Background: The Cobb angle is critical in assessing adolescent idiopathic scoliosis (AIS) patients. This study aimed to evaluate the error in selecting the upper- and lower-end vertebrae on AIS digital X-rays by experienced and novice observers and its correlation with the error in measuring the Cobb angle and determining the length of the scoliotic curves. Methods: Using the TraumaMeter v.873 software, eight raters independently evaluated 68 scoliotic curves. Results: The error percentage in the upper-end vertebra selection was higher than for the lower-end vertebra (44.7%, CI95% 41.05-48.3 compared to 35%, CI95% 29.7-40.4). The mean bias error (MBE) was 0.45 (CI95% 0.38-0.52) for the upper-end vertebra and 0.35 (CI% 0.69-0.91) for the lower-end vertebra. The percentage of errors in the choice of the end vertebrae was lower for the experienced than for the novices. There was a positive correlation (r = 0.673, p = 0.000) between the error in selecting the end vertebrae and determining the length of the scoliotic curves. Conclusions: We can conclude that errors in selecting end vertebrae are common among experienced and novice observers, with a greater error frequency for the upper-end vertebrae. Contrary to the consensus, the accuracy of determining the length of the scoliotic curve is limited by the Cobb method\'s reliance on the correct selection of the end vertebrae.

To propose a deep learning framework \"SpineCurve-net\" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4 ± 24.5 years) and a validation set of 27 patients (average age 17.3 ± 5.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve. The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method\'s Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons\' annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively. This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.

Scoliosis often occurs in adolescents and seriously affects physical development and health. Traditionally, medical imaging is the most common means of evaluating the corrective effect of bracing during treatment. However, the imaging approach falls short in providing real-time feedback, and the optimal corrective force remains unclear, potentially slowing the patient\'s recovery progress. To tackle these challenges, an all-in-one integrated array of pressure sensors and sEMG electrodes based on hierarchical MXene/chitosan/polydimethylsiloxane (PDMS)/polyurethane sponge and MXene/polyimide (PI) is developed. Benefiting from the microstructured electrodes and the modulus enhancement of PDMS, the sensor demonstrates a high sensitivity of 444.3 kPa-1 and a broad linear detection range (up to 81.6 kPa). With the help of electrostatic attraction of chitosan and interface locking of PDMS, the pressure sensor achieves remarkable stability of over 100 000 cycles. Simultaneously, the sEMG electrodes offer exceptional stretchability and flexibility, functioning effectively at 60% strain, which ensures precise signal capture for various human motions. After integrating the developed all-in-one arrays into a commercial scoliosis brace, the system can accurately categorize human motion and predict Cobb angles aided by deep learning. This study provides real-time insights into brace effectiveness and patient progress, offering new ideas for improving the efficiency of scoliosis treatment.

Spinal deformities, including adolescent idiopathic scoliosis (AIS) and adult spinal deformity (ASD), affect many patients. The measurement of the Cobb angle on coronal radiographs is essential for their diagnosis and treatment planning. To enhance the precision of Cobb angle measurements for both AIS and ASD, we developed three distinct artificial intelligence (AI) algorithms: AIS/ASD-trained AI (trained with both AIS and ASD cases); AIS-trained AI (trained solely on AIS cases); ASD-trained AI (trained solely on ASD cases). We used 1612 whole-spine radiographs, including 1029 AIS and 583 ASD cases with variable postures, as teaching data. We measured the major and two minor curves. To assess the accuracy, we used 285 radiographs (159 AIS and 126 ASD) as a test set and calculated the mean absolute error (MAE) and intraclass correlation coefficient (ICC) between each AI algorithm and the average of manual measurements by four spine experts. The AIS/ASD-trained AI showed the highest accuracy among the three AI algorithms. This result suggested that learning across multiple diseases rather than disease-specific training may be an efficient AI learning method. The presented AI algorithm has the potential to reduce errors in Cobb angle measurements and improve the quality of clinical practice.

The goal of this study is to discuss the transitional nature of idiopathic scoliosis and the variation in treatment and management across the spectrum of age presentation.
This is a review article that discusses the evaluation, management, and classification of idiopathic scoliosis. The authors searched PubMed/MEDLINE, Google Scholar, and the Cochrane database for articles published up to April 2024. Keywords and MeSH terms relevant to the topic were used, including adolescent idiopathic scoliosis (AIS), adult idiopathic scoliosis (AdIS), adult degenerative scoliosis, young adult idiopathic scoliosis, early-onset scoliosis (EOS), classification, management, follow-up, outcomes, natural history, Cobb angle, and transitional care. Reference lists of selected articles were also searched to identify further articles. Inclusion criteria included English language articles that summarized any type of study design, including randomized controlled trials, observational studies, case-control/series, or metaanalysis, with study populations ranging from infants to > 50-year-old patients. Inter-reviewer disagreement on inclusion of particular articles was resolved through discussion. Related information was analyzed, and relevant concepts related to the transitional period dilemma have been discussed.
Each idiopathic scoliosis case needs independent assessment with regard to the age, degree of the curve, and patient-specific presentation. An accurate prediction of the curve progression by considering the patient\'s remaining growth potential is paramount to the treatment strategy. The classification system for EOS, AIS Lenke classification, AdIS classification, and the Scoliosis Research Society-Schwab classification are important for reliable communication between surgeons treating deformities. Untreated progressive idiopathic scoliosis warrants multidisciplinary management during the transition from EOS stage to AIS and then to AdIS. Also, surgical treatment of untreated AIS transitioning to AdIS is specific and nuanced. AdIS needs to be differentiated from adult degenerative scoliosis because the latter is associated with multiple comorbidities and anatomical differences.
Idiopathic scoliosis presents across the age spectrum with specific age-related decisions that transition into adulthood. Integrated models of both surgical and nonsurgical treatment of idiopathic scoliosis are warranted.

METHODS: Prospective Cohort Study.
OBJECTIVE: The present study aims to determine if the racial representation of patients enrolled in a large prospective scoliosis registry is reflective of the general United States population. Further, we studied whether there was an association between race, pre-operative parameters, outcomes and loss to follow-up.
METHODS: Prospectively collected data for patients who underwent spinal fusion for adolescent idiopathic scoliosis (AIS) was reviewed, including self-reported race/ethnicity. The U.S. pediatric population and U.S. patients enrolled in the prospective registry were compared. The data obtained was analyzed for variations between races, for pre-operative variables and follow-up.
RESULTS: Of the 2210 included patients in the registry 66% of patients reported as White, while 52% of the 2018 U.S. pediatric population reported as White. 15% of the registry reported as Hispanic/Latino compared to 22% of the U.S. pediatric population, 13% Black compared to 14% of the U.S. pediatric population, and 4% Asian compared to 5% of the U.S. pediatric population. Asian and White patients had statistically significant higher 2-year follow-up in all but one of six enrollment sites (P < 0.001). Native American, Other, and Hispanic/Latino patients had the highest BMIs. Native American and Black patients had the highest pre-op thoracic Cobb angles. Pre-op ages of Black, Hispanic, and Native American patients were statistically lower (P < 0.01).
CONCLUSIONS: This study demonstrates the association between race and patient follow-up and pre-operative factors in patients who underwent surgery for AIS. Black, Native American, and Hispanic populations were underrepresented both at pre-op and follow-up when compared to their relative proportion in the U.S. pediatric population.

OBJECTIVE: The study aims to establish the diagnostic accuracy of community spine x-rays for brace candidates.
METHODS: A review of adolescent idiopathic scoliosis patients seen for initial visit at a tertiary care pediatric hospital was conducted (n = 170). The index test was the pre-referral community spine x-ray interpreted by a community radiologist. Measures of diagnostic accuracy for the index test were determined against the reference standard if images were obtained within 90 days (n = 111). The reference standard was the 3-foot standing EOS spine x-ray evaluated by spine specialists. Diagnostic criterion for a brace candidate was dichotomized by Cobb angle range (25-40°) according to Scoliosis Research Society criteria. Risser stage was not included given significant missing data in index reports. To mitigate the uncertainty around true progression, sensitivity analyses were conducted on a sub-sample of data when index test was within 60 days of the reference standard (n = 67).
RESULTS: Accuracy of the community spine x-ray to detect a brace candidate was 65.8% (95% CI 56.2-74.5). Sensitivity of the index test was 65.4% with a false negative rate of 34.6%. Specificity was 66.1% with a false positive rate of 33.9%. Positive and negative predictive values were 63.0% and 68.4%, respectively. Of the total number of brace candidates (n = 52), 32.7% were missed because of underestimation in Cobb angle (95% CI 21.5-46.2). The proportion of missed brace candidates because of underestimation was unchanged with 60-day data (p = 0.37).
CONCLUSIONS: Inaccuracies in community spine radiology may lead to missed opportunities for non-operative treatment.

UNASSIGNED: Age-related postural hyper-kyphosis is an exaggerated anterior curvature of the thoracic spine, that impairs balance and increases the risk of falls and fractures in elderly subjects. Our objectives are to review the effect of elderly-specific spinal orthoses on muscle function and kyphosis angle in this subjects.
UNASSIGNED: We searched PubMed, Scopus, ISI web of Knowledge, ProQuest and Cochrane library to identify relevant studies that assessed efficacy of spinal orthoses on muscle function and kyphosis angle of elderly subjects with elderly with hyper-kyphosis. Quality assessment was implemented using the Downs and Black scale.
UNASSIGNED: Results for 709 individuals were described in 18 articles which 12 studies involved RCT. There was significant difference for kyphosis angle after use of orthosis of 148 participants (SMD: -3.79, 95% CI -7.02 to -0.56, p < 0.01). Except one study, all of studies showed significantly increased on the back muscle strength when the participants wore the spinal orthosis and this effect was significantly better in long-term follow up (MD: 84.73; 95% CIs, 23.24 to 146.23; p < 0.01). In the outcome of pain, the efficacy brought by orthosis was large and significant (SMD: -1.66; 95% CIs, -2.39 to 0.94; p < 0.01).
UNASSIGNED: Spinal orthosis may be an effective treatment for elderly hyper-kyphosis. However, the small number, and heterogeneity of the included studies, indicate that higher-quality studies should be conducted to verify the effectiveness and orthosis in hyper-kyphosis.
Age-related postural hyper kyphosis is an exaggerated anterior curvature of the thoracic spine, that impairs balance and increases the risk of falls and fractures in elderly subjects.Based on the findings of this review, elderly specific spinal orthoses may be recommended as effective device for elderly hyper kyphotic subjects.Spinal orthoses prescription is important for health practitioners to consider when planning treatment.

OBJECTIVE: Ultrasonography for scoliosis is a novel imaging method that does not expose children with adolescent idiopathic scoliosis (AIS) to radiation. A single ultrasound scan provides 3D spinal views directly. However, measuring ultrasonograph parameters is challenging, time-consuming, and requires considerable training. This study aimed to validate a machine learning method to measure the coronal curve angle on ultrasonographs automatically.
METHODS: A total of 144 3D spinal ultrasonographs were extracted to train and validate a machine learning model. Among the 144 images, 70 were used for training, and 74 consisted of 144 curves for testing. Automatic coronal curve angle measurements were validated by comparing them with manual measurements performed by an experienced rater. The inter-method intraclass correlation coefficient (ICC2,1), standard error of measurement (SEM), and percentage of measurements within clinical acceptance (≤ 5°) were analyzed.
RESULTS: The automatic method detected 125/144 manually measured curves. The averages of the 125 manual and automatic coronal curve angle measurements were 22.4 ± 8.0° and 22.9 ± 8.7°, respectively. Good reliability was achieved with ICC2,1 = 0.81 and SEM = 1.4°. A total of 75% (94/125) of the measurements were within clinical acceptance. The average measurement time per ultrasonograph was 36 ± 7 s. Additionally, the algorithm displayed the predicted centers of laminae to illustrate the measurement.
CONCLUSIONS: The automatic algorithm measured the coronal curve angle with moderate accuracy but good reliability. The algorithm\'s quick measurement time and interpretability can make ultrasound a more accessible imaging method for children with AIS. However, further improvements are needed to bring the method to clinical use.

OBJECTIVE: The accurate measurement of Cobb angles is crucial for the effective clinical management of patients with adolescent idiopathic scoliosis (AIS). The Lenke classification system plays a pivotal role in determining the appropriate fusion levels for treatment planning. However, the presence of interobserver variability and time-intensive procedures presents challenges for clinicians. The purpose of this study is to compare the measurement accuracy of our developed artificial intelligence measurement system for Cobb angles and Lenke classification in AIS patients with manual measurements to validate its feasibility.
METHODS: An artificial intelligence (AI) system measured the Cobb angle of AIS patients using convolutional neural networks, which identified the vertebral boundaries and sequences, recognized the upper and lower end vertebras, and estimated the Cobb angles of the proximal thoracic, main thoracic, and thoracolumbar/lumbar curves sequentially. Accordingly, the Lenke classifications of scoliosis were divided by oscillogram and defined by the AI system. Furthermore, a man-machine comparison (n = 300) was conducted for senior spine surgeons (n = 2), junior spine surgeons (n = 2), and the AI system for the image measurements of proximal thoracic (PT), main thoracic (MT), thoracolumbar/lumbar (TL/L), thoracic sagittal profile T5-T12, bending views PT, bending views MT, bending views TL/L, the Lenke classification system, the lumbar modifier, and sagittal thoracic alignment.
RESULTS: In the AI system, the calculation time for each patient\'s data was 0.2 s, while the measurement time for each surgeon was 23.6 min. The AI system showed high accuracy in the recognition of the Lenke classification and had high reliability compared to senior doctors (ICC 0.962).
CONCLUSIONS: The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.