Imaging examination is one of the main methods to assess the degree of spinal degeneration, but the standardization of disease diagnosis and treatment is limited due to differences in imaging equipment and imaging scanning modalities in different medical institutions, complicated measurement data, and controversial description and grading of imaging results. The application of artificial intelligence(AI)to spinal degenerative disease imaging analysis can improve the consistency of disease diagnostic criteria, as well as enhance the diagnostic efficiency of physicians, benefiting both doctors and patients. In order to standardize the annotation and measurement of spinal degenerative disease image and to promote the better application of AI in clinical practice, with reference to the latest domestic and international literature, clinical research data and relevant industry requirements, experts have formulated a unified opinion on spinal degenerative disease imaging annotation data acquisition specifications, definitions, imaging performances, and measurement protocols and formulated this consensus, which can help improve the consistency of data annotation and measurement, and then establish an AI algorithm model with high accuracy, good versatility and strong generalization ability, providing a solid imaging basis for the standardized diagnosis and treatment of spinal degenerative diseases.

Robot-assisted TLIF(RA-TLIF)is an emerging surgical technique with higher accuracy than traditional fluoroscopy-traditional guided TLIF, which highlights great advantages especially in pedicle slenderness, anatomical variations and revision surgery. There is still some controversy about the key surgical techniques, complications and long-term follow-up of RA-TLIF, and some surgeons grasp of the indications and contraindications of the procedure is still unclear. In order to standardise the clinical application of RA-TLIF and to steadily promote the development of this technology, this consensus has been formulated on the basis of evidence-based medicine using the modified Delphi survey method after many discussions among spine experts across the country to provide a reference for the majority of colleagues.

With the development of artificial intelligence, the application of machine learning in the field of orthopedic biomaterials is also increasing, which has great potential. This paper first introduced the basic types of machine lear-ning, algorithm selection for different target scenarios and its evaluation index. Secondly, the key chemical and physical parameters in the design process of different orthopedic biomaterials, and the training data set of machine learning were analyzed. Then, the specific applications of machine learning in metal biological materials, bioceramics implant materials, polymer biological materials and new materials for bioprinting were discussed in detail, and the advantages of machine learning in predicting material properties, optimizing manufacturing processes, and studying biocompatibility were demonstrated through cases. Orthopedic biomaterials are developing in the direction of multidisciplinary integration and intelligence, and machine learning, as a key technology in this development trend, will more efficiently promote material development and clinical application. Finally, this paper analyzed the crucial impediments that hinder the further application of machine learning in clinical research, and looksed forward to the broad prospect of machine learning in the field of orthopedic biomaterials optimization design. In conclusion, machine learning provides new ideas and methods for the design and optimization of orthopedic biomaterials.

As a non-invasive method, gait analysis plays an important role in the diagnosis and treatment of orthopaedic diseases. By observing and analysing a patients walking style, gait analysis can reveal motor function problems such as changes in stride length, decreased stride speed and abnormal joint angles, all of which are early symptoms of cervical spondylotic myelopathy, lumbar spinal stenosis, osteoarthritis and other diseases. Traditional gait analysis methods typically require professionals to manually interpret the results from gait measurement devices. The integration of artificial intelligence(AI)technology with gait data has led to intelligent analysis methods that not only automate gait analysis but also significantly enhance the objectivity, consistency, and accuracy of the process. The application of intelligent analysis methods in orthopedic disease diagnosis and treatment can facilitate more efficient and accurate diagnoses and provide valuable insights for personalized rehabilitation plans through real-time monitoring of gait changes. However, challenges such as multimodal gait data fusion, the interpretability of AI models, and the portability and ease of use of gait measurement devices remain areas that require further research and development. These issues represent key directions for future studies. This paper primarily explored the research progress and existing challenges of intelligent gait analysis in assisting orthopedic diagnosis and treatment, with the aim of promoting the wider clinical application of gait analysis technology.

Carbon dot, as an emerging zero-dimensional photoluminescent nanomaterial, has broad application prospects in biomedicine, sensors, and optoelectronic devices due to their simple preparation, easy regulation of optical properties, high photochemical stability, excellent photoelectric properties, low toxicity, and good biocompatibility. With the research deepening, a variety of carbon dots have been successively reported to exhibit excellent enzyme-like catalytic activity(carbon dot nanozymes), and can be used to regulate the pathological redox microenvironment for efficient disease treatment. To elucidate the unique advantages and potential prospects of carbon dot nanozymes in neuroscience, in this review, we firstly introduced the antioxidant capacity and the activity source of carbon dots, and then discussed the antimicrobial activity and their corresponding action mechanisms. Finally, we summarized the recent progress of antioxidant carbon-dot nanozymes in regulating the neuro-regeneration microenvironment, and further proposed the prospect of carbon dot-based modulation of pathological microenvironment for nerve regeneration.

Objective To explore the construction of a time-sequential coating system on the surface of 3D-printed titanium alloy implants, which can sequentially deliver dual functions of anti-tumor and osteogenic promotion for the treatment and repair of bone defects following osteosarcoma surgery. Methods Porous Ti-6Al-4V samples were fabricated using selective laser melting(SLM), and the samples were first subjected to flow acid etching treatment(AE group). Subsequently, nano-hydroxyapatite(nHA)was mixed with agarose solution to form an internal hydrogel, which was then infiltrated into the porous structure through mechanical interlocking mechanisms(AN group). Subsequently, the AN group samples were then immersed in a calcium chloride solution to facilitate calcium ion adsorption through hydrogel swelling. Finally, a composite solution of sodium alginate, gelatin, and doxorubicin(DOX)(outer hydrogel)was applied to the surface of the AN group samples via a layer-by-layer crosslinking method(AG group). The surface morphology, roughness, wettability, corrosion resistance and mechanical properties of each group were characterized using scanning electron microscopy(SEM), three-dimensional confocal laser microscopy, contact angle measurement, electrochemical workstation and ME50 electronic universal testing machine. The swelling ratio and degradation rate of the inner and outer hydrogels were determined by the immersion method, and the in vitro release kinetics of DOX were evaluated. Rat bone marrow mesenchymal stem cells(BMSCs)were cultured on the surfaces of the AE and AN groups to evaluate their adhesion, proliferation, and differentiation capabilities. In order to evaluate the antitumor efficacy and toxicity of the samples, human osteosarcoma cells(HOS)and rat bone marrow mesenchymal stem cells were cultured on the surfaces of the AE and AG groups. Tumor-bearing models were established in the subcutaneous tissue of BALB/c mice. In these models, AE and AG implants were surgically positioned at the tumor tissue base. This approach was used to systematically evaluate the in vivo antitumor efficacy and biosafety profiles of the implants. Results SEM analysis revealed the presence of micron-scale pit structures on the surface of the AE group samples, exhibiting uniform distribution. In the AN group samples, agarose and nanohydroxyapatite were clearly observed to be present within the porous structure, forming a large-pore network on the scale of hundreds of micrometers. The AG group samples exhibited a surface with larger micropores, showing wrinkle-like features caused by the loading of DOX, while the interior exhibited a significant number of millimeter-scale porous structures. Surface roughness analysis revealed that the roughness of the AE and AN groups remained within the range of 4-5 μm, while the roughness of the AG group decreased to 2.180 μm. Contact angle measurements demonstrated that the loading of the inner hydrogel significantly enhanced the hydrophilicity of the AE group samples, whereas the loading of the outer hydrogel slightly reduced their hydrophilicity. Electrochemical tests demonstrated that the sequential loading of the double-layer hydrogel led to a substantial enhancement in the corrosion resistance of the samples. Swelling and degradation experiments revealed that the inner hydrogel exhibited a lower swelling ratio and slower degradation rate compared to the outer hydrogel. In vitro drug release kinetics studies indicated that DOX release exhibited an initial burst release profile, with sustained release lasting over 10 days. Cellular experiments demonstrated that, compared to the AE group, the AN group significantly promoted the adhesion, proliferation(P<0.001), and differentiation(alkaline phosphatase, bone morphogenetic protein 2 and osteopontin: P<0.01; osteocalcin: P<0.05)capabilities of rat bone marrow mesenchymal stem cells. In contrast, the AG group exhibited significant killing effects on human osteosarcoma cells(HOS)compared to the AE group(P<0.000 1), while showing only mild toxicity toward BMSCs. Tumor-bearing animal experiments confirmed that the AG group achieved effective antitumor efficacy while maintaining biosafety. Conclusion The double-layer hydrogel is methodically loaded into the 3D-printed porous Ti-6Al-4V implant, and it synergistically delivers dual functions of anti-tumor and osteogenic promotion based on a temporal sequence.

Objective By combining the complementary information from spinal CT and MR multimodal medical images, and utilizing detailed features of both bone and soft tissues, to improve the accuracy of identification and enhance the segmentation precision of spinal medical images by soft tissues, thereby providing a more comprehensive assessment of spinal lesions. Methods This paper proposed a multimodal medical image fusion network model for the fusion of spinal CT and MR images and a semi-supervised segmentation network model for the segmentation tasks based on the fused images. The multimodal fusion network retained the shared features of different modalities through a shared encoder, with a basic part extracting global features and a detail part focusing on local details. A dual-network architecture was employed to the segmentation network, which was corrected and constrained by a contrastive difference review module and a dynamic competitive pseudo-label generation module when the network was training. Results The proposed fusion network performed well in preserving image information and features, with less high-frequency noise in the fused images. The semi-supervised segmentation network excelled in both the Dice coefficient and Jaccard index, improving the clarity between spinal soft tissues and bone tissues. Conclusion The proposed multimodal medical image fusion network and semi-supervised segmentation network effectively enhance the fusion and segmentation accuracy of spinal images. The introduction of the contrastive difference review and dynamic competitive pseudo-label generation modules further improved the accuracy of the segmentation results, providing clearer and more reliable image information for the assessment of spinal diseases.

Objective To construct a high-precision and well-interpretable risk prediction model for chronic post-surgical pain(CPSP)after posterior lumbar interbody fusion(PLIF), so as to provide a reliable tool for the early identification of high-risk populations and precise prevention in clinical practice. Methods A retrospective study was conducted on 759 patients who underwent PLIF at our hospital from January 2019 to December 2023, including 375 males and 384 females. The patients were 33-80(55.28±9.94)years old. All cases were stratified and randomly divided into a training set(n=531)and a testing set(n=228)at a ratio of 7∶3. A total of 40 characteristic variables were collected from all patients before, during, and after surgery. After data pre-processing and LASSO regression feature screening, seven machine learning models were constructed, with area under the curve(AUC), F1 score, and other indicators as the core indicator to select the optimal model, and the Shapley additive explanations(SHAP)tool was used for interpretability analysis. Results Ten core predictive features of CPSP were ultimately selected, among which pain catastrophizing scale(PCS)score, preoperative surgical site pain, and complications were the three core driving factors(cumulative contribution: 48.21%). The Naive Bayes(AUC=0.914)and logistic regression(AUC=0.913)models exhibited excellent performance, and logistic regression had a more balanced overall performance(F1 score=0.685). The Naive Bayes specificity was as high as 0.958. SHAP analysis clarified the direction and magnitude of the influence of each feature on the prediction results, revealing the threshold effect of significantly increasing CPSP risk when the PCS score exceeds 30 points. Conclusion The CPSP risk prediction model based on machine learning has good discriminative power and clinical interpretability. The core predictive factors provide clear targets for personalized prevention and control strategies in clinical practice, which helps to promote the transformation of spinal surgery from “empirical medicine” to “data-driven precision prevention”.

Objective To evaluate the consistency of a new weight-bearing three-dimensional imaging(WR-3D)technology in measuring Cobb angles of adolescent idiopathic scoliosis(AIS)patients with traditional standing X-ray films. Methods From July to October, 2025, a total of 52 AIS patients have been enrolled at Qilu Hospital of Shandong University and underwent both standing full-spine X-ray and weight-bearing three-dimensional imaging examinations simultaneously. Pearson correlation analysis was used to assess the linear association between the two methods. Bland-Altman analysis was performed to calculate the mean bias and 95% limits of agreement, and the intraclass correlation coefficient(ICC)was used to evaluate the reliability. Paired t-test was used to test for systematic bias, and Cohens d was calculated to assess the effect size. Results There was a significant positive correlation between the measurements of WR-3D and traditional X-ray(r=0.977, P<0.001). Bland-Altman analysis showed a mean bias of -1.37°(95%CI: -7.32° to 4.58°), and the majority of data points(51/52)were within the limits of agreement. The ICC indicated excellent consistency(ICC=0.973, 95% CI: 0.954 to 0.985). Paired t-test suggested a statistically significant difference(P=0.002), but the effect size was moderate(Cohens d=0.450). Conclusion The WR-3D imaging system has excellent consistency with traditional X-ray in measuring Cobb angles of AIS patients, and the minor systematic bias has no significant clinical significance. This technique, combined with its three-dimensional, weight-bearing, and low-radiation advantages, provides a reliable new clinical tool for the precise assessment of scoliosis.

Objective To explore the application value of modified percutaneous intervertebral foraminal biopsy under C-arm guidance combined with metagenomics next-generation sequencing(mNGS)in the early diagnosis of lumbar disc infection. Methods A retrospective analysis was conducted on the clinical data of 179 patients suspected of having lumbar disc infection admitted to the Second Department of Orthopedics(Bone Infection), Shandong Public Health Clinical Center Affiliated to Shandong University from October 2020 to November 2023. All patients underwent puncture under C-arm guidance, with 90 cases employed the conventional percutaneous kyphoplasty puncture system(conventional group)and 89 cases utilized the improved percutaneous transforaminal biopsy system(improved group). Cultures for aerobic and anaerobic bacteria, acid-fast bacilli, and fungi, as well as routine drug susceptibility testing, mNGS, and histopathological examination were performed for each patient. The diagnostic sensitivity of puncture biopsy, operative time, fluoroscopy frequency, and incidence of immediate complications were compared between the two biopsy systems. Results There were no statistically significant differences between the two groups in baseline clinical characteristics such as gender, age, body mass index(BMI), proportion of patients with hypertension and diabetes, and preoperative C-reactive protein(CRP)and erythrocyte sedimentation rate(ESR)(P>0.05), indicating good comparability. However, there was a significant difference in the distribution of surgical segments(P=0.027). Regarding the main efficacy indicators, there were no statistically significant differences in the diagnostic sensitivity of puncture biopsy between the two groups(P>0.05), suggesting that the modified biopsy technique was comparable to the traditional technique in terms of pathogen detection efficacy. Safety analysis showed no statistically significant differences in the incidence of complications and operation time between the two groups(P>0.05); however, in terms of procedural characteristics, the modified group(8.04±0.94)overall, had slightly more fluoroscopy sessions than the traditional group(7.53±0.75)(P<0.001). Overall, the modified biopsy technique could achieve a level of efficacy comparable to the traditional method in early etiological diagnosis. Conclusion The improved percutaneous transforaminal biopsy combined with mNGS is a minimally invasive method for diagnosing early lumbar disc infection, characterized by high safety and a high diagnostic yield. It provides a robust and reliable optimized diagnostic pathway for clinical practice and holds significant clinical application value. Its effectiveness warrants further validation through studies with larger sample sizes and longer follow-up periods.

Objective To explore the application effectiveness of different generation modes of large language models(such as DeepSeek, ChatGPT, etc.)in the field of preoperative management and their value in assisting decision-making processes for junior physicians. Methods A total of 100 medical history records of orthopedic inpatients at Qilu Hospital of Shandong University were randomly selected from January to August 2025. Patients who were scheduled to undergo Grade I, II, III surgeries and non-joint replacement surgeries were excluded, resulting in the inclusion of total 87 patients. Guidelines related to perioperative management were retrieved from databases such as PubMed and UpToDate. After text processing and vectorization, these guidelines were used to build a perioperative management knowledge base, providing external knowledge support for subsequent model calls and question-answering tasks. The anonymized patient records were uploaded to different versions of the DeepSeek model ［DeepSeek Chat version(V3), DeepSeek Chat + knowledge base version, DeepSeek Deep Thinking version(R1), and DeepSeek R1 + knowledge base version］, and questions were posed under the identical “Instruction-Context-Input-Output(ICIO)” prompt framework. The model outputs were evaluated both objectively and subjectively. Results The DeepSeek R1 model achieved accuracy rates of 75.86% and 78.16% in the Revised Cardiac Risk Index(RCRI)scoring and risk classification tasks, respectively, significantly outperforming the Chat series models. All four model versions showed moderate accuracy in the American Society of Anesthesiologists(ASA)physical status classification and surgical feasibility judgment, with the R1 version performing slightly better. The introduction of the knowledge base slightly improved RCRI scoring accuracy only in the Chat version(+4.6%)but reduced performance in the R1 version. Subjective evaluation results indicated that junior physicians generally considered the R1 series models answers to be of greater clinical reference value, with an average score(4.19±0.72)significantly higher than that of the Chat series(Chat version: 3.06±0.06; Chat + knowledge base version: 2.97±0.03). This suggested that the R1 model has stronger practicality and acceptability in preoperative decision support(P<0.05). Conclusion The DeepSeek R1 model demonstrates good application potential in orthopedic preoperative anesthesia risk assessment and clinical decision support. However, knowledge base building and task adaptation require further optimization to enhance the models reliability and generalizability in real clinical scenarios.

Objective To compare the analgesic efficacy of liposomal bupivacaine with that of a traditional periarticular injection(TPAI)in patients undergoing total knee arthroplasty(TKA). Methods A total of 60 patients(28 males and 32 females, aged 53-83 years, with a mean age of 68.15 years)who were scheduled to undergo unilateral TKA between April and September 2024 were enrolled in this study. The patients were randomly assigned to either the liposomal bupivacaine group or the TPAI group. The primary outcome was pain intensity, which was assessed by the visual analogue scale(VAS)during activity and at rest at 24, 48, and 72 hours postoperatively. Secondary outcomes included the range of motion(ROM)at 72 hours, Western Ontario and McMaster Universities Osteoarthritis Index(WOMAC)score at discharge, the time to first use of patient-controlled analgesia(PCA), time to leg lift and ambulation, incidence of postoperative nausea and bleeding, and frequency of tramadol consumption. Results The resuts showed that the liposomal bupivacaine group had significantly lower pain scores during activity at 24(P=0.04), 48(P=0.02), and 72(P=0.02)hours postoperatively compared to the TPAI group. There were no statistically significant differences between the two groups in pain scores at rest, ROM, WOMAC scores, time to first PCA use, time to leg lift, time to ambulation, incidence of postoperative nausea or bleeding, or frequency of tramadol consumption(P>0.05). Conclusion Liposomal bupivacaine provides superior analgesia during activity compared to the TPAI after total knee arthroplasty(TKA), although it shows no significant advantage for pain at rest or on other secondary outcomes. These findings suggest its potential role in multimodal analgesia strategies for TKA.