A Novel Combination Technique: Three Points of Epiduroscopic Laser Neural Decompression and Percutaneous Laser Disc Decompression With the Ho:YAG Laser in an MSU Classification 3AB Herniated Disc

Beyaz, Serbülent Gökhan; Ülgen, Ali Metin; Kaya, Burak; İnanmaz, Mustafa Erkan; Ergönenç, Tolga; Eman, Ali; Doğan, Burcu

doi:10.1111/papr.12878

Cited by 3 publications

(5 citation statements)

References 19 publications

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“…A herniation size can be classified as 1‐2‐3. Several clinical studies have confirmed that in regard to selecting appropriate LDH patients who urgently require surgery, the MSU classification is more consistent with that of surgeons 17,18 . Patients with size‐2 or size‐3 lesions may account for a higher proportion of satisfactory surgical outcomes when using the MSU classification to guide surgical options 16 .…”

Section: Discussionmentioning

confidence: 86%

“…Several clinical studies have confirmed that in regard to selecting appropriate LDH patients who urgently require surgery, the MSU classification is more consistent with that of surgeons. 17 , 18 Patients with size‐2 or size‐3 lesions may account for a higher proportion of satisfactory surgical outcomes when using the MSU classification to guide surgical options. 16 To our knowledge, this is the first LDH diagnostic criterion closely linked with severity evaluation and corresponding clinical decision‐making.…”

Section: Discussionmentioning

confidence: 99%

“…Because the Michigan State University (MSU) classification is often considered an objective criterion for LDH surgical selection on lumbar axial MR images, 16 it has been widely applied in clinical practice. 17 , 18 To comprehensively describe LDH severity according to disc herniation size, this study aimed to develop an automated diagnostic model using the MSU classification. Thus, surgeons can accurately and rapidly make appropriate treatment options.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images

Zhang,

Chen,

et al. 2023

JOR Spine

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BackgroundThe severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time‐consuming and requires repetitive work. This study aims to develop and evaluate a deep learning‐based diagnostic model for automated LDH detection and classification on lumbar axial T2‐weighted MR images.MethodsA total of 1115 patients were analyzed in this retrospective study; both a development dataset (1015 patients, 15 249 images) and an external test dataset (100 patients, 1273 images) were utilized. According to the Michigan State University (MSU) classification criterion, experts labeled all images with consensus, and the final labeled results were regarded as the reference standard. The automated diagnostic model comprised Faster R‐CNN and ResNeXt101 as the detection and classification network, respectively. The deep learning‐based diagnostic performance was evaluated by calculating mean intersection over union (IoU), accuracy, precision, sensitivity, specificity, F1 score, the area under the receiver operating characteristics curve (AUC), and intraclass correlation coefficient (ICC) with 95% confidence intervals (CIs).ResultsHigh detection consistency was obtained in the internal test dataset (mean IoU = 0.82, precision = 98.4%, sensitivity = 99.4%) and external test dataset (mean IoU = 0.70, precision = 96.3%, sensitivity = 97.8%). Overall accuracy for LDH classification was 87.70% (95% CI: 86.59%–88.86%) and 74.23% (95% CI: 71.83%–76.75%) in the internal and external test datasets, respectively. For internal testing, the proposed model achieved a high agreement in classification (ICC = 0.87, 95% CI: 0.86–0.88, P < 0.001), which was higher than that of external testing (ICC = 0.79, 95% CI: 0.76–0.81, P < 0.001). The AUC for model classification was 0.965 (95% CI: 0.962–0.968) and 0.916 (95% CI: 0.908–0.925) in the internal and external test datasets, respectively.ConclusionsThe automated diagnostic model achieved high performance in detecting and classifying LDH and exhibited considerable consistency with experts' classification.

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Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images

Zhang,

Chen,

et al. 2023

JOR Spine

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“…( 7 ) suggest that patients with grade 1 should be excluded from surgical consideration, and patients with grade 2 or grade 3 should be considered for performing micro discectomies. This classification system is currently widely used in the clinical setting ( 29 , 30 ). In addition, the classification system of LCCS, a valuable method for clinicians and radiologists to use in clinical practice, is defined by the degree of separation of the cauda equina related to the severity of the clinical signs ( 9 , 31 ).…”

Section: Discussionmentioning

confidence: 99%

Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis

Liu²,

Yang³

et al. 2022

Front. Endocrinol.

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AimAccurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs.MethodsTotal 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model’s performance on the internal test dataset and external test datasets.ResultsA total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model.ConclusionThe multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

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“…Even though low back and radicular pain may usually be managed with conservative approaches, about 20% of patients develop chronic pain [28]. Minimally invasive procedures for treating low back and radicular pain have grown in popularity [29 ▪ ].…”

Section: Chronic Low Back Painmentioning

confidence: 99%

New ultrasound-guided techniques in chronic pain management: an update

Ergönenç

Stockman

2021

Current Opinion in Anaesthesiology

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Purpose of reviewThe introduction of ultrasound guidance in interventional pain management leads to the development of new interventional pain management techniques. Ultrasound-guided (UG) interventional pain management is rapidly developing and increasingly more commonly used, due to its many advantages over traditional radiologic imaging modalities. This review will summarize recent literature around novel interventional pain techniques with ultrasound guidance published over the last 18 months. Recent findingsMany new interventional pain management methods have been described in the last few years in the literature. The use of ultrasonography in interventional pain management played a crucial role in these developments. This review includes newly described interventional methods in the literature. The review particularly focussed on the methods that are used to treat the pain in the hip, shoulder, knee, and lumbar area. These new techniques hold promise for significant improvements in the efficacy and safety of interventional pain management.

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A Novel Combination Technique: Three Points of Epiduroscopic Laser Neural Decompression and Percutaneous Laser Disc Decompression With the Ho:YAG Laser in an MSU Classification 3AB Herniated Disc

Cited by 3 publications

References 19 publications

Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images

Deep learning‐based detection and classification of lumbar disc herniation on magnetic resonance images

Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis

New ultrasound-guided techniques in chronic pain management: an update

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