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Trinh GM, Shao HC, Hsieh KLC, Lee CY, Liu HW, Lai CW, Chou SY, Tsai PI, Chen KJ, Chang FC, Wu MH, Huang TJ. Detection of Lumbar Spondylolisthesis from X-ray Images Using Deep Learning Network. J Clin Med 2022; 11:jcm11185450. [PMID: 36143096 PMCID: PMC9501139 DOI: 10.3390/jcm11185450] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022] Open
Abstract
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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Affiliation(s)
- Giam Minh Trinh
- International Graduate Program in Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Trauma-Orthopedics, College of Medicine, Pham Ngoc Thach Medical University, Ho Chi Minh City 700000, Vietnam
- Department of Pediatric Orthopedics, Hospital for Traumatology and Orthopedics, Ho Chi Minh City 700000, Vietnam
| | - Hao-Chiang Shao
- Institute of Data Science and Information Computing, National Chung Hsing University, Taichung City 402, Taiwan
| | - Kevin Li-Chun Hsieh
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Medical Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Research Center of Translational Imaging, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Ching-Yu Lee
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Orthopedics, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Hsiao-Wei Liu
- Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 30044, Taiwan
| | - Chen-Wei Lai
- Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 30044, Taiwan
| | - Sen-Yi Chou
- Center for Measurement Standards, Industrial Technology Research Institute, Hsinchu 30044, Taiwan
| | - Pei-I Tsai
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
| | - Kuan-Jen Chen
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
| | - Fang-Chieh Chang
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu 31057, Taiwan
| | - Meng-Huang Wu
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Orthopedics, Taipei Medical University Hospital, Taipei 11031, Taiwan
- TMU Biodesign Center, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence: (M.-H.W.); (T.-J.H.)
| | - Tsung-Jen Huang
- Department of Orthopedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Department of Orthopedics, Taipei Medical University Hospital, Taipei 11031, Taiwan
- Correspondence: (M.-H.W.); (T.-J.H.)
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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. Diagnostics (Basel) 2021; 11:diagnostics11111934. [PMID: 34829286 PMCID: PMC8619195 DOI: 10.3390/diagnostics11111934] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/08/2021] [Accepted: 10/14/2021] [Indexed: 11/17/2022] Open
Abstract
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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