Diagnosis Of Lumbar Spinal Stenosis Research Articles

Predictive value of nerve root sedimentation sign in diagnosis of lumbar spinal stenosis

To explore the predictive value of the nerve root sedimentation sign in the diagnosis of lumbar spinal stenosis (LSS). Between January 2019 and July 2021, 201 patients with non-specific low back pain (NS-LBP) who met the selection criteria were retrospectively analyzed. There were 67 males and 134 females, with an age of 50-80 years (mean, 60.7 years). Four intervertebral spaces (L 1, 2, L 2, 3, L 3, 4, L 4, 5) of each case were studied, with a total of 804. The nerve root sedimentation sign was positive in 126 intervertebral spaces, and central canal stenosis was found in 203 intervertebral spaces. Progression to symptomatic LSS was determined by follow-up for lower extremity symptoms similar to LSS, combined with central spinal stenosis. Univariate analysis was performed for gender, age, visual analogue scale (VAS) score for low back pain at initial diagnosis, treatment, dural sac cross-sectional area at each intervertebral space, number of spinal stenosis segments, lumbar spinal stenosis grade, positive nerve root sedimentation sign, and number of positive segments between patients in the progression group and non-progression group, and logistic regression analysis was further performed to screen the risk factors for progression to symptomatic LSS in patients with NS-LBP. All patients were followed up 17-48 months, with an average of 32 months. Of 201 patients with NS-LBP, 35 progressed to symptomatic LSS. Among them, 33 cases also had central spinal stenosis, which was defined as NS-LBP progressing to symptomatic LSS (33 cases in progression group, 168 cases in non-progression group). Univariate analysis showed that CSA at each intervertebral space, the number of spinal stenosis segments, lumbar spinal stenosis grade, whether the nerve root sedimentation sign was positive, and the number of nerve root sedimentation sign positive segments were the influencing factors for the progression to symptomatic LSS ( P<0.05); and further logistic regression analysis showed that positive nerve root sedimentation sign increased the risk of progression of NS-LBP to symptomatic LSS ( OR=8.774, P<0.001). The nerve root sedimentation sign may be associated with the progression of NS-LBP to symptomatic LSS, and it has certain predictive value for the diagnosis of LSS.

Cauda Equina Syndrome: Cost Burden After Spinal Decompression.

Observational cohort study. Cauda equina syndrome (CES) is a rare neurologic condition with potentially devastating consequences. The objective of this study was to compare the 2-year postoperative cost-associated treatments after posterior spinal decompression between patients with and without CES. By analyzing a commercial insurance claims database, patients who underwent posterior spinal decompression with a concurrent diagnosis of lumbar spinal stenosis, radiculopathy, or disk herniation in 2017 were identified and included in the study. The primary outcome was the cost of payments for identified treatments in the 2-year period after surgery. Treatments included were (1) physical therapy (PT), (2) pain medication, (3) injections, (4) bladder management, (5) bowel management, (6) sexual dysfunction treatment, and (7) psychological treatment. In total, 3,140 patients (age, 55.3 ± 12.0 years; male, 62.2%) were included in the study. The average total cost of treatments identified was $2,996 ± 6,368 per patient. The overall cost of identified procedures was $2,969 ± 6,356 in non-CES patients, compared with $4,535 ± 6,898 in patients with CES ( P = 0.079). Among identified treatments, only PT and bladder management costs were significantly higher for patients with CES (PT: +115%, P < 0.001; bladder management: +697%, P < 0.001). The difference in overall cost was significant between patients (non-CES: $1,824 ± 3,667; CES: $3,022 ± 4,679; P = 0.020) in the first year. No difference was found in the second year. A short-term difference was observed in costs occurring in the first postoperative year. Cost of treatments was similar between patients apart from PT and bladder management.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. The mean DCS of the 3D U-Net were 0.905±0.080, 0.933±0.018, and 0.928±0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p=0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r=0.805). A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

Time Course of Asymptomatic Stenosis in Multiple Lumbar Spinal Stenosis-Five-Year Results of Selective Decompression of Symptomatic Levels.

Background: In the diagnosis of lumbar spinal stenosis (LSS), finding stenosis with magnetic resonance imaging (MRI) does not always correlate with symptoms such as sciatica or intermittent claudication. We perform decompression surgery only for cases where the levels diagnosed from neurological findings are symptomatic, even if multiple stenoses are observed on MRI. The objective of this study was to examine the time course of asymptomatic stenosis in patients with LSS after they underwent decompression surgery for symptomatic stenosis. Materials and Methods: The participants in this study comprised 137 LSS patients who underwent single-level L4-5 decompression surgery from 2003 to 2013. The dural sac cross-sectional area at the L3-4 disc level was calculated based on preoperative MRI. A cross-sectional area less than 50 mm2 was defined as stenosis. The patients were grouped, according to additional spinal stenosis at the L3-4 level, into a double group (16 cases) with L3-4 stenosis, and a single group (121 cases) without L3-4 stenosis. Incidences of new-onset symptoms originating from L3-4 and additional L3-4-level surgery were examined. Results: Five years after surgery, 98 cases (72%) completed follow-up. During follow-up, 2 of 12 patients in the double group (16.7%) and 9 of 86 patients in the single group (10.5%) presented with new-onset symptoms originating from L3-4, showing no significant difference between groups. Additional L3-4 surgery was performed for one patient (8.3%) in the double group and three patients (3.5%) in the single group; again, no significant difference was shown. Conclusion: Patients with asymptomatic L3-4 stenosis on preoperative MRI were not prone to develop new symptoms or need additional L3-4-level surgery within 5 years after surgery when compared to patients without preoperative L3-4 stenosis. These results indicate that prophylactic decompression for asymptomatic levels is unnecessary.

BUA-Net: Boundary and uncertainty-aware attention network for lumbar multi-region magnetic resonance imaging segmentation

Background and Objective:Multi-region segmentation by axial lumbar magnetic resonance imaging (MRI) is important for the diagnosis of lumbar spinal stenosis (LSS) and lumbar disc herniation (LDH) disease. Deep learning-based methods have been widely used in this field, but the segmentation task is still challenging due to intra-class grayscale unevenness, inter-class low contrast and unclear boundaries. Methods:To solve the above problems, we propose a novel boundary and uncertainty-aware attention network (BUA-Net). It is structured around a single encoder and two decoders, each dedicated to either multi-region or boundary segmentation. We use an uncertainty-aware attention (UA) module to extract regions with large segmentation uncertainty and generate an attention mask. The learning and supervisory capabilities of the model are enhanced by a fine seg (FS) module and an attention supervision loss (ASL) in the boundaries and blurred regions. Moreover, a multi-scale spatial channel attention (MSCA) module is proposed that refines the high-level features without increasing any layers. Results:The results of the BUA-Net experiments were tested against seven networks and showed a 0.26% improvement in Dice scores, a 0.86% improvement in precision, and a 0.2% improvement in recall. In metrics related to boundary segmentation effectiveness, our method nearly matches the state-of-the-art in the ASD metric, surpasses it by 2.32 in the 95HD metric, and leads by 2.04, 2.68, and 1.22 in the IVD, TS, and PE categories, respectively. Conclusions:The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods and shows excellent segmentation ability in the boundaries and blurred regions.

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9-91.8%) for internal validation. The AUROC of the MP-CNN model were 91.3% (95% CI 90.7-91.9%) and 79.5% (95% CI 78.2-80.8%) for the extra-internal and external validation, respectively. The MP-CNNbased heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.

Diabetes Mellitus and Poor Glycemic Control Are Associated With a Higher Risk of Lumbar Spinal Stenosis: An Analysis of a Large Nationwide Database.

A large-scale retrospective case-control study. Examine diabetes as a risk factor for lumbar spinal stenosis (LSS) development and evaluate the impact of diabetes duration, glycemic control, and associated complications on this risk. Diabetes mellitus, a multiorgan disorder impacting various connective tissues, induces histological changes in spinal structures, particularly the ligamentum flavum. While clinical studies suggest a higher incidence of LSS in diabetic patients, substantial epidemiological research on the likelihood of LSS diagnosis in individuals with diabetes is scarce. Using nationwide data, a total of 49,576 patients diagnosed with LSS based on International Classification of Diseases-10 codes were matched with controls of the same number based on age and sex. Employing a multivariable logistic regression model, the study assessed for the association between spinal stenosis and diabetes, while adjusting for confounders. We found a higher likelihood of LSS diagnosis in diabetic patients [odds ratio (OR) 1.39, 95% CI: 1.36 - 1.43, P <0.001]. Those with hemoglobin A1c ≥7% and ≥1 diabetes-related complication also had an elevated likelihood (OR: 1.19, 95% CI: 1.08-1.31, P =0.001). Prolonged diabetes exposure increased the risk. Diabetes diagnosis reduced median survival by around 4.5 years for both stenosis and nonstenosis patients; spinal stenosis diagnosis alone minimally impacted survival. Relative to individuals diagnosed with diabetes mellitus at the age of 65 or older, the OR for developing LSS were 1.22 (95% CI: 1.18-1.27, P <0.001) when DM was diagnosed at 50 to 65 years old and 1.67 (95% CI: 1.56-1.79, P <0.001) for those under 50 years old. Multivariate analysis revealed a significantly increased risk of all-cause mortality in patients with DM and spinal stenosis (hazard ratio: 1.36, 95% CI: 1.29-1.44, P <0.001) and those with DM without stenosis (hazard ratio: 1.49, 95% CI: 1.41-1.57, P <0.001) compared with controls. Diabetic patients with prolonged disease, poor glycemic control, and diabetes-related complications face an elevated risk of developing LSS. Recognizing the reciprocal adverse relationship between these conditions is crucial in clinical practice and designing public health measures for managing both conditions. 4.

An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire

ObjectivesSymptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiological characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. MethodsWe evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics compared. ResultsData from 4,827 patients (4,690 patients without LSS: mean age 62.44, range 27 – 84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30 – 71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91 and a Cohen’s kappa of 0.85. ConclusionsOur results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower healthcare costs.

Lumbar Spinal Stenosis Analysis with Deep Learning Based Decision Support Systems

Lumbar spinal stenosis (LSS) is a condition that affects the quality of life of the 3 vertebrae, the disc and the canal in the lower back. In this region, the nerves in the canal may be subjected to pressure for various reasons, and disease occurs. Surgical intervention is required to treat canal stenosis, and the exact location and size of the spinal stenosis is critical to the surgery. The UNet model, which is an example of this network, can be further deepened with various deep learning networks. In this study, it will be the basis for creating a system that helps in the diagnosis of spinal stenosis by using a deeper network. The ResUNET model using ResNet as the backbone achieved an average IoU of 0.987. This study demonstrated that expert decision support systems using MR images can be used in the diagnosis of LSS.

ChatGPT and its Role in the Decision-Making for the Diagnosis and Treatment of Lumbar Spinal Stenosis: A Comparative Analysis and Narrative Review.

Comparative Analysis and Narrative Review. To assess and compare ChatGPT's responses to the clinical questions and recommendations proposed by The 2011 North American Spine Society (NASS) Clinical Guideline for the Diagnosis and Treatment of Degenerative Lumbar Spinal Stenosis (LSS). We explore the advantages and disadvantages of ChatGPT's responses through an updated literature review on spinal stenosis. We prompted ChatGPT with questions from the NASS Evidence-based Clinical Guidelines for LSS and compared its generated responses with the recommendations provided by the guidelines. A review of the literature was performed via PubMed, OVID, and Cochrane on the diagnosis and treatment of lumbar spinal stenosis between January 2012 and April 2023. 14 questions proposed by the NASS guidelines for LSS were uploaded into ChatGPT and directly compared to the responses offered by NASS. Three questions were on the definition and history of LSS, one on diagnostic tests, seven on non-surgical interventions and three on surgical interventions. The review process found 40 articles that were selected for inclusion that helped corroborate or contradict the responses that were generated by ChatGPT. ChatGPT's responses were similar to findings in the current literature on LSS. These results demonstrate the potential for implementing ChatGPT into the spine surgeon's workplace as a means of supporting the decision-making process for LSS diagnosis and treatment. However, our narrative summary only provides a limited literature review and additional research is needed to standardize our findings as means of validating ChatGPT's use in the clinical space.

Longitudinal Comparative Analysis of Complications and Subsequent Interventions Following Stand-Alone Interspinous Spacers, Open Decompression, or Fusion for Lumbar Stenosis.

For individuals with lumbar spinal stenosis (LSS), minimally invasive procedures such as an interspinous spacer device without decompression or fusion (ISD) or open surgery (i.e., open decompression or fusion) may relieve symptoms and improve functions when patients fail to respond to conservative therapies. This research compares longitudinal postoperative outcomes and rates of subsequent interventions between LSS patients treated with ISD and those with open decompression or fusion as their first surgical intervention. This retrospective, comparative claims analysis identified patients age≥50years with LSS diagnosis and with a qualifying procedure during 2017-2021 in the Medicare database which includes healthcare encounters in inpatient and outpatient settings. Patients were followed from the qualifying procedure until end of data availability. The outcomes assessed during the follow-up included subsequent surgical interventions, including subsequent fusion and lumbar spine surgeries, long-term complications, and short-term life-threatening events. Additionally, the costs to Medicare during a 3-year follow-up were calculated. Cox proportional hazards, logistic regression, and generalized linear models were used to compare outcomes and costs, adjusted for baseline characteristics. A total of 400,685 patients who received a qualifying procedure were identified (mean age 71.5years, 50.7% male). Compared to ISD patients, patients receiving open surgery (i.e., decompression and/or fusion) were more likely to have a subsequent fusion [hazard ratio (HR), 95% confidence intervals (CI): 1.49 (1.17,1.89)-2.54 (2.00, 3.23)] or other lumbar spine surgery [HR (CI): 3.05 (2.18, 4.27)-5.72 (4.08,8.02)]. Short-term life-threatening events [odds ratio (CI): 2.42 (2.03, 2.88)-6.36 (5.33, 7.57)] and long-term complications [HR (CI): 1:31 (1.13, 1.52)-2.38 (2.05, 2.75)] were more likely among the open surgery cohorts. Adjusted mean index costs were lowest for decompression alone (US$7001) and highest for fusion alone ($33,868). ISD patients had significantly lower 1-year complication-related coststhan allsurgery cohorts and lower 3-year all-cause coststhan fusion cohorts. ISD resulted in lower risks of short- and long-term complications and lower long-term costs than open decompression and fusion surgeries as a first surgical intervention for LSS.

Diagnosis Value of Patient Evaluation Components Applicable in Primary Care Settings for the Diagnosis of Low Back Pain: A Scoping Review of Systematic Reviews.

Low back pain ranks as the leading cause of years lived with disability worldwide. Although best practice guidelines share a consistent diagnostic approach for the evaluation of patients with low back pain, confusion remains as to what extent patient history and physical examination findings can inform management strategies. The aim of this study was to summarize evidence investigating the diagnostic value of patient evaluation components applicable in primary care settings for the diagnosis of low back pain. To this end, peer-reviewed systematic reviews were searched in MEDLINE, CINAHL, PsycINFO and Cochrane databases from 1 January 2000 to 10 April 2023. Paired reviewers independently reviewed all citations and articles using a two-phase screening process and independently extracted the data. Of the 2077 articles identified, 27 met the inclusion criteria, focusing on the diagnosis of lumbar spinal stenosis, radicular syndrome, non- specific low back pain and specific low back pain. Most patient evaluation components lack diagnostic accuracy for the diagnosis of low back pain when considered in isolation. Further research is needed to develop evidence-based and standardized evaluation procedures, especially for primary care settings where evidence is still scarce.

Possibilities of MRI in the Diagnosis of Lumbar Vertebral Stenosis

This article highlights work devoted to the study of the possibility of MRI in the diagnosis of lumbar spinal stenosis.

Diagnostic value of a new axial loading MRI device in patients with suspected lumbar spinal stenosis.

This study was carried out to investigate a new device for axial loading MRI (alMRI) in the diagnosis of lumbar spinal stenosis (LSS). A total of 87 patients with suspected LSS sequentially underwent conventional MRI and alMRI using a new device with pneumatic shoulder-hip compression mode. Four quantitative parameters of dural sac cross-sectional area (DSCA), sagittal vertebral canal diameter (SVCD), disc height (DH), and ligamentum flavum thickness (LFT) at L3-4, L4-5, and L5-S1 in both examinations were measured and compared. Eight qualitative indicators were compared as valuable diagnostic information. Image quality, examinee comfort, test-retest repeatability, and observer reliability were also assessed. Using the new device, all 87 patients successfully completed alMRI with no statistically significant differences in image quality and examinee comfort from conventional MRI. Statistically significant changes were observed in DSCA, SVCD, DH, and LFT after loading (p < 0.01). SVCD, DH, LFT, and DSCA changes were all positively correlated (r = 0.80, 0.72, 0.37, p < 0.01). Eight qualitative indicators increased from 501 to 669 after axial loading, for a total increase of 168 (33.5%). Nineteen patients (21.8%, 19/87) developed absolute stenosis after axial loading and 10 of them (11.5%, 10/87) also had a significant reduction in DSCA (> 15 mm2). The test-retest repeatability and observer reliability were good to excellent. The new device is stable for performing alMRI and can exacerbate the severity of spinal stenosis, providing more valuable information for diagnosing LSS and reducing missed diagnoses. •The new axial loading MRI (alMRI) device could detect a higher frequency of patients with lumbar spinal stenosis (LSS). •The new device with pneumatic shoulder-hip compression mode was used to investigate its applicability in alMRI and diagnostic value for LSS. •The new device is stable for performing alMRI and can provide more valuable information for diagnosing LSS.

LSS-VGG16: Diagnosis of Lumbar Spinal Stenosis With Deep Learning.

This was a retrospective study. Lumbar Spinal Stenosis (LSS) is a disease that causes chronic low back pain and can often be confused with herniated disk. In this study, a deep learning-based classification model is proposed to make LSS diagnosis quickly and automatically with an objective tool. LSS is a disease that causes negative consequences such as low back pain, foot numbness, and pain. Diagnosis of this disease is difficult because it is confused with herniated disk and requires serious expertise. The shape and amount of this stenosis are very important in deciding the surgery and the surgical technique to be applied in these patients. When the spinal canal narrows, as a result of compression on these nerves and/or pressure on the vessels feeding the nerves, poor nutrition of the nerves causes loss of function and structure. Image processing techniques are applied in biomedical images such as MR and CT and high classification success is achieved. In this way, computer-aided diagnosis systems can be realized to help the specialist in the diagnosis of different diseases. To demonstrate the success of the proposed model, different deep learning methods and traditional machine learning techniques have been studied. The highest classification success was obtained in the VGG16 method, with 87.70%. The proposed LSS-VGG16 model reveals that a computer-aided diagnosis system can be created for the diagnosis of spinal canal stenosis. In addition, it was observed that higher classification success was achieved compared with similar studies in the literature. This shows that the proposed LSS-VGG16 model will be an important resource for scientists who will work in this field.

Another perspective on lumber spinal stenosis treatment: Should exercise be added to pre-surgical treatment?

Background/Aim: Lumbar spinal stenosis (LSS) is a disease that affects the quality of life of elderly individuals. Most patients undergoing surgery for lumbar spinal stenosis try physical therapy before opting to undergo surgery. The effect of pre-surgical exercise treatment is unclear. This study aimed to examine the effect of pre-surgical exercise treatment on functionality, quality of life, and balance. Methods: Patients between the ages of 40 and 70 who were scheduled for surgery based on a diagnosis of LSS were included in this cross-sectional study. The patients were randomly divided into two groups for which exercise therapy was added to the first group before the surgery, and the control group followed in the normal process. Visual Analogue Scale (VAS), Oswestry Disability Index (ODI), Beck Depression and Anxiety Inventory (BDI and BAI, respectively), Berg Balance Scale (BBS), static and dynamic balance measurements holding hands on and off on the balance device (SBHON, DBHON, DBHOFF) and the SF-36 quality of life scale tests were administered pre-operatively and eight weeks post-operatively, and the results were compared between the two groups that did and did not exercise before surgery. Results: Post-operative SBHON values were found to be significantly lower in the exercise group compared to the other group (P &lt; 0.001). While no differences between pre- and post-operative BBS, DBHON, and DBHOFF values in the non-exercising group were detected, a favorable significant difference in the exercising group was found (all P &lt; 0.001). Conclusion: The addition of pre-surgical exercise therapy to patients can lead to improvements in the success of surgery and contribute to the functionality of patients with LSS diagnosis.

LSS‐net: 3‐dimensional segmentation of the spinal canal for the diagnosis of lumbar spinal stenosis

AbstractLumbar Spinal Stenosis (LSS) is one of the main causes of chronic low back pain. Chronic low back pain not only reduces the quality of life of people but also can be an important expense item in the country's economy due to the inability of the person to participate in working life and treatment costs. As in other diseases, rapid diagnosis and early treatment of LSS significantly affect the quality of life of the person. Magnetic Resonance (MR) imaging is one of the methods used to diagnose LSS. Diagnosis by interpreting MR images requires serious expertise, and it has been frequently studied by academics in recent years because it is a system that assists the doctor with an objective approach. This field of study is machine learning, which we can call the sub‐branch of Artificial Intelligence. Deep learning‐based machine learning is very successful in processing biomedical images such as MR. In this study, a model that performs 3‐dimensional automatic segmentation on T2 sequence Lumbar MR Images is proposed for the diagnosis of LSS. This 3D LSS segmentation study, according to our knowledge, has the feature of being the first in its field and will be an important resource for those who work in this field. In addition, with the proposed model, parts that cannot be fully opened in LSS surgical operations, especially in the nerve roots, can be fully determined beforehand which will ensure that the patient's complaints are completely eliminated after the operation. In MR images, a total of 6 classes were created and segmentation was carried out, including the spinal disc, canal, thecal sac, posterior element, and other regions and background in the image, which are important for LSS. To measure the success of segmentation, the Intersection over Union (IoU) metric was calculated for each class. 3D segmentation success for the validation set in the dataset; Background (IoU = 0.83), Canal (IoU = 0.61), Disc (IoU = 0.91), Other (IoU = 0.97), Posterior element (IoU = 0.82), and Thecal Sac (IoU = 0.81). The 3D automatic segmentation success rates obtained are quite high and show that a Computer Aided Diagnosis system can be created in LSS diagnosis.

Variation of Torg-Pavlov ratio with age, gender, vertebral level, dural sac area, and ethnicity in lumbar magnetic resonance imaging

The objectives of the study were to provide normal values of the Torg-Pavlov ratio (TPR) of the lumbar spine in magnetic resonance imaging (MRI) for the Jordanian population and examine differences observed according to factors including age, gender, lumbar level, dural sac area, or ethnic group. Two hundred and eighteen lumbar MRIs from the Picture Archiving and Communication System were reviewed. These were collected from three main governmental hospitals, in North, Central, and South of Jordan. The mid-sagittal diameters of the vertebral body, spinal canal, and dural canal area were measured at all levels. Patients' gender and age were documented as well. Exclusion criteria were kyphoscoliosis alignments disorders, lumbar spinal canal compression regardless of the cause, vertebral bony disease (including fractures), and the presence of technical artifacts. Statistical analyses used descriptive and correlational methods. Comparisons were made between genders, age groups, lumbar level, dural sac area in the study population using independent t-test and one-way ANOVA tests, and between ethnicities by reviewing previous reports on subjects of different ethnicities. The mean TPR ratio for the study participants was 0.4502 ± 0.097. The value of TPR was widest in the 20-29-year-old group at all vertebral levels and in both males and females. Females had a significantly wider TPR than males (P = 0.003) in all age groups. TPR differed significantly between the five vertebral levels (P = 0.026). The difference in TPR between age groups was not statistically significant. TPR showed a positive significant correlation with dural sac (r = 0.203, P = 0.003). Comparison with the previous literature demonstrated variation in the TPR where Jordanian population had a lower TPR in comparison with Negros and Caucasoids whereas similar to Koreans, especially in females. There are significant differences in TPR according to gender, dural sac area, lumbar spinal level (except between L1 and L2), and ethnic group, but no significant difference with the age was found. The present study has identified normal values of Torg's ratio in the Jordanian population. Although the study may not be able to provide clear guidelines for use in clinical practice, it has still highlighted possible between countries variations and has identified differences in these values to different factors. Implications on clinical practice could be reflected on the diagnosis of lumbar spinal stenosis or on predicting the prognosis of lumbar spine injury.

120. The clinical and operative impact of lumbar epidural steroid injections prior to spine surgery for lumbar spinal stenosis

<h3>BACKGROUND CONTEXT</h3> Lumbar spinal stenosis is a common finding in the adult population. Lumbar epidural steroid injections (ESIs) are often used in management of this condition, with conflicting evidence regarding their efficacy. Previous research has suggested a negative impact of ESIs on the operative times and postoperative outcomes when ESIs are administered preoperatively prior to spine surgery in this population. Our retrospective study was performed to gain greater insight into the impact of preoperative ESIs on postoperative outcomes following spine surgery in management of lumbar stenosis. <h3>PURPOSE</h3> Our objective is to determine how preoperative ESIs impact postoperative outcomes following spine surgery in management of lumbar stenosis. <h3>STUDY DESIGN/SETTING</h3> Retrospective cohort involving patients who underwent surgical management of lumbar stenosis. Data for patients with preoperative ESI was compared to those without preoperative ESI administration. <h3>PATIENT SAMPLE</h3> Patient data for this retrospective cohort was retrieved solely from a patient database of a medical institution in north Texas, US. Patients were included in the study if they underwent surgical management of lumbar spinal stenosis between January 2016 and December 2020. The inclusion criteria required an active diagnosis of lumbar spinal stenosis. Patients who received an ESI within three months prior to the spine surgery were included in the "ESI" group, while those who did not receive a lumbar ESI within three months prior to the spine surgery were included in the "Non-ESI" group. <h3>OUTCOME MEASURES</h3> Primary outcome measures were ODI (Oswestry Disability Index) back, NDI (Neck Disability Index) back, VAS (Visual Analog Scale) leg pain, VAS back pain, PROMIS (Patient-Reported Outcomes Measurement Information System) pain, PROMIS pain interference, PROMIS satisfaction, PROMIS physical function at time of spine surgery (baseline) and at three months after surgery. <h3>METHODS</h3> This study was conducted at a medical institution in north Texas, US, utilizing the institution's spine center database. The institutional review board approved the standardized protocol. This study included a retrospective cohort. All results were collated on a Microsoft Excel file as retrieved from a patient registry of a medical institution in north Texas based on exclusion and inclusion criteria above. Statistical analysis was performed using IBM SPSS software, with t-test and ANOVA used to determine significance. P-value less than 0.05 was considered statistically significant. <h3>RESULTS</h3> Patients receiving an ESI in the preoperative period had shorter operative times (ESI 157 minutes vs Non-ESI 187 minutes, p=.10) during spine surgery than did patients in the Non-ESI group. Both ESI and Non-ESI groups showed significant improvements in VAS leg, VAS back, ODI and PROMIS pain scores at three months after surgery, with the ESI group showing a significantly larger improvement compared to the Non-ESI group in PROMIS pain scores. <h3>CONCLUSIONS</h3> Regardless of preoperative ESI administration, this study demonstrated improvement in pain and disability scores in the postoperative period following surgical management of lumbar stenosis. Patients who received ESI in the preoperative course had shorter operative times and greater improvements in PROMIS pain scores in the postoperative course compared to those who did not receive ESI. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Diagnostic accuracy of the lumbar spinal stenosis-diagnosis support tool and the lumbar spinal stenosis-self-administered, self-reported history questionnaire

Despite the applicability of the lumbar spinal stenosis (LSS)-diagnosis support tool (DST) and the LSS-self-administered, self-reported history questionnaire (SSHQ), their diagnostic accuracy has never been compared with that of the well-known North American Spine Society (NASS) clinical description of LSS. This study aimed to compare the diagnostic accuracy of the two diagnostic tools with that of the NASS guidelines' clinical description of LSS in a Japanese secondary care hospital setting. This multicenter cross-sectional study used data from the lumbar spinal stenosis diagnostic support tool (DISTO) project, which was conducted from December 1, 2011 to December 31, 2012. Japanese adults with low back pain (LBP) aged ≥20 years were consecutively included. The reference standard was LSS diagnosed by orthopedic physicians. The diagnostic accuracy of the two support tools was compared. Of 3,331 patients, 1,416 (42.5%) patients were diagnosed with LSS. The NASS clinical description of LSS had a sensitivity of 63.9% and specificity of 89.5%. The LSS-DST and LSS-SSHQ had sensitivities of 91.3% and 83.8% and specificities of 76.0% and 57.6%, respectively, with substantial improvements in sensitivity (P < 0.0001). Similar results were obtained when we limited included patients to those aged >60 years. These findings indicated that the LSS-DST and LSS-SSHQ were more sensitive in screening patients with LBP for a diagnosis of LSS than the NASS clinical description of LSS. This study strongly supports prioritizing the use of either of these two diagnostic support tools for screening.