Scoliosis is characterized by a three-dimensional distortion of the spine, involving a lateral curvature exceeding 10 degrees with vertebral rotation within the curve. The condition can manifest as congenital, neuromuscular, or idiopathic. Idiopathic scoliosis is further categorized by the age of onset, including infantile (birth to two years), juvenile (three to nine years), and adolescent (10 years and older) stages. As the most prevalent pediatric musculoskeletal disorder, scoliosis induces a consistent three-dimensional spinal deformity, marked by axial rotation of the vertebrae rather than mere frontal plane displacement and rotation. Adolescent idiopathic scoliosis (AIS) is particularly common, evolving during periods of substantial physical growth. The diagnosis of idiopathic scoliosis is established when alternative causative factors, such as congenital anomalies or inflammatory processes, cannot be identified, thus excluding conditions like myotonia or myopathy resulting from primary or secondary motor neuron damage.

Surface Topography Imaging for Evaluating Spinal Deformities

Body surface topography (ST) utilizes photogrammetric techniques to reconstruct the shapes, sizes, and spatial relationships of objects through photograms. This method primarily focuses on imaging and analyzing the external contours of the torso, typically from the subject’s back. Widely applied for assessing trunk deformities, particularly in pediatric scoliosis cases, ST leverages the relationship between spinal curvature angles and surface deformities. Its notable advantages include non-invasiveness, safety, swift and accurate assessments of body posture in three spatial planes, computerized data storage, and its positive reception among school-age children and adolescents.

Radiography Methods for Scoliosis Detection

Radiography, commonly known as X-ray, plays a pivotal role in visualizing the spine, offering a comprehensive view through two projections: anterior-posterior (AP)/posterior-anterior (PA) and lateral (LAT). While X-rays were historically prevalent, ongoing efforts to minimize patient exposure have driven the adoption of optoelectronic methods for assessing posture and body statics. Despite technological advancements, X-rays remain essential for calculating torsion angles using the Cobb method and observing vertebral morphological changes. However, the drawback lies in the harmful radiation involved, leading to prolonged diagnosis times. Computer diagnostic methods, known for precision and non-invasiveness, are considered ideal, circumventing the adverse effects of X-rays. Notable computerized posture examination methods, such as Moiré bar, ISIS, Posturomet-S, Metrecom System, and Diers Formetric III 4D optoelectronic, hold practical significance by enabling early detection of curvature signs, offering a comprehensive view of the patient’s body in all planes, and identifying issues not visible to the naked eye.

Magnetic Resonance Imaging (MRI) Method for Scoliosis Detection

The utilization of magnetic resonance imaging (MRI) is increasingly expanding, particularly in the advancement of specialized methods and sequences. This non-invasive technique can be applied to virtually any part of the body through appropriately selected sequences.

In scoliosis diagnosis, MRI serves as a valuable tool, primarily focused on assessing neural structures and the configuration of the spinal canal. Its application extends to cases of atypical scoliosis patterns (e.g., left thoracic scoliosis), diagnosing congenital spinal curvature, and identifying nervous system defects in conjunction with neurologic disorders.

Computed Tomography (CT) for Scoliosis Detection

While 2D images persist in clinical research, the evolution of medical technology has given rise to a contemporary 3D technique, a crucial tool harnessed through computed tomography (CT) and magnetic resonance imaging (MRI). In CT examinations, clinicians must establish optimal parameters to effectively assess diseases or the extent of scoliosis. The optimization of parameters necessitates a profound understanding of their impact on results. Therefore, the implementation of specially developed quantitative methods for evaluating spinal curvatures becomes imperative, enhancing medical diagnoses, treatments, and the overall management of spinal disorders.

Augmenting the CT method with three-dimensional image processing presents opportunities for spatial imaging of the spine. This includes the identification of spinal canal deformities, detection of congenital spinal malformations, visualization of the location of spinal implants, and evaluation of the quality of spondylodesis. This comprehensive examination plays a pivotal role in guiding the selection of surgical techniques.

Artificial Intelligence (AI) for Scoliosis Detection

The utilization of Artificial Intelligence (AI) has proven to be a valuable approach for identifying scoliosis. By harnessing sophisticated algorithms and machine learning, AI systems meticulously analyze medical images, such as X-rays or other imaging modalities, to recognize and evaluate spinal deformities.

The integration of AI into scoliosis detection brings forth numerous benefits. Primarily, AI algorithms deliver swift and precise assessments, supporting healthcare professionals in early diagnosis and timely intervention—an imperative aspect for scoliosis, where early detection significantly influences treatment effectiveness.

Furthermore, AI-based methods diminish reliance on conventional techniques like manual measurements, enhancing operational efficiency while minimizing potential human errors. The technology facilitates automated and standardized evaluations, ensuring consistency across various medical practitioners and healthcare settings.

Moreover, AI systems exhibit adaptability and continuous improvement over time as they encounter more data, leading to the ongoing enhancement of diagnostic capabilities. This adaptability contributes to the refinement of scoliosis detection methods, ultimately enhancing the quality of patient care.

Technologies of Accurate Scoliosis Detection in Forethought

Intelligent Accurate Spine Angle Measurement: Our products can intelligently measure the Cobb angle of the spine to provide accurate scoliosis measurements.

Advanced Digital Twin Technology: The company’s products utilize digital twin technology to enable a highly accurate examination of the spine, providing more detailed and comprehensive spinal measurements.

Spine Whale for spine health monitoring & accurate scoliosis detection: Our Spine Whale monitors accurate scoliosis detection, including scoliosis assessment and physical therapy.

参考文献

• [1] Weinstein SL, Dolan LA, Cheng JC, et al. "Adolescent idiopathic scoliosis.".Lancet.2008;371(9623):1527-1537： 10.1016/S0140-6736(08)60658-3.
• [2] Negrini S, Donzelli S, Aulisa AG, et al. "2016 SOSORT guidelines：成長期の特発性側弯症の整形外科的治療とリハビリテーション治療".側弯症と脊椎疾患.2018;13:3： 10.1186/s13013-018-0175-8.
• [3] Kotwicki T, Negrini S, Grivas TB, et al. “Methodology of evaluation of scoliosis, back deformities and posture.” Scoliosis. 2009;4:26. doi: 10.1186/1748-7161-4-26.
• [4] Hresko MT.「臨床の実際。青年期の特発性側弯症".N Engl J Med.2013;368(9):834-841： 10.1056/NEJMcp1209063.
• [5] Bettany-Saltikov J, Weiss HR, Chockalingam N, et al. "Surgical versus non-surgical interventions in people with adolescent idiopathic scoliosis.".Cochrane Database Syst Rev. 2015;2015(4). doi： 10.1002/14651858.CD010663.pub2.
• [6] Glassman SD, Berven S, Kostuik J, et al. "Scoliosis Research Society Instrument Validation Study：特発性側弯症における手術成績の多施設評価".Spine.2005;30(6):699-702： 10.1097/01.brs.0000157447.56975.3e.
• [7] Cheung KM, Zhang YG, Lu DS, et al. “MRI for characterization of mild scoliosis in elderly people.” Spine. 2003;28(20):2313-2319. doi: 10.1097/01.BRS.0000084643.78221.7D.
• [8] Monticone M, Ambrosini E, Cazzaniga D, et al. “Active self-correction and task-oriented exercises reduce spinal deformity and improve quality of life in subjects with mild adolescent idiopathic scoliosis: Results of a randomized controlled trial.” Eur Spine J. 2016;25(10):3118-3127. doi: 10.1007/s00586-016-4625-4.
• [9] Schlosser TPC, van der Heijden GJMG, Versteeg AL, et al. “Scoliosis during pubertal growth: Spontaneous evolution and predictive factors.” European Spine Journal. 2014;23(12):2625-2631. doi: 10.1007/s00586-014-3594-8.
• [10] Sanders JO, Browne RH, McConnell SJ, et al. “Maturity assessment and curve progression in girls with idiopathic scoliosis.” Journal of Bone and Joint Surgery. 2007;89(1):64-73. doi: 10.2106/jbjs.f.00004.
• [11] Pialasse JP, Simoneau M, Lemay M, et al. “Postural stability and sensorimotor adaptations in adolescent idiopathic scoliosis.” Gait & Posture. 2016;50:40-45. doi: 10.1016/j.gaitpost.2016.08.016.
• [12] Trobisch P, Suess O, Schwab F. “Idiopathic scoliosis.” Dtsch Arztebl Int. 2010;107(49):875-883. doi: 10.3238/arztebl.2010.0875.
• [13] Negrini S, Hresko TM, O’Brien JP, et al. “Recommendations for research studies on treatment of idiopathic scoliosis: Consensus 2014 between SOSORT and SRS Non-Operative Management Committee.” Scoliosis and Spinal Disorders. 2015;10:8. doi: 10.1186/s13013-015-0032-4.
• [14] Ohrt-Nissen S, Dahl B, Gehrchen M. “Surgical treatment of adolescent idiopathic scoliosis: 2-year postoperative radiographic and clinical outcome in 125 consecutive patients.” European Spine Journal. 2016;25(10):3362-3368. doi: 10.1007/s00586-016-4541-7.
• [15] Schlosser TPC, van der Heijden GJMG, Versteeg AL, et al. "Scoliosis during pubertal growth：自然進化と予測因子".European Spine Journal.2014;23(12):2625-2631： 10.1007/s00586-014-3594-8.