A non-invasive method for scoliosis assessment—A new mathematical concept using polar angle

Roy, Susmita; Grünwald, Alexander T.D.; Lampe, Renée

doi:10.1371/journal.pone.0275395

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…2). Roy et al 42 employed 3D scan images of human torsos to analyze 2D transverse cross-sections, quantifying scoliosis characteristics from the asymmetries, providing a faster, less expensive diagnosis method without radiation exposure. Furthermore, 3D body models facilitate skull shape assessment, aiding in detecting conditions such as craniosynostosis, which can cause abnormal skull bone fusion.…”

Section: Potential Of 3d Body Model-based Technologies For Infant Hea...mentioning

confidence: 99%

Emergence of 3D body model-based infant health evaluation: a review

Tang¹,

Lee²,

Xia³

et al. 2023

Preprint

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The accurate evaluation of infant health is paramount in ensuring optimal growth, development, and overall well-being. Traditional methods of evaluation, such as physical exams and laboratory tests, can be invasive and may not provide a detailed enough assessment of an infant's health or development. In recent years, the use of 3D body models has emerged as a promising approach for the evaluation of infant health. We examine the application of 3D body models in detecting body shape abnormalities, evaluating infant motor function using 3D body tracking technology, and utilizing facial recognition software to assess infant emotions. The benefits of these innovative approaches, including enhanced accuracy, non-invasive assessment, and cost-effectiveness, are highlighted. However, we also acknowledge the limitations of 3D body model-based evaluation, such as limited availability, potential for errors, and challenges in interpreting the data. Despite these challenges, the potential of 3D body models to transform infant health evaluations is significant. As the technology continues to evolve, its adoption in healthcare settings is expected to improve the accuracy and effectiveness of infant health assessments, ultimately contributing to better care and treatment for infants.

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Section: Potential Of 3d Body Model-based Technologies For Infant Hea...mentioning

confidence: 99%

Emergence of 3D body model-based infant health evaluation: a review

Tang¹,

Lee²,

Xia³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…There are studies on the golden parameter which is the Cobb Angle measurement, using AI methods performed by Vyas [11] and Sun [12] for detecting deformities. Research [13] on a new angle, a polar angle using non-invasive methods to assess scoliosis, also has been explored. Due to the recent development of these methods, there exists a scarcity of comprehensive reviews that concentrate on summarizing the assessment methods employed in diagnosing scoliosis deformities.…”

Section: Introductionmentioning

confidence: 99%

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.

show abstract

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

Amran,

Basaruddin,

Ijaz

et al. 2023

Preprint

View full text Add to dashboard Cite

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, lordosis, or kyphosis. 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 recent works on spinal deformity as-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.

show abstract

A non-invasive method for scoliosis assessment—A new mathematical concept using polar angle

Cited by 3 publications

References 31 publications

Emergence of 3D body model-based infant health evaluation: a review

Emergence of 3D body model-based infant health evaluation: a review

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

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

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