Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks.

【PURPOSE】: Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed manually on hospital workstations. The purpose of this study is to construct a system that extracts the lumbar nerve and generates tractography automatically using deep learning semantic segmentation. 【METHODS】: We acquired 839 axial diffusion weighted images (DWI) from the DTI data of 90 patients with degenerative lumbar disorders, and segmented the lumbar nerve roots using U-Net, a semantic segmentation model. Using five architectural models, the accuracy of the lumbar nerve root segmentation was evaluated using a Dice coefficient. We also created automatic scripts from three commercially available software tools, including MRICronGL for medical image viewing, Diffusion Toolkit for reconstruction of the DWI data, and Trackvis for the creation of the tractography, and compared the time required to create the tractography, and evaluated the quality of the automated tractography was evaluated. 【RESULTS】: Among the five models, the architectural model Resnet34 performed the best with a Dice = 0.780. The creation time for the automatic lumbar nerve tractography was 191 s, which was significantly shorter by 235 s than the manual time of 426 s (p < 0.05). Furthermore, the agreement between manual and automated tractography was 3.67 ± 1.53 (satisfactory). 【CONCLUSIONS】: Using deep learning semantic segmentation, we were able to construct a system that automatically extracted the lumbar nerve and generated lumbar nerve tractography. This technology makes it possible to analyze lumbar nerve DTI and create tractography automatically, and is expected to advance the clinical applications of DTI for the assessment of the lumbar nerve.

Outcome of intradiscal condoliase injection therapy for patients with recurrent lumbar disc herniation.

STUDY DESIGN: Retrospective cohort study. PURPOSE: This study aimed to compare data from patients who received intradiscal condoliase (chondroitin sulfate ABC endolyase) injection for primary lumbar disc herniation (LDH) and recurrent LDH. OVERVIEW OF LITERATURE: Chemonucleolysis with condoliase for LDH is a treatment with relatively good results and a high safety profile; however, few studies have reported recurrence after LDH surgery. METHODS: The study participants were 249 patients who underwent intradiscal condoliase injection for LDH at nine participating institutions, including 241 patients with initial LDH (group C) and eight with recurrent LDH (group R). Patient characteristics including age, sex, body mass index, disease duration, intervertebral LDH level, smoking history, and diabetes history were evaluated. Low back pain/leg pain Numerical Rating Scale (NRS) scores and the Oswestry Disability Index (ODI) were used to evaluate clinical symptoms before treatment and at 6 months and 1 year after treatment. RESULTS: Low back pain NRS scores (before treatment and at 6 months and 1 year after treatment, respectively) in group C (4.9 → 2.6 → 1.8) showed significant improvement until 1 year after treatment. Although a tendency for improvement was observed in group R (3.5 → 2.8 → 2.2), no significant difference was noted. Groups C (6.6 → 2.4 → 1.4) and R (7.0 → 3.1 → 3.2) showed significant improvement in the leg pain NRS scores after treatment. Group C (41.4 → 19.5 → 13.7) demonstrated significant improvement in the ODI up to 1 year after treatment; however, no significant difference was found in group R (35.7 → 31.7 → 26.4). CONCLUSIONS: Although intradiscal condoliase injection is less effective for LDH recurrence than for initial cases, it is useful for improving leg pain and can be considered a minimally invasive and safe treatment method.

Estimation of the Radiographic Parameters for Hallux Valgus From Photography of the Feet Using a Deep Convolutional Neural Network.

BACKGROUND: Hallux valgus (HV), also known as bunion deformity, is one of the most common forefoot deformities. Early diagnosis and proper evaluation of HV are important because timely management can improve symptoms and quality of life. Here, we propose a deep learning estimation for the radiographic measurement of HV based on a regression network where the input to the algorithm is digital photographs of the forefoot, and the radiographic measurement of HV is computed as output directly. The purpose of our study was to estimate the radiographic parameters of HV using deep learning, to classify the severity by grade, and to assess the agreement of the predicted measurement with the actual radiographic measurement. METHODS: There were 131 patients enrolled in this study. A total of 248 radiographs and 337 photographs of the feet were acquired. Radiographic parameters, including the HV angle (HVA), M1-M2 angle, and M1-M5 angle, were measured. We constructed a convolutional neural network using Xception and made the classification model into the regression model. Then, we fine-tuned the model using images of the feet and the radiographic parameters. The coefficient of determination (R2) and root mean squared error (RMSE), as well as Cohen's kappa coefficient, were calculated to evaluate the performance of the model. RESULTS: The radiographic parameters, including the HVA, M1-M2 angle, and M1-M5 angle, were predicted with a coefficient of determination (R2)=0.684, root mean squared error (RMSE)=7.91; R2=0.573, RMSE=3.29; R2=0.381, RMSE=5.80, respectively. CONCLUSION: The present study demonstrated that our model could predict the radiographic parameters of HV from photography. Moreover, the agreement between the expected and actual grade of HV was substantial. This study shows a potential application of a convolutional neural network for the screening of HV.

Facet Joint Bridging in Adolescent-Onset Adult Idiopathic Scoliosis with Thoracolumbar/Lumbar Curves.

OBJECTIVE: This study aims to comprehend the natural history of adolescent idiopathic scoliosis (AIS) patients and determine risk factors for facet joint bridging in adolescent-onset adult idiopathic scoliosis with thoracolumbar/lumbar (TL/L) curves. METHODS: We included 50 patients with residual AIS with TL/L curves (3 males, 47 females; age 41.5 ± 17.3 years, TL/L Cobb angle 59.4 ± 11.8°). They were >20 years old and diagnosed with AIS during their adolescence. Radiographic parameters were measured, and facet joint bridging was defined from axial computed tomography images. RESULTS: The sagittal vertical axis (SVA) significantly increased with age (r = 0.71, P < 0.01). Coronal Cobb angle of the TL/L curve, L4 tilt, C7 translation, lumbar lordosis (LL), pelvic incidence-LL, pelvic tilt, and thoracolumbar kyphosis were also correlated to age (P < 0.05). There were significant differences in age, SVA, pelvic incidence-LL, vertebral bridging, facet tropism, and apical vertebral rotation (AVR) between the facet joint bridging group (n = 10) and the non-facet joint bridging group (n = 40). In the multivariate logistic regression analysis, SVA, vertebral bridging, and AVR emerged as notable risk determinants for facet joint bridging. The threshold for facet joint bridging based on SVA was 2.1 cm (area under the curve: 0.801; sensitivity = 90%; specificity = 65%). CONCLUSIONS: This research revealed that large SVA, the presence of vertebral bridging, and large AVR are associated with facet joint bridging in adolescent-onset adult idiopathic scoliosis patients with TL/L curves. The cutoff value for facet joint bridging based on SVA was 2.1 cm.

Emergency Patient Triage Improvement through a Retrieval-Augmented Generation Enhanced Large-Scale Language Model.

OBJECTIVES: Emergency medical triage is crucial for prioritizing patient care in emergency situations, yet its effectiveness can vary significantly based on the experience and training of the personnel involved. This study aims to evaluate the efficacy of integrating Retrieval Augmented Generation (RAG) with Large Language Models (LLMs), specifically OpenAI's GPT models, to standardize triage procedures and reduce variability in emergency care. METHODS: We created 100 simulated triage scenarios based on modified cases from the Japanese National Examination for Emergency Medical Technicians. These scenarios were processed by the RAG-enhanced LLMs, and the models were given patient vital signs, symptoms, and observations from emergency medical services (EMS) teams as inputs. The primary outcome was the accuracy of triage classifications, which was used to compare the performance of the RAG-enhanced LLMs with that of emergency medical technicians and emergency physicians. Secondary outcomes included the rates of under-triage and over-triage. RESULTS: The Generative Pre-trained Transformer 3.5 (GPT-3.5) with RAG model achieved a correct triage rate of 70%, significantly outperforming Emergency Medical Technicians (EMTs) with 35% and 38% correct rates, and emergency physicians with 50% and 47% correct rates (p < 0.05). Additionally, this model demonstrated a substantial reduction in under-triage rates to 8%, compared with 33% for GPT-3.5 without RAG, and 39% for GPT-4 without RAG. CONCLUSIONS: The integration of RAG with LLMs shows promise in improving the accuracy and consistency of medical assessments in emergency settings. Further validation in diverse medical settings with broader datasets is necessary to confirm the effectiveness and adaptability of these technologies in live environments.

Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament.

STUDY DESIGN: A retrospective analysis. OBJECTIVE: This research sought to develop a predictive model for surgical outcomes in patients with cervical ossification of the posterior longitudinal ligament (OPLL) using deep learning and machine learning (ML) techniques. SUMMARY OF BACKGROUND DATA: Determining surgical outcomes assists surgeons in communicating prognosis to patients and setting their expectations. Deep learning and ML are computational models that identify patterns from large datasets and make predictions. METHODS: Of the 482 patients, 288 patients were included in the analysis. A minimal clinically important difference (MCID) was defined as gain in Japanese Orthopaedic Association (JOA) score of 2.5 points or more. The predictive model for MCID achievement at 1 year post-surgery was constructed using patient background, clinical symptoms, and preoperative imaging features (x-ray, CT, MRI) analyzed via LightGBM and deep learning with RadImagenet. RESULTS: The median preoperative JOA score was 11.0 (IQR: 9.0-12.0), which significantly improved to 14.0 (IQR: 12.0-15.0) at 1 year after surgery (P < 0.001, Wilcoxon signed-rank test). The average improvement rate of the JOA score was 44.7%, and 60.1% of patients achieved the MCID. Our model exhibited an area under the receiver operating characteristic curve of 0.81 and the accuracy of 71.9% in predicting MCID at 1 year. Preoperative JOA score and certain preoperative imaging features were identified as the most significant factors in the predictive models. CONCLUSION: A predictive ML and deep learning model for surgical outcomes in OPLL patients is feasible, suggesting promising applications in spinal surgery. LEVEL OF EVIDENCE: 4.

Conversion of T2-Weighted Magnetic Resonance Images of Cervical Spine Trauma to Short T1 Inversion Recovery (STIR) Images by Generative Adversarial Network.

INTRODUCTION: The short T1 inversion recovery (STIR) sequence is advantageous for visualizing ligamentous injuries, but the STIR sequence may be missing in some cases. The purpose of this study was to generate synthetic STIR images from MRI T2-weighted images (T2WI) of patients with cervical spine trauma using a generative adversarial network (GAN).  Methods: A total of 969 pairs of T2WI and STIR images were extracted from 79 patients with cervical spine trauma. The synthetic model was trained 100 times, and the performance of the model was evaluated with five-fold cross-validation.  Results: As for quantitative validation, the structural similarity score was 0.519±0.1 and the peak signal-to-noise ratio score was 19.37±1.9 dB. As for qualitative validation, the incorporation of synthetic STIR images generated by a GAN alongside T2WI substantially enhances sensitivity in the detection of interspinous ligament injuries, outperforming assessments reliant solely on T2WI. CONCLUSION: The GAN model can generate synthetic STIRs from T2 images of cervical spine trauma using image-to-image conversion techniques. The use of a combination of synthetic STIR images generated by a GAN and T2WI improves sensitivity in detecting interspinous ligament injuries compared to assessments that use only T2WI.

A novel simultaneous three-dimensional volumetric morphological imaging and T2-mapping method, multi-interleaved X-prepared turbo-spin echo with intuitive relaxometry provides more accurate quantification of cervical spinal nerves.

PURPOSE: MIXTURE is a simultaneous morphological and quantitative imaging sequence developed by Philips that provides high-resolution T2 maps from the imaged series. We aimed to compare the T2 maps of MIXTURE and SHINKEI-Quant (S-Q) in the cervical spine and to examine their usefulness in the functional diagnosis of cervical radiculopathy. METHODS: Seven healthy male volunteers (mean age: 31 ± 8.0 years) and one patient with cervical disc herniation (44 years old, male) underwent cervical spine magnetic resonance imaging (MRI), and T2-mapping of each was performed simultaneously using MIXTURE and S-Q in consecutive sequences in one imaging session. The standard deviation (SD) of the T2 relaxation times and T2 relaxation times of the bilateral C6 and C7 dorsal root ganglia (DRG) and C5/6 level cervical cord on the same slice in the 3D T2-map of the cervical spine coronal section were measured and compared between MIXTURE and S-Q. RESULTS: T2 relaxation times were significantly shorter in MIXTURE than in S-Q for all C6, C7 DRG, and C5/6 spinal cord measurements. The SD values of the T2 relaxation times were significantly lower for MIXTURE in the C5/6 spinal cord and C7 DRG. In cervical disc herniation, MRI showed multiple intervertebral compression lesions with spinal canal stenosis at C5/6 and disc herniation at C6/7. CONCLUSION: MIXTURE is useful for preoperative functional diagnosis. T2-mapping using MIXTURE can quantify cervical nerve roots more accurately than the S-Q method and is expected to be clinically applicable to cervical radiculopathy.

A Novel Evaluation for Vertebral Artery Course Using 3D Magnetic Resonance Imaging with Computed Tomography -like Bone Contrast and Magnetic Resonance Angiography: A Proof of Concept Study.

OBJECTIVE: Vertebral artery (VA) injury poses a significant risk in cervical spine surgery, necessitating accurate preoperative assessment. This study aims to introduce and validate a novel approach that combines the Fast field echo that resembles a computed tomography using restricted echo spacing (FRACTURE) sequence with Time of Flight (TOF) Magnetic Resonance Angiography (MRA) for comprehensive evaluation of VA courses in the cervical spine. MATERIALS AND METHODS: A total of eight healthy volunteers and two patients participated in this study. The FRACTURE sequence provided high-resolution bone images of the cervical spine, while TOF MRA offered non-invasive vascular imaging. Fusion images were created by merging FRACTURE and MRA modalities to simultaneously visualize cervical spine structures and VA courses. Board-certified orthopedic spine surgeons independently evaluated images to assess the visibility of anatomical characteristics of the VA course by Likert-scale. RESULTS: The FRACTURE-MRA fusion images effectively depicted the extraosseous course of the VA at the craniovertebral junction, the intraosseous course of the VA at the craniovertebral junction, the VA entrance level to the transverse foramen, and the side-to-side asymmetry of bilateral VAs. Additionally, clinical cases demonstrated the utility of the proposed technique in identifying anomalies and guiding surgical interventions. CONCLUSIONS: The integration of the FRACTURE sequence and TOF MRA presents a promising methodology for the precise evaluation of VA courses in the cervical spine. This approach improves preoperative planning for cervical spine surgery with detailed anatomy and is a valuable alternative to conventional methods without contrast agents.

Bone union-promoting effect of romosozumab in an ovariectomized rat posterolateral lumbar fusion model.

Spinal fixation surgery has been increasingly performed in patients with osteoporosis. Romosozumab, a drug that was introduced in Japan recently, is known to possibly promote bone healing. However, few studies have reported the therapeutic effects of romosozumab in clinical practice in Japan. Therefore, here, we investigated the effects of romosozumab dosage on bone fusion promotion using an ovariectomized rat spinal fusion model. Eight-week-old female Sprague-Dawley rats were matched by body weight and divided into three groups: 1.0 romosozumab (R) group (Evenity®, 25 mg/kg), 1/10R group (Evenity®, 2.5 mg/kg), and control (C) group (saline). Subcutaneous injections were administered twice a week for 8 weeks postoperatively. Computed tomography scans were performed every 2 weeks from the time of surgery till 8 weeks postoperatively. The mean fusion rates in terms of volume were significantly higher in the R groups [1/10R, 1.0R] than in the C group from 4 weeks postoperatively. The rate of increase was significantly higher in the 1.0R group from 4 weeks postoperatively and in the 1/10R group from 6 weeks postoperatively, than in the C group. The proportion of trabecular bone area was approximately 1.5 times higher in the R groups than in the C group. No significant differences were observed between the R groups. Our results suggest that romosozumab stimulates bone growth at the graft site, and similar effects were achieved at 1/10 of the standard dosage.

Short-Term Clinical and Radiographic Evaluation of Patients Treated With Expandable and Static Interbody Spacers Following Lumbar Lateral Interbody Fusion.

OBJECTIVE: The goal of this study was to evaluate, using computed tomography (CT) and magnetic resonance imaging (MRI), patients who underwent oblique lateral interbody fusion (OLIF) using either expandable or static interbody spacers. METHODS: Thirty-five patients with degenerative disc disease were surgically treated with one-level OLIF and were followed up for more than 6 months. The Static group consisted of 22 patients, and 13 patients were in the Expandable group. Intraoperative findings included operative time (min), blood loss (ml), and cage size. Low back pain, leg pain, and leg numbness were measured using the Japanese Orthopedic Association score, visual analogue score, and the Roland-Morris Disability Questionnaire. Radiologic evaluation using computed tomography (CT) and magnetic resonance imaging (MRI) allowed measurement of cage subsidence, cross-sectional area (CSA) of the dural sac, disc height, segmental lordosis, foraminal height, and foraminal CSA preoperatively and 6 months postoperatively. RESULTS: The Expandable group had significantly larger cage height and lordosis than the Static group (P < 0.05). The Expandable group also had greater dural sac area expansion and enlargement of the intervertebral foramen, as well as better correction of vertebral body slip (P < 0.05). Cage subsidence was significantly lower in the Expandable group (P < 0.05). JOA and VAS scores for leg numbness were significantly better in the Expandable group (P < 0.05). CONCLUSIONS: Compared with static spacers, expandable spacers significantly enlarged the dural sac area, corrected vertebral body slippage, expanded the intervertebral foramen, and achieved good indirect decompression while reducing cage subsidence, resulting in improvement in clinical symptoms.

External validation of a deep learning model for predicting bone mineral density on chest radiographs.

We developed a new model for predicting bone mineral density on chest radiographs and externally validated it using images captured at facilities other than the development environment. The model performed well and showed potential for clinical use. PURPOSE: In this study, we performed external validation (EV) of a developed deep learning model for predicting bone mineral density (BMD) of femoral neck on chest radiographs to verify the usefulness of this model in clinical practice. METHODS: This study included patients who visited any of the collaborating facilities from 2010 to 2020 and underwent chest radiography and dual-energy X-ray absorptiometry (DXA) at the femoral neck in the year before and after their visit. A total of 50,114 chest radiographs were obtained, and BMD was measured using DXA. We developed the model with 47,150 images from 17 facilities and performed EV with 2914 images from three other facilities (EV dataset). We trained the deep learning model via ensemble learning based on chest radiographs, age, and sex to predict BMD using regression. The outcomes were the correlation of the predicted BMD and measured BMD with diagnoses of osteoporosis and osteopenia using the T-score estimated from the predicted BMD. RESULTS: The mean BMD was 0.64±0.14 g/cm2 in the EV dataset. The BMD predicted by the model averaged 0.61±0.08 g/cm2, with a correlation coefficient of 0.68 (p<0.01) when compared with the BMD measured using DXA. The accuracy, sensitivity, and specificity of the model were 79.0%, 96.6%, and 34.1% for T-score < -1 and 79.7%, 77.1%, and 80.4% for T-score ≤ -2.5, respectively. CONCLUSION: Our model, which was externally validated using data obtained at facilities other than the development environment, predicted BMD of femoral neck on chest radiographs. The model performed well and showed potential for clinical use.

Machine learning algorithms for predicting Cobb angle beyond 25 degrees in female adolescent idiopathic scoliosis patients.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: To develop a machine learning (ML) model that predicts the progression of AIS using minimal radiographs and simple questionnaires during the first visit. SUMMARY OF BACKGROUND DATA: Several factors are associated with angle progression in patients with AIS. However, it is challenging to predict angular progression at the first visit. METHODS: Among female patients with AIS treated at a single institution from July 2011 to February 2023, 1119 cases were studied. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first and last visits. The last visit was defined differently based on treatment plans. For patients slated for surgery or bracing, the last visit occurred just before these interventions. For others, it was their final visit before turning 18 years. Angular progression was defined as a Cobb angle greater than 25 degrees for each of the proximal thoracic (PT), main thoracic (MT), and thoracolumbar/lumbar (TLL) curves at the last visit. ML algorithms were employed to develop individual binary classification models for each type of curve (PT, MT, and TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the area under the curve (AUC) and Recall scores. Feature importance was evaluated to understand the contribution of each feature to the model predictions. RESULTS: For PT, MT, and TLL progression, the top-performing models exhibit AUC values of 0.94, 0.89, and 0.84, and achieve recall rates of 0.90, 0.85, and 0.81. The most significant factors predicting progression varied for each curve: initial Cobb angle for PT, presence of menarche for MT, and Risser grade for TLL. CONCLUSIONS: This study introduces an ML-based model using simple data at the first visit to precisely predict angle progression in female patients with AIS.

Automated detection and classification of the rotator cuff tear on plain shoulder radiograph using deep learning.

BACKGROUND: The diagnosis of rotator cuff tears (RCTs) using radiographs alone is clinically challenging; thus, the utility of deep learning algorithms based on convolutional neural networks has been remarkable in the field of medical imaging recognition. We aimed to evaluate the diagnostic performance of artificial intelligence (a deep learning algorithm; a convolutional neural network) to detect and classify RCTs using shoulder radiographs, and compare its diagnostic performance with that of orthopedic surgeons. METHODS: A total of 1169 plain shoulder anteroposterior radiographs (1 image per shoulder) were included in the total dataset and divided into four groups: intact, small, medium, and large to massive tear groups. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the receiver operating curve were measured for the detection of RCTs through binary classification. The average accuracy, recall, precision, and F1-score were divided into four groups by cuff tear size for multiclass classification. RESULTS: The convolutional neural network demonstrated a high performance, with 92% sensitivity, 69% specificity, 86% accuracy, and an area under the receiver operating curve of 0.88 for the detection of RCTs. The average accuracy, recall, precision, and F1-score of the convolutional neural network for classification were 60%, 0.42, 0.49, and 0.45, respectively. The accuracy of the convolutional neural network for the detection and classification of RCTs was significantly better than that of orthopedic surgeons. CONCLUSION: The convolutional neural network demonstrated the diagnostic ability to detect and classify RCTs using plain shoulder radiographs, and the diagnostic performance exhibited equal to superior accuracy when compared with those of shoulder experts.

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis.

Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.

Posterior Decompression and Fusion for Cervical Spondylodiscitis due to Osteoradionecrosis after Head-and-Neck Cancer Radiotherapy: Two Case Reports.

Introduction: Cervical spondylodiscitis due to osteoradionecrosis (ORN) after head-and-neck cancer radiotherapy is a severe complication. However, there are few reports on the surgical treatment of this condition. Case Report: We report two cases of cervical spondylodiscitis due to ORN, which were successfully treated with posterior decompression and fusion. The first case was in a 73-year-old male patient with spondylodiscitis at C3-C5, due to ORN. A posterior fusion of the spine (C2-T1) was performed, and a biopsy was conducted at a site separate from the incision for fusion. The second case was in a 76-year-old female patient with spondylodiscitis due to C4-C7 ORN. Cervical posterior decompression and fusion (C2-Th2) were performed, and decompression (C5-6) was conducted through an incision separate from that for the fusion.An anterior approach was avoided in both cases because of radiation-induced tissue changes. For these two patients with cervical spondylodiscitis due to ORN, surgery resulted in an improvement of infection and neurological deficits by posterior spinal fusion, isolation from decompression or biopsy of the infected area, and antibiotic treatment. Conclusion: Posterior decompression and fusion are effective for spondylodiscitis in the cervical spine after head-and-neck radiotherapy, treating both infection and neurological deficits. Spinal fusion that avoids the level of the infected vertebral body and decompression from separate skin incision sites may prevent the spread of infection. An anterior approach should be avoided because the risk of esophageal perforation and posterior pharyngeal wall defects is high.

Association between Osteoporosis and Skeletal Muscle Mass in Men.

STUDY DESIGN: Cross-sectional study. PURPOSE: This cross-sectional study aimed to investigate the risk factors for osteoporosis in men by assessing bone mineral density (BMD), skeletal muscle mass, body fat mass, grip strength, and advanced glycation end products (AGEs). OVERVIEW OF LITERATURE: Fewer studies have reported the correlation between BMD and skeletal muscle mass in women. Moreover, a few studies have examined the relationship between osteoporosis and skeletal muscle mass. METHODS: This study included 99 men (mean age, 74.9 years; range, 28-93 years) who visited Qiball Clinic for BMD and body composition examinations. The osteoporosis group consisted of 24 patients (mean age, 72.5 years; range, 44-92 years), and the control group consisted of 75 individuals (mean age, 74.9 years; range, 28-93 years). Whole-body skeletal muscle mass was measured using a bioelectrical impedance analyzer. BMD was measured by dual X-ray absorptiometry. Skin autofluorescence (SAF), a marker of dermal AGE accumulation, was measured using a spectroscope. Osteoporosis was defined as a bone density T score of -2.5 or less. Physical findings, skeletal muscle mass, BMD, grip strength, and SAF were compared between the osteoporosis and control groups. RESULTS: The osteoporosis group had significantly lower trunk muscle mass (23.1 kg vs. 24.9 kg), lower leg muscle mass (14.4 kg vs. 13.0 kg), and skeletal mass index (7.1 kg/m2 vs. 6.7 kg/m2) than the control group (all p<0.05). Lower limb muscle mass was identified as a risk factor for osteoporosis in men (odds ratio, 0.64; p=0.03). CONCLUSIONS: Conservative treatment of osteoporosis in men will require an effective approach that facilitates the maintenance or strengthening of skeletal muscle mass, including exercise therapy with a focus on lower extremities and nutritional supplementation.

Quantitative evaluation of the lumbar ligamentum flavum using MRI T2-mapping: Efficacy of its clinical application in patients with lumbar spinal stenosis.

OBEJECTIVE: To perform a magnetic resonance imaging T2-mapping of the ligamentum flavum in healthy individuals and patients with lumbar spinal stenosis scheduled for surgery and compare the T2 relaxation times. SUBJECTS AND METHODS: The T2 relaxation time of the ligamentum flavum was compared among 3 groups, healthy young individuals (H group (age< 50)), healthy middle-aged and older individuals (H group (age≥50)), and patients with lumbar spinal stenosis (L group). Additionally, the thickness of the ligament was measured in the axial image plane, and the occupied area ratio of each fiber was measured by staining the surgically obtained ligament, and each was correlated with the T2 relaxation time. We also evaluated the adhesion of the ligamentum flavum with the dura mater during the surgery. RESULTS: The T2 relaxation times were significantly prolonged in H group (age ≥50) and L group (P < 0.001) compared to H group (age<50). The relationship between collagen fiber and T2 relaxation times was significantly positive (r = 0.720, P < 0.001). Moreover, the relaxation times were significantly prolonged in those with adhesion of the ligamentum flavum with the dura mater (P < 0.05). The cut-off for the relaxation time was 50 ms (sensitivity: 62.50%, false positive rate: 10.8%). CONCLUSION: Healthy middle-aged and older individuals and patients with lumbar spinal stenosis and adhesion of the ligamentum flavum with the dura mater have prolonged T2 relaxation times. Hence, the adhesion between the ligamentum flavum and dura mater should be considered in cases with a relaxation time ≥50 ms.

Mapping the Japanese orthopedic association national registry (JOANR) to the international classification of health interventions (ICHI).

BACKGROUND: The Japanese Orthopedic Association launched the Japanese Orthopedic Association National Registry (JOANR), Japan's first large-scale nationwide musculoskeletal disease registry, in 2020. The World Health Organization released the International Classification of Health Interventions (ICHI) Beta-3 version in the same year. This concurrence served as an impetus to examine the relationship between domestic and international classification for orthopedic interventions. Our objective was to evaluate the possibility of utilizing JOANR for international comparison and the potential usage of ICHI in the domestic medical fee reimbursement system. This study is a novel attempt at mapping a domestic orthopedic scheme to the ICHI. METHODS: We mapped 149 codes out of 581 orthopedic surgical codes, on JOANR's registration form, to the ICHI, and then classified the nature of JOANR codes' relationship, to both ICHI single stem codes and stem codes accompanied by other additional stem codes, extension codes, and International Classification of Diseases for Mortality and Morbidity Statistics (ICD) codes, into five categories: Equivalent (exact match), Narrower (compared to ICHI; can be smoothly incorporated into ICHI), Broader (compared to ICHI), Slipped (combination of both Narrower and Broader), and None (no appropriate code). Finally, debatable issues that arose during the mapping operation were noted. RESULTS: The domestic codes' relationship to ICHI single stem code by category were Equivalent: 27 (18.1%) and Narrower: 65 (43.6%), respectively. Further, the rate of Equivalent rose to 120 (80.5%) on adding other stem codes, extension codes, and ICD codes. Additionally, certain domestic titles, which were unsuitable for classification as they included diagnostic information, and arthroscopic surgeries without corresponding ICHI codes, were recoded. CONCLUSIONS: JOANR can be converted to an international comparison standard via ICHI to a certain extent, and ICHI accompanied by ICD codes has potential for deployment in the domestic medical fee reimbursement system.

Machine Learning Web Application for Predicting Functional Outcomes in Patients With Traumatic Spinal Cord Injury Following Inpatient Rehabilitation.

Accurately predicting functional outcomes in patients with spinal cord injury (SCI) helps clinicians set realistic functional recovery goals and improve the home environment after discharge. The present study aimed to develop and validate machine learning (ML) models to predict functional outcomes in patients with SCI and deploy the models within a web application. The study included data from the Japan Association of Rehabilitation Database from January 1, 1991, to December 31, 2015. Patients with SCI who were admitted to an SCI center or transferred to a participating post-acute rehabilitation hospital after receiving acute treatment were enrolled in this database. The primary outcome was functional ambulation at discharge from the rehabilitation hospital. The secondary outcome was the total motor Functional Independence Measure (FIM) score at discharge. We used binary classification models to predict whether functional ambulation was achieved, as well as regression models to predict total motor FIM scores at discharge. In the training dataset (70% random sample) using demographic characteristics and neurological and functional status as predictors, we built prediction performance matrices of multiple ML models and selected the best one for each outcome. We validated each model's predictive performance in the test dataset (the remaining 30%). Among the 4181 patients, 3827 were included in the prediction model for the total motor FIM score. The mean (standard deviation [SD]) age was 50.4 (18.7) years, and 3211 (83.9%) patients were male. There were 3122 patients included in the prediction model for functional ambulation. The CatBoost Classifier and regressor models showed the best performances in the training dataset. On the test dataset, the CatBoost Classifier had an area under the receiver operating characteristic curve of 0.8572 and an accuracy of 0.7769 for predicting functional ambulation. Likewise, the CatBoost Regressor performed well, with an R2 of 0.7859, a mean absolute error of 9.2957, and a root mean square error of 13.4846 for predicting the total motor FIM score. The final models were deployed in a web application to provide functional predictions. The application can be found at http://3.138.174.54:8501. In conclusion, our prediction models developed using ML successfully predicted functional outcomes in patients with SCI and were deployed in an open-access web application.