Using artificial intelligence algorithms to identify existing knowledge within the back pain literature

Kawchuk, Gregory N.; Guan, Rui; Keen, Christopher; Hauer, Bradley; Kondrak, Grzegorz

doi:10.1007/s00586-020-06447-y

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…In the meantime, it has been shown several times that, using modern techniques, predictions can also be made for these patients with increasing accuracy [2][3][4][5].…”

Section: Artificial Intelligence In Treatment Of Back Painmentioning

confidence: 99%

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Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Wirries¹,

Geiger²,

Hammad³

et al. 2021

Diagnostics

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Patients with back pain are common and present a challenge in everyday medical practice due to the multitude of possible causes and the individual effects of treatments. Predicting causes and therapy efficien cy with the help of artificial intelligence could improve and simplify the treatment. In an exemplary collective of 1000 conservatively treated back pain patients, it was investigated whether the prediction of therapy efficiency and the underlying diagnosis is possible by combining different artificial intelligence approaches. For this purpose, supervised and unsupervised artificial intelligence methods were analyzed and a methodology for combining the predictions was developed. Supervised AI is suitable for predicting therapy efficiency at the borderline of minimal clinical difference. Non-supervised AI can show patterns in the dataset. We can show that the identification of the underlying diagnostic groups only becomes possible through a combination of different AI approaches and the baseline data. The presented methodology for the combined application of artificial intelligence algorithms shows a transferable path to establish correlations in heterogeneous data sets when individual AI approaches only provide weak results.

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“…In the meantime, it has been shown several times that, using modern techniques, predictions can also be made for these patients with increasing accuracy [2][3][4][5].…”

Section: Artificial Intelligence In Treatment Of Back Painmentioning

confidence: 99%

“…In recent years, there have been increasingly promising efforts to improve medical care in the field of back pain and spinal therapy through the application of artificial Diagnostics 2021, 11,1934 2 of 10 intelligence [2][3][4][5][6]. Typically, two approaches are followed to establish an AI dealing with medical issues.…”

Section: Introductionmentioning

confidence: 99%

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Wirries¹,

Geiger²,

Hammad³

et al. 2021

Diagnostics

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AI Pathfinding Algorithm in 3D Game Development Strategy Optimization System

Fang

2023

Lecture Notes in Mechanical Engineering

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Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network

Trinh

Shao

Hsieh

et al. 2022

JCM

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Spondylolisthesis refers to the displacement of a vertebral body relative to the vertrabra below it, which can cause radicular symptoms, back pain or leg pain. It usually occurs in the lower lumbar spine, especially in women over the age of 60. The prevalence of spondylolisthesis is expected to rise as the global population ages, requiring prudent action to promptly identify it in clinical settings. The goal of this study was to develop a computer-aided diagnostic (CADx) algorithm, LumbarNet, and to evaluate the efficiency of this model in automatically detecting spondylolisthesis from lumbar X-ray images. Built upon U-Net, feature fusion module (FFM) and collaborating with (i) a P-grade, (ii) a piecewise slope detection (PSD) scheme, and (iii) a dynamic shift (DS), LumbarNet was able to analyze complex structural patterns on lumbar X-ray images, including true lateral, flexion, and extension lateral views. Our results showed that the model achieved a mean intersection over union (mIOU) value of 0.88 in vertebral region segmentation and an accuracy of 88.83% in vertebral slip detection. We conclude that LumbarNet outperformed U-Net, a commonly used method in medical image segmentation, and could serve as a reliable method to identify spondylolisthesis.

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Using artificial intelligence algorithms to identify existing knowledge within the back pain literature

Cited by 3 publications

References 7 publications

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

Combined Artificial Intelligence Approaches Analyzing 1000 Conservative Patients with Back Pain—A Methodological Pathway to Predicting Treatment Efficacy and Diagnostic Groups

AI Pathfinding Algorithm in 3D Game Development Strategy Optimization System

Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network

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