it difficult for patient to persist. Autogenous bone grafts or bone graft substitutes had demonstrated a recognized clinical efficacy, but the existing biomaterials could not meet the clinical requirements including bone induction, structural support, and controllable biodegradability. In order to offer the possibility of individualized treatment, the application of three-dimensional (3D) printing technology in the medical field has been expanding. A 1:1 3D reconstruction model can be used to control the accuracy of implantation in Masquelet's technique for tibial bone defect patients, which could improve the quality and size of induction membrane. However, there are still many disadvantages of its application. Infectious bone defects of the tibia are often frequently accompanied with defect or deficiency of skin, muscle, blood vessels, or some other soft tissues. Moreover, it is difficult to be applied in some hospitals because it requires requirement cooperation of orthopedic surgeons, imaging physicians, and device engineers. This paper reviews the research and application of 3D printing technology in Masquelet membrane induction in patients with infectious tibial bone defect, as well as its clinical advantages and challenges.

Background and Objectives: Digital heart atlases play important roles in computational cardiac simulation and medical image analysis. During the past decades, various heart anatomy models were developed, but they mostly focused on the ventricular part. Recently, a number of whole-heart atlases were developed but they rarely modelled the motion features. This study constructed a whole-heart atlas incorporating dynamic cardiac motion. Materials and Methods: The shape and motion features of the atlas were learnt from a training set of 57 dynamic computed tomographic angiography images including 20 cardiac phases. Inter-subject variations of the heart anatomy and motion were incorporated into the atlas using the statistical shape modelling approach. Clinically relevant physiological parameters (e.g., chamber volumes, ejection fraction, and percentage of systolic phase) were correlated with the shape and motion variations using the linear regression approach. The shape and motion pattern of the atlas can be adapted by adjusting the physiological parameters. Results: Quantitative experiments were conducted to measure the anatomical accuracy of the atlas for whole-heart shape reconstruction of different subjects, a mean Dice score of 0.89–0.93 and a mean surface distance of 1.02–1.91 mm were achieved for the four heart chambers, respectively. Conclusions: This atlas provides a novel computational tool with adjustable shape and motion parameters for cardiac simulation research.

Chinese Visible Human (CVH) data sets have been widely used in anatomical teaching and scientific research. Based on true-color, thin-thickness, and high-resolution images which are much more superior than computed tomography, magnetic resonance imaging, and ultrasound, human organs have been segmented and three-dimensional (3D) reconstructed, and the organs have higher accuracy and more detailed information, which makes complex anatomical structures simplified, and makes abstract anatomical structure visualization. Through CVH and their 3D models, researchers got much more anatomical new finding and understanding about human anatomy, which can update anatomical reference books and atlas, and can provide more human morphological information for medical students, surgeons, and anatomists. Here, we will provide a brief summary of the CVH data sets and its applications in teaching and research in recent years.

Background: The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis. Method: In this paper, based on the study of computer-aided diagnosis (CAD) system, graph-based features are applied to gastric cancer histopathology microscopic image analysis, and a classifier is used to classify gastric cancer cells from benign cells. Firstly, image segmentation is performed. After finding the region, cell nuclei are extracted using the k-means method, the minimum spanning tree (MST) is drawn, and graph-based features of the MST are extracted. The graph-based features are then put into the classifier for classification. Result: Different segmentation methods are compared in the tissue segmentation stage, among which are Level-Set, Otsu thresholding, watershed, SegNet, U-Net and Trans-U-Net segmentation; Graph-based features, Red, Green, Blue features, Grey-Level Co-occurrence Matrix features, Histograms of Oriented Gradient features and Local Binary Patterns features are compared in the feature extraction stage; Radial Basis Function (RBF) Support Vector Machine (SVM), Linear SVM, Artificial Neural Network, Random Forests, k-NearestNeighbor, VGG16, and Inception-V3 are compared in the classifier stage. It is found that using U-Net to segment tissue areas, then extracting graph-based features, and finally using RBF SVM classifier gives the optimal results with 94.29%. Conclusion: This paper focus on a graph-based features microscopic image analysis method for gastric cancer histopathology. The final experimental data shows that our analysis method is better than other methods in classifying histopathological images of gastric cancer.

Background and Purpose: Burnout syndrome (BOS), the popular phenomenon of our pandemic era, is examined in three dimensions: emotional exhaustion, depersonalization and cynicism, and personal inefficiency. One of the known and accepted ways of measuring BOS is Maslach Burnout Inventory (MBI), in which these three dimensions are measured by 22 items, using 5- or 7-point Likert scales. The aim of this study is to eliminate the loss of precision in BOS measurement and handle the subjectivity and uncertainty, as a result, to get rid of the bias caused by the classical way. Methods: To do this, fuzzy conjoint analysis (FCA) is used together with MBI. In the classical way, the calculations are made by assigning crisp values to the answers, which causes scientific bias and loss of precision because Likert scale type answers have subjectivity and uncertainty. Results: When the scores obtained with FCA are examined, all the scores and some BOS levels differ. When the position of the values according to the borders of the BOS levels is taken into account, it can be said that these tiny differences caused by the loss of precision make this difference. Conclusion: Findings show that the resulting scores changed significantly when calculations are made with FCA. Especially, when these scores are interpreted as intervals or grades, as in MBI, even tiny differences may result in significant scientific bias.

Background: The purpose of this study was to establish the newest trends in medical health-care systems. Methods: This is a theoretical reflection about next-generation medicine, which is the first step to begin with an exponential medical health care and break with past models. Results: In the past, the medical health care relied on an evidence-based practice to provide the best treatment options for patients, however, since 2010, a strong economic wave has shaped the perspective into a value-based medicine framework. We are facing new social dynamics and megatrends in our society. The emergence of 4.0 technologies is leading us to a pathway where a next-generation medicine will create an exponential value for the overall health-care ecosystem. Originality: Next-Generation Medicine (NGM) integrates health care into digital ecosystems linked by innovative interfaces, advanced analytics, centric customer models, and digital epidemiology surrounding a new concept of health and disease management. NGM is based on four core capabilities of physicians: creativity, collaboration, communication, and critical thinking added to advanced digital operations that create a systemic risk management. This integration is developed using bidirectional and integrative digital platforms operated by artificial intelligence/Machine Learning (ML) connected to the Internet of things and data collection in the cloud or in the edge computing. It is time for health-care visionaries to set prejudice aside and start contemplating the amazing landscape that next-generation medicine could offer.

Background and Purpose: Three-dimensional printing (3DP) selective laser melting (SLM) and electron beam melting (EBM) technique can construct porous Ti-6Aluminum-4Vanadium (Ti-6Al-4V) scaffolds with special microstructural and biomechanical properties. However, it is still needed to be tested for bone tissue engineering. Materials and Methods: To investigate the microstructure and surface modification of a porous titanium scaffold, 3DP-SLM technique was used, and the mechanical and biological performance of the scaffolds was compared with that fabricated by EBM technique. Ti-6Al-4V scaffolds were computer-designed and fabricated using low-power SLM (L-SLM). The microstructure morphologies of L-SLM Ti-6Al-4V (L-SLM-Ti) scaffolds were determined and compared with EBM-fabricated Ti-6Al-4V (EBM-Ti) scaffolds. Each scaffold was immersed with marrow clot for 1 h until fully combined with bone mesenchymal stem cells in clots. The biomechanical and cellular response of these two kinds of Ti-6Al-4V scaffolds were compared. Results: The L-SLM-Ti scaffolds showed a microstructure closer to the designed parameters than that of the EBM-Ti scaffolds. The L-SLM-Ti scaffold fibers had a rougher surface than the EBM-Ti scaffolds. Meanwhile, L-SLM-Ti scaffolds had a lower elasticity modulus and lower bearing force than EBM-Ti scaffold. Cell proliferation and the relative expression levels of OPN, COL1, and RUNX2 in L-SLM-Ti scaffolds was apparently higher than in the EBM-Ti scaffolds, with no significant difference found between the percentage of live cells found in L-SLM-Ti and EBM-Ti scaffolds. Conclusion: 3DP-Ti-6Al-4V scaffolds fabricated by L-SLM and designed with rougher surfaces and larger pore sizes may have more reasonable biomechanical properties and increased biological performance than traditional EBM-Ti scaffolds. These L-SLM-Ti scaffolds might be suitable candidates for bone defect repair.

Background: Low-resolution magnetic resonance imaging (MRI) has high imaging speed, but the image details cannot meet the needs of clinical diagnosis. More and more researchers are interested in neural network-based reconstruction methods. How to effectively process the super-resolution reconstruction of the low-resolution images has become highly valuable in clinical applications. Methods: We introduced Super-Resolution Convolution Neural Network (SRCNN) into the reconstruction of magnetic resonance images. The SRCNN consists of three layers, the image feature extraction layer, the nonlinear mapping layer, and the reconstruction layer. For the feature extraction layer, a multi-scale feature extraction (MFE) method was used to extract the features in different scales by involving three different levels of views, which is superior to the original feature extraction in views with fixed size. Compared with the original feature extraction only in fixed size views, we used three different levels of views to extract the features of different scales. This MFE could also be combined with residual learning to improve the performance of MRI super-resolution reconstruction. The proposed network is an end-to-end architecture. Therefore, no manual intervention or multi-stage calculation is required in practical applications. The structure of the network is extremely simple by omitting the fully connected layers and the pooling layers from traditional Convolution Neural Network. Results and Conclusions: After comparative experiments,the effectiveness of the MFE SRCNN-based network in super-resolution reconstruction of MR images has been greatly improved. The performance is significantly improved in terms of evaluation indexes peak signal-to-noise ratio and structural similarity index measure, and the detail recovery of images is also improved.

Background and Purpose: To characterize the global physician community's opinions on the use of digital tools for COVID-19 public health surveillance and self-surveillance. Materials and Methods: Cross-sectional, random, stratified survey done on Sermo, a physician networking platform, between September 9 and 15, 2020. We aimed to sample 1000 physicians divided among the USA, EU, and rest of the world. The survey questioned physicians on the risk-benefit ratio of digital tools, as well as matters of data privacy and trust. Statistical Analysis Used: Descriptive statistics examined physicians' characteristics and opinions by age group, gender, frontline status, and geographic region. ANOVA, t-test, and Chi-square tests with P < 0.05 were viewed as qualitatively different. As this was an exploratory study, we did not adjust for small cell sizes or multiplicity. We used JMP Pro 15 (SAS), as well as Protobi. Results: The survey was completed by 1004 physicians with a mean (standard deviation) age of 49.14 (12) years. Enthusiasm was highest for self-monitoring smartwatches (66%) and contact tracing apps (66%) and slightly lower (48–56%) for other tools. Trust was highest for health providers (68%) and lowest for technology companies (30%). Most respondents (69.8%) felt that loosening privacy standards to fight the pandemic would lead to misuse of privacy in the future. Conclusion: The survey provides foundational insights into how physicians think of surveillance.

Background: Telerehabilitation has been recommended as a potential solution to bridge service delivery gap, especially in geographically remote areas with shortage of healthcare personnel and lack of access to physical therapy. This study was aimed to develop and test the feasibility of a smartphone video-based exercise program (VBEP) for patients with knee osteoarthritis (OA). Methods: This two-phase study involved the development and feasibility testing stages. A three-round modified Delphi approach was employed in the development phase involving a panel of four experts and a patient with knee OA. Based on consensus, five types of exercises comprising seated knee flexion and extension, quadriceps isometric setting, quadriceps strengthening exercise, hamstring clenches, and wall squats were developed into a video-program for knee OA. 15 consenting patients with knee OA participated in the feasibility testing of the program after 2 weeks of utilization. Feasibility of the VBEP was assessed using system usability scale and user experience questionnaire, respectively. The quadruple visual analog scale was used to assess the pain intensity. Results: The mean age and pain intensity of the participants were 67.3 ± 6.4 years and 61.1 ± 10.6, respectively. User perceived usability of the VBEP was 77.1 ± 13.1 (out of 100) with a high usability rating of 86.7%. Pragmatic quality score, hedonic quality rating, attractiveness, and perspicuity were 2.2 (out of 3.0), 1.6 (out of 3.0), 2.4 (out of 3.0), and 3.0 (out of 3.0), respectively. Efficiency, dependability, stimulation, and novelty scores were 2.3 (out of 3.0), 1.8 (out of 3.0), 2.3 (out of 3.0), and 1.0 (out of 3.0), respectively. Conclusions: The VBEP for knee OA has high usability and quality rating, as well as good user experience, and it may be a feasible alternative platform for rehabilitation of patients with knee OA.

The treatment of bone defects, especially large-area bone defects caused by trauma, congenital malformations, senile diseases, and other factors, is often the key and difficult point of orthopedic diseases, which often brings a lot of troubles in the daily work of orthopedic physicians. Bone tissue engineering attempts to repair bone defects using three-dimensional (3D) bioprinted living tissue, and induces osteoblasts to differentiate and proliferate by placing biological scaffolds, thus finally forming bone tissue commensurate with the original tissue functional structure, reducing the difficulty of surgery. Compared with the traditional bone grafting surgery, it causes less trauma to patients. It is an emerging technology with crossover and cutting edge, and has huge application space and good application prospect in biomedical field. In this article, the clinical application of 3D bioprinting technology in various orthopedic fields in recent 5 years is briefly discussed.

Background: Some small sample size studies have yielded slightly inconsistent results for comparing the efficacy and toxicity of trifluridine/tipiracil (TAS-102) with or without bevacizumab. This meta-analysis aims to further investigate the additive effect and safety profile of bevacizumab when combined with TAS-102 in patients receiving a salvage-line treatment for metastatic colorectal cancer (mCRC). Methods: A systematic literature search was conducted using PubMed, Web of Science, Cochrane Library, and some oncological conferences by the end of February 2021. No restrictions were placed on the searches. Two reviewers independently performed the retrieval and selection according to the “Patient, Intervention, Comparison, Outcome, Study design” principle. The outcome endpoints included overall survival (OS), progression-free survival (PFS), disease control rate (DCR), and adverse events (AEs). Results: Six controlled trials which recruited 435 refractory mCRC patients were ultimately taken in. Our results suggested that the regimen of TAS-102 plus bevacizumab had a significant advantage in OS, PFS, and DCR over TAS-102 alone (hazard ratio (HR) = 0.43, 95% confidence interval (CI): 0.27–0.67, P < 0.001; HR = 0.48, 95% CI: 0.39–0.59, P < 0.001; OR = 3.19, 95% CI: 1.56–6.50, P = 0.001; respectively). In general, the incidence of AEs was slightly but not obviously higher in the combination therapy group than the monotherapy group (OR = 1.08; 95% CI: 0.89–1.30; P = 0.458). However, the most frequent grade 3 or worse AE was neutropenia (OR = 2.32; 95% CI: 1.53–3.52; P < 0.001) which was higher in the TAS-102 plus bevacizumab group. Meanwhile, the morbidity of anemia (OR = 0.43; 95% CI: 0.22–0.83; P = 0.013) was significantly higher in TAS-102 monotherapy group. Conclusion: TAS-102 plus bevacizumab has promising activity with a manageable safety profile in a salvage-line treatment for mCRC who are refractory or intolerant to standard chemotherapy.

Bioprinting is expected to be a revolutionary technology for application in medicine, bringing hope to countless patients. For a long time, many patients have been experiencing a lack of suitable organs for transplantation, which eventually lead to loss of lives. Bioprinting technology can integrate cells, proteins, cytokines, and other supporting materials, such as biomaterials and hydrogels, to produce biomedical devices with biological functions. However, no bioprinting medical devices have been approved by the National Medical Products Administration, with specific registration and regulatory requirements for bioprinting medical devices still needing to be explored. To standardize the Bioprinting Medical Devices Special Requirements for Quality Management System, Chinese experts in relevant fields were organized to formulate this expert consensus.

Recent clinical research emphasizes utilizing virtual reality (VR) technology in cognitive impairment rehabilitation. The high immersion and polymorphism make a practical option in cases where the current head-mounted devices (HMD). VR has been used in Alzheimer's disease (AD) patients. The function of HMD is better than traditional therapies in clinical diagnosis; however, its application in treatment for AD patients remains unclear. The Web of Science databases were searched from January 2016 to August 2021. In addition, research or review articles were filtered according to the preferred reporting items for systematic reviews and meta-analyses guidelines. We bring into a total of 11 studies published in recent 5 years which show high degree of clinical feasibility. One study showed combined cognitive-behavioral therapy and VR in designing a reminiscence therapy for dementia patients; five other studies were focused on spatial navigation and posture positioning for AD patients and one of them showed effective clinical feasibility; other two studies were aimed at helping AD patients who lacked of attention and failed to propose unique advantage due to the samples data were not large enough for clinical validation. Last three studies were using a comprehensive task model in overall cognitive level and obtain considerable training in specific AD patients. HMD VR has become a reliable tool for cognitive rehabilitation in AD patients' prophylactic treatment: Patients with AD in nursing homes and families showed greater interest in VR-based attention and spatial way finding tasks, and their performance was better for a period of time after the end of the task. Combining the VR tasks with clinical cognitive theory and traditional task models will exert more benefits. More clinical case will further determine the potential of VR in AD patients to develop a practical new path for cognitive rehabilitation in the near future.

Ever since the concept of artificial intelligence (AI) has been suggested, it has undergone years of research and development. Under the current condition of rapid development of information and data technology, AI has shown significant effective value and application capability in multiple fields, especially in medical treatment. AI has become essential for routine medical treatment. This review summarizes the current clinical application of AI in orthopedics, in reference to the basic principle of AI, AI supported in clinical diagnosis, AI supported in clinical decision-making, AI supported clinical surgery, and the combination of AI and telemedicine. At the same time, this review also specifies the advantages, disadvantages, and capability of AI in the current clinical application, to provide some understanding for further research of AI.

The digitalization of medicine promises great advances for global health. Combined with three-dimensional (3D) printing technology, noncontact optical scanner, and computer-aided design, we can make personalized 3D printing scoliosis orthosis for patients across the country – with better diagnostics, personalized treatments, and early disease prevention. We hope optimize the production process of scoliosis orthotics, improve the production efficiency of orthotics, and promote the clinical transformation of 3D-printed scoliosis orthosis. To standardize the design, manufacture, materials, and clinical applications of 3D printing technology in the scoliosis orthosis, Chinese experts in relevant fields were organized to formulate this expert consensus.

Digitalizing clinical trials provide an opportunity to address challenges faced in the Phase I trial settings, where near real-time data capture and data interpretation are prerequisites for iterative decision-making to rapidly adapt trial designs based on emerging insights. Although digital technologies have driven significant improvements in many businesses and organizations, the adoption of digital technologies in clinical trials has been slow. In recognition of this lag, the UpSMART consortium, a 5-year funded program (2020–2024), has been established in Europe between the UK, Spain, and Italy to embrace digital technologies and drive benefits to patients. The consortium, led by the Cancer Research UK Manchester Institute Cancer Biomarker Centre, aims to 'digitalize' Experimental Cancer Medicine Centres in the UK and Early Drug Development Units in Spain and Italy by open-sourcing and sharing digital healthcare products between participating centers across the consortium. The goal is to optimize data capture and interpretation thus accelerating Phase I clinical research to ultimately benefit patients by allowing faster access to tomorrow's medicines.