Does a Video-Based and 3D Animation Hybrid Learning System Improve Teaching Outcomes in Orthopedic Surgery? A Randomized Controlled Trial.

OBJECTIVE: This study aims to evaluate the instructional efficacy of a 3D Surgical Training System (3DSTS), which combines real surgical footage with high-definition 3D animations, against conventional surgical videos and textbooks in the context of orthopedic proximal humerus fracture surgeries. DESIGN: Before the experiment, 89 participants completed a pre-educational knowledge assessment. They were then randomized into 3 groups: the 3DSTS group (n = 30), the surgical video (SV) group (n = 29), and the textbook group (n = 30). After their respective teaching courses, all participants took a posteducational assessment and completed a perceived cognitive load test. The 3DSTS group also filled out a satisfaction survey. Once all assessments were finished, the SV and textbook groups were introduced to the 3DSTS course and subsequently completed a satisfaction survey. All statistical analyses were executed using IBM SPSS version 24 (IBM Corp., Armonk, NY). For data fitting normal distribution, we employed one-way analysis of variance (one-way ANOVA) and Tukey HSD tests, whereas, for non-normally distributed data, we used Kruskal-Wallis H tests and Dunn's tests. The significance level for all tests was set at p < 0.05. SETTING: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, P. R. China. PARTICIPANTS: About 89 doctors who undergoing standardized residents training. RESULT: The initial assessment scores among the three groups were comparable, showing no significant statistical difference. Post-education revealed a marked difference in the scores, with the 3DSTS group outperforming both the SV and textbook groups. Specifically, the 3DSTS group exhibited statistically greater improvement in areas such as procedural steps, and specialized surgical techniques compared to the SV and textbook groups. During the 3DSTS teaching process, participants reported the least perceived cognitive load and expressed strong satisfaction, highlighting that the instructional materials are well-prepared, and considering this teaching method superior and more innovative than previous courses they had encountered. CONCLUSION: The 3D Surgical Training System, integrating real videos with 3D animations, significantly enhances orthopedic surgery education over conventional methods, providing improved comprehension, lower cognitive load, and standardized learning outcomes. Its efficacy and high participant satisfaction underscore its potential for broader adoption in surgical disciplines. This study is registered with ClinicalTrials. gov ID: ChiCTR2300074730.

New augmented reality remote for virtual guidance and education of fracture surgery: a retrospective, non-inferiority, multicenter cohort study.

BACKGROUND: The demand for telesurgery is rapidly increasing. Augmented reality (AR) remote surgery is a promising alternative, fulfilling a worldwide need in fracture surgery. However, previous AR endoscopic and Google Glass remotes remain unsuitable for fracture surgery, and the application of remote fracture surgery has not been reported. We aimed to evaluated the safety and clinical effectiveness of a new AR remote in fracture surgery. MATERIALS AND METHODS: This retrospective non-inferiority cohort study was conducted at three centres. Between January 1, 2018, and March 31, 2022, 800 patients who underwent fracture surgery were eligible for participation. The study enrolled 551 patients with fractures (132 patellae, 128 elbows, 126 tibial plateaus, and 165 ankles) divided into an AR group (specialists used AR to remotely guide junior doctors to perform surgeries) and a traditional non-remote group (specialists performed the surgery themselves). RESULTS: Among 364 patients (182 per group) matched by propensity score, seven (3.8%) in the AR group and four (3%) in the non-remote group developed complications. The 0.005 risk difference (95% confidence interval: -0.033 to 0.044) was below the pre-defined non-inferiority margin of a 10% absolute increase. A similar distribution in the individual components of all complications was found between the groups. Hierarchical analysis following propensity score matching revealed no statistical difference between the two groups regarding functional results at 1-year follow-up, operative time, amount of bleeding, number of fluoroscopies, and injury surgery interval. A Likert scale questionnaire showed positive results (median scores: 4-5) for safety, efficiency, and education. CONCLUSION: This study is the first to report that AR remote surgery can be as safe and effective as that performed by a specialist in person for fracture surgery, even without the physical presence of a specialist, and is associated with improving the skills and increasing the confidence of junior surgeons. This technique is promising for remote fracture surgery and other open surgeries, offering a new strategy to address inadequate medical care in remote areas.

An oxidized dextran-composite self-healing coated magnesium scaffold reduces apoptosis to induce bone regeneration.

Self-healing coatings have shown promise in controlling the degradation of scaffolds and addressing coating detachment issues. However, developing a self-healing coating for magnesium (Mg) possessing multiple biological functions in infectious environments remains a significant challenge. In this study, a self-healing coating was developed for magnesium scaffolds using oxidized dextran (OD), 3-aminopropyltriethoxysilane (APTES), and nano-hydroxyapatite (nHA) doped micro-arc oxidation (MHA), named OD-MHA/Mg. The results demonstrated that the OD-MHA coating effectively addresses coating detachment issues and controls the degradation of Mg in an infectious environment through self-healing mechanisms. Furthermore, the OD-MHA/Mg scaffold exhibits antibacterial, antioxidant, and anti-apoptotic properties, it also promotes bone repair by upregulating the expression of osteogenesis genes and proteins. The findings of this study indicate that the OD-MHA coated Mg scaffold possessing multiple biological functions presents a promising approach for addressing infectious bone defects. Additionally, the study showcases the potential of polysaccharides with multiple biological functions in facilitating tissue healing even in challenging environments.

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures.

Objectives: To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method. Methods: A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals. Results: The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group. Conclusion: This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

Deep learning system assisted detection and localization of lumbar spondylolisthesis.

Objective: Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors' evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Methods: Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set (n = 1,117), a validation set (n = 240), and a test set (n = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals' evaluation. Results: A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s. Conclusion: The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.

Artificial intelligence-aided method to detect uterine fibroids in ultrasound images: a retrospective study.

We explored a new artificial intelligence-assisted method to assist junior ultrasonographers in improving the diagnostic performance of uterine fibroids and further compared it with senior ultrasonographers to confirm the effectiveness and feasibility of the artificial intelligence method. In this retrospective study, we collected a total of 3870 ultrasound images from 667 patients with a mean age of 42.45 years ± 6.23 [SD] for those who received a pathologically confirmed diagnosis of uterine fibroids and 570 women with a mean age of 39.24 years ± 5.32 [SD] without uterine lesions from Shunde Hospital of Southern Medical University between 2015 and 2020. The DCNN model was trained and developed on the training dataset (2706 images) and internal validation dataset (676 images). To evaluate the performance of the model on the external validation dataset (488 images), we assessed the diagnostic performance of the DCNN with ultrasonographers possessing different levels of seniority. The DCNN model aided the junior ultrasonographers (Averaged) in diagnosing uterine fibroids with higher accuracy (94.72% vs. 86.63%, P < 0.001), sensitivity (92.82% vs. 83.21%, P = 0.001), specificity (97.05% vs. 90.80%, P = 0.009), positive predictive value (97.45% vs. 91.68%, P = 0.007), and negative predictive value (91.73% vs. 81.61%, P = 0.001) than they achieved alone. Their ability was comparable to that of senior ultrasonographers (Averaged) in terms of accuracy (94.72% vs. 95.24%, P = 0.66), sensitivity (92.82% vs. 93.66%, P = 0.73), specificity (97.05% vs. 97.16%, P = 0.79), positive predictive value (97.45% vs. 97.57%, P = 0.77), and negative predictive value (91.73% vs. 92.63%, P = 0.75). The DCNN-assisted strategy can considerably improve the uterine fibroid diagnosis performance of junior ultrasonographers to make them more comparable to senior ultrasonographers.

Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography.

Purpose: To develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT). Methods: In this retrospective study, CT scans acquired from a single institution from June 2012 to May 2022 were included. In total, 126 patients were assigned to a training cohort (n = 76), a validation cohort (n = 12), and a testing cohort (n = 38). We trained and developed a DCNN model based on positive scans with bone metastases and negative scans without bone metastases to detect and segment the bone metastases of lung cancer on CT. We evaluated the clinical efficacy of the DCNN model in an observer study with five board-certified radiologists and three junior radiologists. The receiver operator characteristic curve was used to assess the sensitivity and false positives of the detection performance; the intersection-over-union and dice coefficient were used to evaluate the segmentation performance of predicted lung cancer bone metastases. Results: The DCNN model achieved a detection sensitivity of 0.894, with 5.24 average false positives per case, and a segmentation dice coefficient of 0.856 in the testing cohort. Through the radiologists-DCNN model collaboration, the detection accuracy of the three junior radiologists improved from 0.617 to 0.879 and the sensitivity from 0.680 to 0.902. Furthermore, the mean interpretation time per case of the junior radiologists was reduced by 228 s (p = 0.045). Conclusions: The proposed DCNN model for automatic lung cancer bone metastases detection can improve diagnostic efficiency and reduce the diagnosis time and workload of junior radiologists.

Laminin α1 as a target for the treatment of epidural fibrosis by regulating fibrotic mechanisms.

Excessive proliferation and migration of fibroblasts in the lumbar laminectomy area can lead to epidural fibrosis, eventually resulting in failed back surgery syndrome. It has been reported that laminin α1, a significant biofunctional glycoprotein in the extracellular matrix, is involved in several fibrosis­related diseases, such as pulmonary, liver and keloid fibrosis. However, the underlying mechanism of laminin α1 in epidural fibrosis remains unknown. The present study aimed to explore the effect and mechanism of laminin α1 in fibroblast proliferation, apoptosis and migration, and epidural fibrosis. Following the establishment of a laminectomy model, hematoxylin and eosin, Masson's trichrome and immunohistochemical staining were performed to determine the degree of epidural fibrosis, the number of fibroblasts, collagen content and the epidural expression levels of laminin α1, respectively. Furthermore, a stable small interfering RNA system was used to knock down the expression of laminin α1 in fibroblasts. The transfection efficiency was confirmed by reverse transcription­quantitative PCR and immunofluorescence staining. Western blot analysis, scratch wound assay, EdU incorporation assay, flow cytometric analysis and Cell Counting Kit 8 assay were performed to assess the proliferation, apoptosis, migration and viability of fibroblasts, as well as the expression levels of the AKT/mechanistic target of rapamycin (mTOR) signaling­related proteins. In vivo experiments revealed that laminin α1 was positively and time­dependently associated with epidural fibrosis. In addition, laminin α1 knockdown attenuated cell proliferation, viability and migration, and promoted apoptosis. Furthermore, the results revealed that the activation of the AKT/mTOR signaling pathway was involved in the aforementioned processes. Overall, the current study illustrated the positive association between laminin α1 and epidural fibrosis, and also verified the effect of laminin α1 on fibroblast proliferation, apoptosis and migration. Furthermore, the results suggested that the AKT/mTOR signaling pathway may serve a significant role in regulating the behavior of laminin α1­induced fibroblasts.

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era.

Objective: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm. Methods: 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays. The performance of the AI algorithm such as accuracy, sensitivity, miss diagnosis rate, specificity, misdiagnosis rate, and time consumption was calculated and compared with that of orthopedic attending physicians. Results: Compared with orthopedic attending physicians, the Faster-RCNN algorithm performed better in accuracy (0.88 vs. 0.84 ± 0.04), specificity (0.87 vs. 0.71 ± 0.08), misdiagnosis rate (0.13 vs. 0.29 ± 0.08), and time consumption (5 min vs. 18.20 ± 1.92 min). As for the sensitivity and missed diagnosis rate, there was no statistical difference between the AI and orthopedic attending physicians (0.89 vs. 0.87 ± 0.03 and 0.11 vs. 0.13 ± 0.03). Conclusion: The AI diagnostic algorithm is an available and effective method for the clinical diagnosis of FIF. It could serve as a satisfying clinical assistant for orthopedic physicians.

Operative treatment outcomes of anterior sternoclavicular joint dislocation using two experimental methods - an acromioclavicular joint hook plate versus a locking plate: a retrospective study.

BACKGROUND: We aimed to compare the intraoperative and early postoperative clinical outcomes of using an acromioclavicular joint hook plate (AJHP) versus a locking plate (LP) in the treatment of anterior sternoclavicular joint dislocation. METHODS: Seventeen patients with anterior sternoclavicular joint dislocation were retrospectively analyzed from May 2014 to September 2019. Six patients were surgically treated with an AJHP, and 11 were surgically treated with an LP. Five male and one female patients composed the AJHP group, and nine male and two female patients composed the LP group. The mean age of all patients was 49.5 years. RESULTS: Reduction and fixation were performed with AJHP or LP in all 17 patients. The mean operative blood loss, operative time, and length of incision in the AJHP group were significantly better than those in the LP group. Shoulder girdle movement of the AJHP group was significantly better than that of the LP group. CONCLUSIONS: This study revealed that AJHP facilitated glenohumeral joint motion, reduced the risk of rupture of mediastinal structures, required a shorter incision, and had lesser blood loss and a shorter duration of operation compared with LP. However, some deficiencies require further improvement.

Applications of Mixed Reality Technology in Orthopedics Surgery: A Pilot Study.

Objective: The aim of this study is to explore the potential of mixed reality (MR) technology in the visualization of orthopedic surgery. Methods: The visualization system with MR technology is widely used in orthopedic surgery. The system is composed of a 3D imaging workstation, a cloud platform, and an MR space station. An intelligent segmentation algorithm is adopted on the 3D imaging workstation to create a 3D anatomical model with zooming and rotation effects. This model is then exploited for efficient 3D reconstruction of data for computerized tomography (CT) and magnetic resonance imaging (MRI). Additionally, the model can be uploaded to the cloud platform for physical parameter tuning, model positioning, rendering and high-dimensional display. Using Microsoft's HoloLens glasses in combination with the MR system, we project and view 3D holograms in real time under different clinical scenarios. After each procedure, nine surgeons completed a Likert-scale questionnaire on communication and understanding, spatial awareness and effectiveness of MR technology use. In addition to that, the National Aeronautics and Space Administration Task Load Index (NASA-TLX) is also used to evaluate the workload of MR hologram support. Results: 1) MR holograms can clearly show the 3D structures of bone fractures, which improves the understanding of different fracture types and the design of treatment plans; 2) Holograms with three-dimensional lifelike dynamic features provide an intuitive communication tool among doctors and also between doctors and patients; 3) During surgeries, a full lesion hologram can be obtained and blended in real time with a patient's virtual 3D digital model in order to give surgeons superior visual guidance through novel high-dimensional "perspectives" of the surgical area; 4) Hologram-based magnetic navigation improves the accuracy and safety of the screw placement in orthopaedics surgeries; 5) The combination of mixed reality cloud platform and telemedicine system based on 5G provides a new technology platform for telesurgery collaboration. Results of qualitative study encourage the usage of MR technology for orthopaedics surgery. Analysis of the Likert-scale questionnaire shows that MR adds significant value to understanding and communication, spatial awareness, learning and effectiveness. Based on the NASA TLX-scale questionnaire results, mixed reality scored significantly lower under the "mental," "temporal," "performance," and "frustration" categories compared to usual 2D. Conclusion: The integration of MR technology in orthopaedic surgery reduces the dependence on surgeons' experience and provides personalized 3D visualization models for accurate diagnosis and treatment of orthopaedic abnormalities. This integration is clearly one of the prominent future development directions in medical surgery.

Application and Development of Intelligent Medicine in Traditional Chinese Medicine.

As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What's more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.

Application of Artificial Intelligence in Medicine: An Overview.

Artificial intelligence (AI) is a new technical discipline that uses computer technology to research and develop the theory, method, technique, and application system for the simulation, extension, and expansion of human intelligence. With the assistance of new AI technology, the traditional medical environment has changed a lot. For example, a patient's diagnosis based on radiological, pathological, endoscopic, ultrasonographic, and biochemical examinations has been effectively promoted with a higher accuracy and a lower human workload. The medical treatments during the perioperative period, including the preoperative preparation, surgical period, and postoperative recovery period, have been significantly enhanced with better surgical effects. In addition, AI technology has also played a crucial role in medical drug production, medical management, and medical education, taking them into a new direction. The purpose of this review is to introduce the application of AI in medicine and to provide an outlook of future trends.

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians.

OBJECTIVE: To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of tibial plateau fractures (TPFs) and further measure its validity and feasibility. METHODS: A total of 542 X-rays of TPFs were collected as a reference database. An AI algorithm (RetinaNet) was trained to analyze and detect TPF on the X-rays. The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis. The algorithm performance was also compared with orthopedic physicians. RESULTS: The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF, which was similar to the performance of orthopedic physicians (0.92±0.03). The average time spent for analysis of the AI was 0.56 s, which was 16 times faster than human performance (8.44±3.26 s). CONCLUSION: The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF. It can be a useful assistant for orthopedic physicians, which largely promotes clinical workflow and further guarantees the health and security of patients.

Application of AI and IoT in Clinical Medicine: Summary and Challenges.

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare.

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.

Applications of Blockchain in the Medical Field: Narrative Review.

BACKGROUND: As a distributed technology, blockchain has attracted increasing attention from stakeholders in the medical industry. Although previous studies have analyzed blockchain applications from the perspectives of technology, business, or patient care, few studies have focused on actual use-case scenarios of blockchain in health care. In particular, the outbreak of COVID-19 has led to some new ideas for the application of blockchain in medical practice. OBJECTIVE: This paper aims to provide a systematic review of the current and projected uses of blockchain technology in health care, as well as directions for future research. In addition to the framework structure of blockchain and application scenarios, its integration with other emerging technologies in health care is discussed. METHODS: We searched databases such as PubMed, EMBASE, Scopus, IEEE, and Springer using a combination of terms related to blockchain and health care. Potentially relevant papers were then compared to determine their relevance and reviewed independently for inclusion. Through a literature review, we summarize the key medical scenarios using blockchain technology. RESULTS: We found a total of 1647 relevant studies, 60 of which were unique studies that were included in this review. These studies report a variety of uses for blockchain and their emphasis differs. According to the different technical characteristics and application scenarios of blockchain, we summarize some medical scenarios closely related to blockchain from the perspective of technical classification. Moreover, potential challenges are mentioned, including the confidentiality of privacy, the efficiency of the system, security issues, and regulatory policy. CONCLUSIONS: Blockchain technology can improve health care services in a decentralized, tamper-proof, transparent, and secure manner. With the development of this technology and its integration with other emerging technologies, blockchain has the potential to offer long-term benefits. Not only can it be a mechanism to secure electronic health records, but blockchain also provides a powerful tool that can empower users to control their own health data, enabling a foolproof health data history and establishing medical responsibility.

Mixed reality assists the fight against COVID-19.

Coronavirus disease 2019 (COVID-19) made a huge effect globally. With the assistance of mixed reality (MR) technology, complicated clinical works became easier to carry out and the condition had been greatly improved with high-tech advantages such as improved convenience, better understanding and communication, higher security, and medical resource saving. This study aimed to introduce one kind of MR application in the fight against COVID-19 and anticipate more feasible smart healthcare applications to enhance our strength for the final victory.

A 3D Hologram With Mixed Reality Techniques to Improve Understanding of Pulmonary Lesions Caused by COVID-19: Randomized Controlled Trial.

BACKGROUND: The COVID-19 outbreak has now become a pandemic and has had a serious adverse impact on global public health. The effect of COVID-19 on the lungs can be determined through 2D computed tomography (CT) imaging, which requires a high level of spatial imagination on the part of the medical provider. OBJECTIVE: The purpose of this study is to determine whether viewing a 3D hologram with mixed reality techniques can improve medical professionals' understanding of the pulmonary lesions caused by COVID-19. METHODS: The study involved 60 participants, including 20 radiologists, 20 surgeons, and 20 medical students. Each of the three groups was randomly divided into two groups, either the 2D CT group (n=30; mean age 29 years [range 19-38 years]; males=20) or the 3D holographic group (n=30; mean age 30 years [range 20=38 years]; males=20). The two groups completed the same task, which involved identifying lung lesions caused by COVID-19 for 6 cases using a 2D CT or 3D hologram. Finally, an independent radiology professor rated the participants' performance (out of 100). All participants in two groups completed a Likert scale questionnaire regarding the educational utility and efficiency of 3D holograms. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) was completed by all participants. RESULTS: The mean task score of the 3D hologram group (mean 91.98, SD 2.45) was significantly higher than that of the 2D CT group (mean 74.09, SD 7.59; P<.001). With the help of 3D holograms, surgeons and medical students achieved the same score as radiologists and made obvious progress in identifying pulmonary lesions caused by COVID-19. The Likert scale questionnaire results showed that the 3D hologram group had superior results compared to the 2D CT group (teaching: 2D CT group median 2, IQR 1-2 versus 3D group median 5, IQR 5-5; P<.001; understanding and communicating: 2D CT group median 1, IQR 1-1 versus 3D group median 5, IQR 5-5; P<.001; increasing interest: 2D CT group median 2, IQR 2-2 versus 3D group median 5, IQR 5-5; P<.001; lowering the learning curve: 2D CT group median 2, IQR 1-2 versus 3D group median 4, IQR 4-5; P<.001; spatial awareness: 2D CT group median 2, IQR 1-2 versus 3D group median 5, IQR 5-5; P<.001; learning: 2D CT group median 3, IQR 2-3 versus 3D group median 5, IQR 5-5; P<.001). The 3D group scored significantly lower than the 2D CT group for the "mental," "temporal," "performance," and "frustration" subscales on the NASA-TLX. CONCLUSIONS: A 3D hologram with mixed reality techniques can be used to help medical professionals, especially medical students and newly hired doctors, better identify pulmonary lesions caused by COVID-19. It can be used in medical education to improve spatial awareness, increase interest, improve understandability, and lower the learning curve. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100045845; http://www.chictr.org.cn/showprojen.aspx?proj=125761.