New augmented reality remote for virtual guidance and education of fracture surgery: a retrospective, non-inferiority, multi-center 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. The authors aimed to evaluate 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 1 January 2018 and 31 March 2022, 800 patients who underwent fracture surgery were eligible for participation. The study enroled 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 six (3%) in the non-remote group developed complications. The 0.005 risk difference (95% CI: -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.

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.

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.

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.

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.

Down-regulation of insulin-like growth factor binding protein 5 is involved in intervertebral disc degeneration via the ERK signalling pathway.

It is obvious that epigenetic processes influence the evolution of intervertebral disc degeneration (IDD). However, its molecular mechanisms are poorly understood. Therefore, we tested the hypothesis that IGFBP5, a potential regulator of IDD, modulates IDD via the ERK signalling pathway. We showed that IGFBP5 mRNA was significantly down-regulated in degenerative nucleus pulposus (NP) tissues. IGFBP5 was shown to significantly promote NP cell proliferation and inhibit apoptosis in vitro, which was confirmed by MTT, flow cytometry and colony formation assays. Furthermore, IGFBP5 was shown to exert its effects by inhibiting the ERK signalling pathway. The effects induced by IGFBP5 overexpression on NP cells were similar to those induced by treatment with an ERK pathway inhibitor (PD98059). Moreover, qRT-PCR and Western blot analyses were performed to examine the levels of apoptosis-related factors, including Bax, caspase-3 and Bcl2. The silencing of IGFBP5 up-regulated the levels of Bax and caspase-3 and down-regulated the level of Bcl2, thereby contributing to the development of human IDD. Furthermore, these results were confirmed in vivo using an IDD rat model, which showed that the induction of Igfbp5 mRNA expression abrogated the effects of IGFBP5 silencing on intervertebral discs. Overall, our findings elucidate the role of IGFBP5 in the pathogenesis of IDD and provide a potential novel therapeutic target for IDD.