Objective:To construct an artificial intelligence (AI)-based automatic grading model for neurological severity at acute phase of patients with traumatic cervical spinal cord injury (TCSCI) and evaluate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 315 patients with TCSCI admitted to the Second Affiliated Hospital of Naval Medical University from January 2019 to December 2023, including 243 males and 72 females, aged 30-75 years [(57.6±7.0)years]. Injured segments involved C 1-C 4 in 143 patients and C 5-C 8 in 172. According to the American Spinal Injury Association (ASIA) scale, the injuries were classified as Grade A in 15 patients, Grade B in 53, Grade C in 74, and Grade D in 173. The patients were randomly divided into training group ( n=252) and test group ( n=63) with a ratio of 8∶2. The patients′ sensory and motor functions were assessed according to the ASIA scale within 48 hours after injury. The cervical spine MRI instance segmentation model was used to extract injury severity features of TCSCI patients in sagittal T2-weighted images. The grading model consisted of a two-layer cascade network. The first layer involved gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier. In the training group, the 6 machine learning models were trained separately. In the second layer, the performance of the six models was optimized to obtain the corresponding optimal grading models, so as to match the models with the best grading performance for each feature. In the test group, the performance of each model was evaluated by calculating accuracy, recall, precision, average precision, and F1 score. Results:A total of 138 clinical and imaging features were included to construct an automatic grading model for neurological severity of TCSCI patients at acute phase, comprising 132 clinical neurological features (including 56 light touch sensory scores, 56 pinprick sensory scores, and 20 key muscle scores) and 6 MRI imaging features. In the test group, the accuracy, recall, precision, average precision and F1 score of the six models, including gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier in the first layer of the automatic grading model for neurological severity at acute phase of TCSCI patients, in the overall grading of light touch, pinprick sensory and key muscle motor function were all above 0.86. In terms of the overall light touch function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.90), gradient boosting (0.99), Gaussian naive bayes (0.98), random forest (0.96), and gradient boosting (0.96), respectively. In terms of the overall pinprick sensory function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were gradient boosting (0.98), Gaussian naive bayes (0.98), gradient boosting (0.99), decision tree (0.99), and gradient boosting (0.95), respectively. In terms of the overall key muscle motor function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.97), gradient boosting and support vector machine classifier (0.97), decision tree (0.95), random forest (0.95), and support vector machine classifier (0.96), respectively. In terms of sensory function, gradient boosting had the highest number of superior performances in the overall light touch and pinprick sensory function grading. In terms of motor function, the support vector machine classifier had the highest number of superior performances in the overall key muscle motor function grading.Conclusion:The automatic grading model for neurological severity at acute phase of patients with TCSCI that is constructed based on machine learning models and two-layer cascade networks can achieve the optimization of the grading performance of each feature and exhibit a strong grading ability for the sensory and motor function severity.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Thoracolumbar spine fracture often leads to severe pain, functional impairments, and neurological deficits, for which open reduction and internal fixation can effectively restore the spinal structural stability. Open decompression and reduction with internal fixation can help relieve spinal cord compression and improve spinal function in cases of concomitant cord injury. Although spinal stability can be restored through surgery, patients often face chronic pain and functional impairments postoperatively. A postoperative rehabilitation program is critical in optimizing therapeutic outcomes, reducing complications, and minimizing the risk of secondary injuries. However, current rehabilitation methods, such as physical therapy, functional training, and pain management, are confronted with problems in clinical practice, including significant variation in efficacy, poor patient adherence, and prolonged rehabilitation period. There is an urgent need for a unified rehabilitation strategy to address these problems. To this end, the Spinal Trauma Group of the Orthopedic Physicians Branch of the Chinese Medical Association and the Spine Health Professional Committee of the Chinese Human Health Technology Promotion Association organized experts from relevant fields to formulate Evidence-based guidelines for rehabilitation treatment after internal fixation of thoracolumbar spine fracture in adults ( version 2025) by integrating evidences from clinical researches and advanced rehabilitation concepts at home and abroad. A total number of 14 recommendations concerning the rehabilitation treatment with multimodal analgesia, psychological intervention, deep vein thrombosis prevention, core muscle and extremity exercise, appropriate use of braces, early weight-bearing, device-aided rehabilitation exercise, neuroregulatory therapy, rehabilitation team were put forward, aiming to standardize the post-operative rehabilitation process following internal fixation, promote the functional recovery, and enhance patients′ quality of life.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.

The diagnosis of osteoporosis is mainly characterized by reduced bone mineral density(BMD).Commonly used BMD examination methods are dual-energy X-ray absorptiometry and quantitative CT,but their distribution is not enough.Routine clinical CT can also be used for BMD assessment,which mainly includes vertebral body CT values and BMD values obtained based on asynchronous calibration and internal calibration technology,which is expected to achieve opportunistic osteoporosis screening and fracture risk prediction.This paper reviews the application of routine clinical CT in assessing BMD of osteoporosis patients,in order to help clinicians and scholars understand the current status and future research directions of opportunistic osteoporosis screening.

The diagnosis of osteoporosis is mainly characterized by reduced bone mineral density(BMD).Commonly used BMD examination methods are dual-energy X-ray absorptiometry and quantitative CT,but their distribution is not enough.Routine clinical CT can also be used for BMD assessment,which mainly includes vertebral body CT values and BMD values obtained based on asynchronous calibration and internal calibration technology,which is expected to achieve opportunistic osteoporosis screening and fracture risk prediction.This paper reviews the application of routine clinical CT in assessing BMD of osteoporosis patients,in order to help clinicians and scholars understand the current status and future research directions of opportunistic osteoporosis screening.

Objective:To construct an artificial intelligence (AI)-based automatic grading model for neurological severity at acute phase of patients with traumatic cervical spinal cord injury (TCSCI) and evaluate its efficacy.Methods:A retrospective cohort study was conducted to analyze the clinical data of 315 patients with TCSCI admitted to the Second Affiliated Hospital of Naval Medical University from January 2019 to December 2023, including 243 males and 72 females, aged 30-75 years [(57.6±7.0)years]. Injured segments involved C 1-C 4 in 143 patients and C 5-C 8 in 172. According to the American Spinal Injury Association (ASIA) scale, the injuries were classified as Grade A in 15 patients, Grade B in 53, Grade C in 74, and Grade D in 173. The patients were randomly divided into training group ( n=252) and test group ( n=63) with a ratio of 8∶2. The patients′ sensory and motor functions were assessed according to the ASIA scale within 48 hours after injury. The cervical spine MRI instance segmentation model was used to extract injury severity features of TCSCI patients in sagittal T2-weighted images. The grading model consisted of a two-layer cascade network. The first layer involved gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier. In the training group, the 6 machine learning models were trained separately. In the second layer, the performance of the six models was optimized to obtain the corresponding optimal grading models, so as to match the models with the best grading performance for each feature. In the test group, the performance of each model was evaluated by calculating accuracy, recall, precision, average precision, and F1 score. Results:A total of 138 clinical and imaging features were included to construct an automatic grading model for neurological severity of TCSCI patients at acute phase, comprising 132 clinical neurological features (including 56 light touch sensory scores, 56 pinprick sensory scores, and 20 key muscle scores) and 6 MRI imaging features. In the test group, the accuracy, recall, precision, average precision and F1 score of the six models, including gradient boosting, Gaussian naive bayes, K-nearest neighbors, decision tree, random forest and support vector machine classifier in the first layer of the automatic grading model for neurological severity at acute phase of TCSCI patients, in the overall grading of light touch, pinprick sensory and key muscle motor function were all above 0.86. In terms of the overall light touch function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.90), gradient boosting (0.99), Gaussian naive bayes (0.98), random forest (0.96), and gradient boosting (0.96), respectively. In terms of the overall pinprick sensory function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were gradient boosting (0.98), Gaussian naive bayes (0.98), gradient boosting (0.99), decision tree (0.99), and gradient boosting (0.95), respectively. In terms of the overall key muscle motor function grading performance, the models with the highest accuracy, recall, precision, average precision, and F1 score were K-nearest neighbors (0.97), gradient boosting and support vector machine classifier (0.97), decision tree (0.95), random forest (0.95), and support vector machine classifier (0.96), respectively. In terms of sensory function, gradient boosting had the highest number of superior performances in the overall light touch and pinprick sensory function grading. In terms of motor function, the support vector machine classifier had the highest number of superior performances in the overall key muscle motor function grading.Conclusion:The automatic grading model for neurological severity at acute phase of patients with TCSCI that is constructed based on machine learning models and two-layer cascade networks can achieve the optimization of the grading performance of each feature and exhibit a strong grading ability for the sensory and motor function severity.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

Thoracolumbar spine fracture often leads to severe pain, functional impairments, and neurological deficits, for which open reduction and internal fixation can effectively restore the spinal structural stability. Open decompression and reduction with internal fixation can help relieve spinal cord compression and improve spinal function in cases of concomitant cord injury. Although spinal stability can be restored through surgery, patients often face chronic pain and functional impairments postoperatively. A postoperative rehabilitation program is critical in optimizing therapeutic outcomes, reducing complications, and minimizing the risk of secondary injuries. However, current rehabilitation methods, such as physical therapy, functional training, and pain management, are confronted with problems in clinical practice, including significant variation in efficacy, poor patient adherence, and prolonged rehabilitation period. There is an urgent need for a unified rehabilitation strategy to address these problems. To this end, the Spinal Trauma Group of the Orthopedic Physicians Branch of the Chinese Medical Association and the Spine Health Professional Committee of the Chinese Human Health Technology Promotion Association organized experts from relevant fields to formulate Evidence-based guidelines for rehabilitation treatment after internal fixation of thoracolumbar spine fracture in adults ( version 2025) by integrating evidences from clinical researches and advanced rehabilitation concepts at home and abroad. A total number of 14 recommendations concerning the rehabilitation treatment with multimodal analgesia, psychological intervention, deep vein thrombosis prevention, core muscle and extremity exercise, appropriate use of braces, early weight-bearing, device-aided rehabilitation exercise, neuroregulatory therapy, rehabilitation team were put forward, aiming to standardize the post-operative rehabilitation process following internal fixation, promote the functional recovery, and enhance patients′ quality of life.

Acute symptomatic osteoporotic thoracolumbar compression fracture (ASOTLF) can lead to chronic low back pain, kyphosis deformity, pulmonary dysfunction, loss of mobility, and even life-threatening complications. Vertebral augmentation is currently the mainstream treatment method for this condition. In 2019, the Editorial Board of Chinese Journal of Trauma and the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association collaboratively led the development of Clinical guideline for vertebral augmentation for acute symptomatic osteoporotic thoracolumbar compression fractures. Six years later, with advances in clinical diagnosis and treatment techniques as well as accumulating evidence in related fields, the 2019 guideline requires updating. To this end, the Spinal Trauma Group of Orthopedic Surgeons Branch of Chinese Medical Doctor Association, the Spinal Health Professional Committee of China Human Health Science and Technology Promotion Association, and the Minimally Invasive Orthopedics Professional Committee of Shaanxi Medical Doctor Association have organized experts in the field to develop the Clinical guideline for vertebral augmentation of acute symptomatic osteoporotic thoracolumbar compression fractures ( version 2025) , based on the latest evidence-based medical researches. This guideline incorporates 3 recommendations retained from the 2019 version with updated strength of evidence, along with 12 new recommendations. It provides recommendations from six aspects of diagnosis, pain management, treatment option selection, prevention of postoperative complications, anti-osteoporosis therapy, and postoperative rehabilitation, aiming to provide a reference for standard treatment of vertebral augmentation for ASOTLF in hospitals at all levels.