A Survey of Methods and Technologies Used for Diagnosis of Scoliosis

Karpiel, Ilona; Ziębiński, Adam; Kluszczyński, Marek; Feige, Daniel

doi:10.3390/s21248410

Cited by 15 publications

(16 citation statements)

References 103 publications

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“…This study reviewed observational investigations (cross-sectional, case–control and cohort) along with those on the following diagnostic methods for IS: surface topography imaging and spinal deformity assessment, radiography-based imaging and evaluation of IS, MRI-based evaluation of IS, CT, Adam’s forward bend test, scoliometer measurements, individual Moiré topography, 23 the Watson-MacDonncha Posture Analysis (WMPA) and spinal mouse. The studies that investigated the prevalence of IS in child, adolescent and adult athletes (any type of sports; individual and team sports) were also reviewed.…”

Section: Methodsmentioning

confidence: 99%

Prevalence of idiopathic scoliosis in athletes: a systematic review and meta-analysis

Mousavi

Seidi

Minoonejad

et al. 2022

BMJ Open Sport Exerc Med

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ObjectiveThis study aimed to determine the prevalence of idiopathic scoliosis (IS) in child, adolescent and adult athletes of all sports activity levels.DesignSystematic review with meta-analysis.Data sourcesElectronic databases (PubMed, Scopus, ProQuest, Sage journals, ScienceDirect, Google Scholar and Springer) were systematically searched up from inception to 28 September 2021.Eligibility criteria for selecting studiesObservational investigations were included to evaluate the prevalence of IS in athletes (engaged in any type of individual and team sports). Congenital scoliosis, neuromuscular scoliosis, Scheuermann’s kyphosis and de novo scoliosis were not included. The risk of bias was assessed using the tool developed by Hoy et al.ResultsTwenty-two studies were included (N=57 470, range 15–46544, participants), thirteen studies were of high-quality. The estimated prevalence of IS in athletes was 27% (95% CI 20% to 35%, I2=98%), with a 95% prediction interval (1% to 69%). The prevalence of IS was significantly higher in female athletes (35%, 95% CI 27% to 34%, I2=98%). Ballet dancers showed a high IS prevalence (35%, 95% CI 24% to 47%, I2=98%). Recreational athletes showed a higher IS prevalence (33%, 95% CI 24% to 43%, I2=98%) than at competitive-level athletes (0.05%, 95% CI 0.03% to 0.08%, I2=98%), followed by elite (20%, 95% CI 13% to 27%, I2=98%).ConclusionsThe prevalence of IS in athletes was similar or higher to that as seen in other studies of the general population. IS prevalence may have a U-shaped relationship relative to level of competition. Further studies are required to determine which sports have the highest IS prevalence.

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Section: Methodsmentioning

confidence: 99%

Prevalence of idiopathic scoliosis in athletes: a systematic review and meta-analysis

Mousavi

Seidi

Minoonejad

et al. 2022

BMJ Open Sport Exerc Med

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“…The research landscape using machine learning (ML) and artificial intelligence (AI) followed a similar path to that of the medical literature by designing algorithms that can automatically extract the aforementioned biomechanical markers of disease from medical images [ 8 – 11 ], which can be utilized by the specialists for diagnosis. Furthermore, these parameters can be utilized as features for AI-based diagnosis by classifying images into healthy and different disease classes [ 1 , 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, Tan et al [ 22 ] used a combination of image processing techniques and U-net deep learning architecture to determine the location of vertebrae of interest and subsequently measure the Cobb angle. A wide range of approaches for Cobb angle measurement and scoliosis detection by Karpiel et al [ 8 ]. Classification techniques were also used to distinguish various scoliosis-related classes.…”

Section: Introductionmentioning

confidence: 99%

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images

et al. 2022

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Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects’ vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions.

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“…• In the literature, there is a great interest in using neural networks for scoliosis. In recent years, that interest has been in Convolutional Neural Networks (CNN) of Deep Learning [27,28]. Although competitive findings were reported with CNN usage, the literature still needs for improvement in terms of findings.…”

Section: Introductionmentioning

confidence: 99%

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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A Survey of Methods and Technologies Used for Diagnosis of Scoliosis

Cited by 15 publications

References 103 publications

Prevalence of idiopathic scoliosis in athletes: a systematic review and meta-analysis

Prevalence of idiopathic scoliosis in athletes: a systematic review and meta-analysis

Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images

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

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