Semi-automatic method for pre-surgery scoliosis classification on X-ray images using Bending Asymmetry Index

Yang, Deyou; Lee, T. T. Y.; Lai, Kelly Ka-Lee; Lam, Tai Pong; Castelein, René M.; Cheng, Jack Chun Yiu; Zheng, Yongping

doi:10.1007/s11548-022-02740-x

Cited by 6 publications

(11 citation statements)

References 26 publications

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“…The instrumentation or imaging modality is crucial in capturing the structure of the spine to obtain good image quality because it affects the accuracy of scoliosis diagnosis. Six out of eighteen studies used common and conventional imaging modalities, such as X-rays, computed tomography (CT), and ultrasounds [18][19][20]28,33,34], while other researchers [21][22][23][24][25][26][27][29][30][31]35] used uncommon instrumentation, like rasterstereography, cameras, EOS imaging, scanners, and 3D laser profilemeters. Special mention goes to Sikidar et al [32], as the study did not collect data as images that used electromyogram (EMG) and ground reaction force (GRF) data.…”

Section: Assessment Methods For Scoliosis Diagnosismentioning

confidence: 99%

“…Study [18] provided no evaluated parameter, as the study used image processing for the assessment, whereas the authors constructed new parameters or improvised from the current parameter to evaluate scoliosis. The new parameters used for scoliosis evaluation are the vertebrae as a landmark [22,24], rasterstereographic measurements [25], the center of laminae (COL) [28], the Scolioscan angle [29], a digital image-based postural assessment (DIPA) [31], the bending asymmetry index (BAI) [33], EMG and GRF [32] data, and a 3D scoliosis angle [34].…”

Section: Assessment Methods For Scoliosis Diagnosismentioning

confidence: 99%

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Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

Amran,

Basaruddin,

Ijaz

et al. 2023

Applied Sciences

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Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, hypo/hyperlordosis, or hypo/hyperkyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person’s diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review the recent works on spinal deformity assessment for scoliosis diagnosis utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022 using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

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Section: Assessment Methods For Scoliosis Diagnosismentioning

confidence: 99%

Section: Assessment Methods For Scoliosis Diagnosismentioning

confidence: 99%

Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

Amran,

Basaruddin,

Ijaz

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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“…When the literature is examined, it is seen that there are different studies in terms of diagnosing the scoliosis. Some of these studies are based on using traditional Machine Learning techniques or some computational algorithms [24][25][26][27][28][29][53][54][55].…”

Section: Deep Learning For Scoliosismentioning

confidence: 99%

“…As related to the scoliosis, such a system will be a nice tool to help both physiotherapists and patients in terms of appropriate diagnosis and treatment steps. In the literature, there is a recent interest to research decision support for scoliosis cases [24][25][26]. It is clear that developing software systems in decision support is a good opportunity for real-use cases.…”

Section: Introductionmentioning

confidence: 99%

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Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Goral

Köse

2022

IEEE Access

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Scoliosis is a disease caused by the spine curving. It is treatable but physiotherapists may do different measurements for curvature angles. That's a problem affecting the treatment planning. This study aims to develop a Deep Learning-based decision support system, which diagnoses scoliosis and plans treatments via Schroth method. The system has an interpretable and explainable CapsNet model processing x-ray image to detect 68-point vertebrae and make Cobb angle measurements. By using angle values and patient parameters, treatment is planned through an automated Schroth definition and the Fuzzy Logic. In the evaluations, the CapsNet had dominating findings (some of them are MSE: 0.0038, PCC: 0.93, Accuracy: 0.98). The Fuzzy Logic model was accurate at exercise plans for past cases. Also, physiotherapists and patients had positive feedback for the system usage, trustworthiness, diagnosis, treatment planning and tracking. As a conclusion, the system ensures advancements for automated diagnosis and treatment of scoliosis.

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Application of machine learning standardized integral area algorithm in measuring the scoliosis

Han,

Zhao,

Zhang

et al. 2023

Sci Rep

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This study was to develop a computer vision evaluation method to automatically measure the degree of scoliosis based on the machine learning algorithm. For the X-ray images of 204 patients with idiopathic scoliosis who underwent full-spine radiography, histogram equalization of original image was performed before a flipping method was used to magnify asymmetric elements, search for the global maximum pixel value in each line, and scan local maximal pixel value, with the intersection set of two point sets being regarded as candidate anchor points. All fine anchors were fitted with cubic spline algorithm to obtain the approximate curve of the spine, and the degree of scoliosis was measured by the standardized integral area. All measured data were analyzed. In manual measurement, the Cobb angle was 11.70–25.00 (20.15 ± 3.60), 25.20–44.70 (33.89 ± 5.41), and 45.10–49.40 (46.98 ± 1.25) in the mild, moderate and severe scoliosis group, respectively, whereas the value for the standardized integral area algorithm was 0.072–0.298 (0.185 ± 0.040), 0.100–0.399 (0.245 ± 0.050), and 0.246–0.901 (0.349 ± 0.181) in the mild, moderate and severe scoliosis group, respectively. Correlation analysis between the manual measurement of the Cobb angle and the evaluation of the standardized integral area algorithm demonstrated the Spearman correlation coefficient r = 0.643 (P < 0.001). There was a positive correlation between the manual measurement of the Cobb angle and the measurement of the standardized integral area value. Two methods had good consistency in evaluating the degree of scoliosis. ROC curve analysis of the standardized integral area algorithm to measure the degree of scoliosis showed he cutoff value of the standardized integral area algorithm was 0.20 for the moderate scoliosis with an AUC of 0.865, sensitivity 0.907, specificity 0.635, accuracy 0.779, positive prediction value 0.737 and negative prediction value 0.859, and the cutoff value of the standardized integral area algorithm was 0.40 for the severe scoliosis with an AUC of 0.873, sensitivity 0.188, specificity 1.00, accuracy 0.936, positive prediction value 1 and a negative prediction value 0.935. Using the standardized integral area as an independent variable and the Cobb angle as a dependent variable, a linear regression equation was established as Cobb angle = 13.36 + 70.54 × Standardized area, the model has statistical significance. In conclusion, the integrated area algorithm method of machine learning can quickly and efficiently assess the degree of scoliosis and is suitable for screening the degree of scoliosis in a large dataset as a useful supplement to the fine measurement of scoliosis Cobb angle.

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Semi-automatic method for pre-surgery scoliosis classification on X-ray images using Bending Asymmetry Index

Cited by 6 publications

References 26 publications

Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Application of machine learning standardized integral area algorithm in measuring the scoliosis

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