Classification Of Adolescent Idiopathic Scoliosis Research Articles

Artificial Intelligence Measurement of Preoperative Radiographs in Adolescent Idiopathic Scoliosis Based on Multiple-View Semantic Segmentation.

Cross-sectional study. Imaging classification of adolescent idiopathic scoliosis (AIS) is directly related to the surgical strategy, but the artificial classification is complex and depends on doctors' experience. This study investigated deep learning-based automated classification methods (DL group) for AIS and validated the consistency of machine classification and manual classification (M group). A total of 506 cases (81 males and 425 females) and 1812 AIS full spine images in the anteroposterior (AP), lateral (LAT), left bending (LB) and right bending (RB) positions were retrospectively used for training. The mean age was 13.6 ± 1.8. The mean maximum Cobb angle was 46.8 ± 12.0. U-Net semantic segmentation neural network technology and deep learning methods were used to automatically segment and establish the alignment relationship between multiple views of the spine, and to extract spinal features such as the Cobb angle. The type of each test case was automatically calculated according to Lenke's rule. An additional 107 cases of adolescent idiopathic scoliosis imaging were prospectively used for testing. The consistency of the DL group and M group was compared. Automatic vertebral body segmentation and recognition, multi-view alignment of the spine and automatic Cobb angle measurement were implemented. Compare to the M group, the consistency of the DL group was significantly higher in 3 aspects: type of lateral convexity (0.989 vs 0.566), lumbar curvature modifier (0.932 vs 0.738), and sagittal plane modifier (0.987 vs 0.522). Deep learning enables automated Cobb angle measurement and automated Lenke classification of idiopathic scoliosis whole spine radiographs with higher consistency than manual measurement classification.

Validation of Artificial Intelligence in the Classification of Adolescent Idiopathic Scoliosis and the Compairment to Clinical Manual Handling.

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. 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. 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). The AI system has high reliability for the Lenke classification and is a potential auxiliary tool for spinal surgeons.

Automatic Lenke classification of adolescent idiopathic scoliosis with deep learning.

The Lenke classification system is widely utilized as the preoperative evaluation protocol for adolescent idiopathic scoliosis (AIS). However, manual measurement is susceptible to observer-induced variability, which consequently impacts the evaluation of progression. The goal of this investigation was to develop an automated Lenke classification system utilizing innovative deep learning algorithms. Using the database from the First Affiliated Hospital of Sun Yat-sen University, the whole spinal x-rays images were retrospectively collected. Specifically, images collection was divided into AIS and control group. The control group consisted of individuals who underwent routine health checks and did not have scoliosis. Afterwards, relative features of all images were annotated. Deep learning was implemented through the utilization of the key-point based detection method to realize the vertebral detection, and Cobb angle measurement and scoliosis classification were performed based on relevant standards. Besides, the segmentation method was employed to achieve the recognition of lumbar vertebral pedicle to determine the type of lumbar spine modifier. Finally, the model performance was further quantitatively analyzed. In the study, a total of 2082 spinal x-ray images were collected from 407 AIS patients and 227 individuals in the control group. The model for vertebral detection achieved an F1-score of 0.809 for curve type evaluation and an F1-score of 0.901 for thoracic sagittal profile. The intraclass correlation efficient (ICC) of the Cobb angle measurement was 0.925. In the analysis of performance for vertebra pedicle segmentation model, the F1-score of lumbar modification profile was 0.942, the intersection over union (IOU) of the target pixels was 0.827, and the Hausdorff distance (HD) was 6.565 ± 2.583 mm. Specifically, the F1-score for ultimate Lenke type classifier was 0.885. This study has constructed an automated Lenke classification system by employing the deep learning networks to achieve the recognition pattern and feature extraction. Our models require further validation in additional cases in the future.

Revision of Surgery for Adolescent Idiopathic Scoliosis: Reasons, Treatments, and Clinical Management with Case Examples.

Adolescent idiopathic scoliosis (AIS) is a curvature of the spine that develops in children ages 10-18 and can be attributed to unknown causes. The Lenke AIS classification system provides a template to classify these deformities by curve type paired with recommended operative treatments. Treatment of this patient population has been associated with low complication rates and overall surgical success. Nonetheless, a fraction of patients remain susceptible to revision surgery. This manuscript will focus on the aspects of AIS surgery, highlighting case examples, the different treatment approaches, complication rates, and primary reasons for revision surgery and associated outcomes.

Application of Deep Learning to Diagnose and Classify Adolescent Idiopathic Scoliosis

A deep learning-based model for automatic diagnosis and classification of adolescent idiopathic scoliosis has been constructed. This model mainly included key points detection and Cobb angle measurement. 748 full-length standing spinal X-ray images were retrospectively collected, of which 602 images were used to train and validate the model, and 146 images were used to test the model performance. The results showed that the model had good diagnostic and classification performance, with an accuracy of 94.5%. Compared with experts' measurement, 94.9% of its Cobb angle measurement results were within the clinically acceptable range. The average absolute difference was 2.1°, and the consistency was also excellent (r2≥0.9552, P<0.001). In the future, this model could be applied clinically to improve doctors' diagnostic efficiency.

Classification of mild and severe adolescent idiopathic scoliosis (AIS) from healthy subjects via a supervised learning model based on electromyogram and ground reaction force data during gait

At early stages, adolescent idiopathic scoliosis (AIS) is quite hard to be distinguished from healthy (HC) subjects by the naked eye. AIS demands multiple corrective surgeries when detected later, thereby causing significant physical and psychological trauma as no mathematical models exist for the classification of mild AIS (MS) (20° < Cobb’s angle < 40°) from HC, we propose a k-nearest neighbour (kNN) method based model. In this work, we collected both the EMG and GRF data from nine severe AIS (SS), three MS and four female HC during gait. Delayed muscle activation in Erector spinatus Iliocostalis, Gluteus Medius and Gastrocnemius lateralis was observed in SS compared to HC. However, no such distinction was noticed between MS and HC motivating for a mathematical model. Eighteen time-domain and nine frequency-domain features were computed from the EMG data of 14 lower extremity muscles, while five time-domain features were calculated from GRF data during gait. Out of all the features computed for each subject, the principal component analysis (PCA) yielded 15 principal components that coupled both time and frequency domains (TFD). Further, the kNN model classified SS, MS and HC from each other by these 15 TFD features. The model was trained and validated using 32 and 21 EMG and GRF data datasets during gait, respectively. The classification and validation accuracy of 90.6% and 85.7% were obtained among SS, MS and HC. The proposed model is capable of early detection of AIS and can be used by medical professionals to plan treatments and corrective measures.

Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

Adolescent Idiopathic Scoliosis (AIS) is a deformity of the spine that affects teenagers. The current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to radiation. Photogrammetry is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s back. Currently, detecting the curve of the spine is manually performed, making it a time-consuming task. To overcome this issue, it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of AIS. This research proposes a new integration of ESNN and Feature Extraction (FE) methods and explores the architecture of ESNN for the AIS classification model. This research identifies the optimal Feature Extraction (FE) methods to reduce computational complexity. The ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial. A comparison between the conventional classifier (Support Vector Machine (SVM), Multi-layer Perceptron (MLP) and Random Forest (RF)) with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia (HUKM). This dataset consists of various photogrammetry images of the human back with different types of Malaysian AIS patients to solve the scoliosis problem. The process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale images. This is then followed by feature extraction, normalization, and classification. The experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS classification. This demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.

A novel classification method for mild adolescent idiopathic scoliosis using 3D ultrasound imaging

Mild adolescent idiopathic scoliosis (AIS), with Cobb<20°, was hypothesized as the right stage to intervene to prevent progression. AIS curve can be categorized into either structural or non-structural depending on the spine morphology (flexibility). Using X-ray to characterize AIS curves remains the clinical gold standard while compromising the risks of radiation exposure. In previous works, 3D ultrasound imaging had proved the reliability of the coronal spinal curvature measurement. This research aimed at developing a mild AIS classification scheme through examining spine flexibility using 3D ultrasound imaging.For the preliminary study, 90 mild AIS subjects (21 ​M and 69 ​F; Age:14.5 ​± ​1.7 years old; Cobb: 18.2 ​± ​6.4°) underwent both 3D ultrasound and X-ray scanning on the same day. For each case, a clinician measured Cobbs and denoted major curve as ground truth. Bending Asymmetry Index (BAI) was developed to indicate the presence of a possible structural curve. The curve classification was coded to a modified Lenke classification for mild cases (m-Lenke). The results of 3D ultrasound classification were evaluated with the X-ray.It was shown that 70.1% of the subjects had identical curve classification results and 72.0% had the correct major curve detection. Lumbar-dominated curves had distinctive performance (p ​= ​0.91, r ​= ​0.91) against others. The study demonstrated the possibility of a 3D ultrasound-based method for mild AIS curve classification. The discrepancies could be partially explained by the limitations of the ultrasound scanning in proximal thoracic region. Subsequent studies will validate the proposed method with a larger cohort.

Discovering the association between the pre- and post-operative 3D spinal curve patterns in adolescent idiopathic scoliosis.

The advantage of considering the three-dimensional curve patterns, including the patterns of the vertebral position and alignment, in classification of adolescent idiopathic scoliosis (AIS) patients and whether such classification system relates to the surgical outcomes are not fully determined. A total of 371 AIS patients who received posterior spinal fusion surgery with 2-year follow-up were included retrospectively and consecutively. The 3D positions and orientations of the T1-L5 vertebrae were calculated from the 3D reconstructions of the spines at pre-operative and 2-year follow-up, a total of 102 variables per patient. A probabilistic clustering method was used to cluster the pre-operative and 2-year follow-up 3D spinal curve patterns separately. The distributions of the Lenke types and 3D pre-operative clusters in the post-operative clusters were determined. A total of nine pre-operative clusters including, four right thoracic types, three left thoracolumbar/lumbar types, one low apex right thoracic/thoracolumbar, and one left thoracic/right lumbar were determined. Three post-operative 3D curve patterns were Type 1 with higher residual proximal Cobb angle, Type 2 with lower T5-T12 kyphosis and highest pelvic incidence-lordosis mismatch, and Type 3 with larger lumbar curve magnitude and rotation compared to the other two groups. More than 50% of patients in each of the 3D pre-operative clusters had the same post-operative group. We developed a 3D classification of the AIS patients before and two-year after spinal fusion surgery. The link between the pre- and post-operative clusters lends itself to application of this classification system in developing predictive models of the AIS surgical outcomes.

An energy approach describes spine equilibrium in adolescent idiopathic scoliosis.

The adolescent idiopathic scoliosis (AIS) is a 3D deformity of the spine whose origin is unknown and clinical evolution unpredictable. In this work, a mixed theoretical and numerical approach based on energetic considerations is proposed to study the global spine deformations. The introduced mechanical model aims at overcoming the limitations of computational cost and high variability in physical parameters. The model is constituted of rigid vertebral bodies associated with 3D effective stiffness tensors. The spine equilibrium is found using minimization methods of the mechanical total energy which circumvents forces and loading calculation. The values of the model parameters exhibited in the stiffness tensor are retrieved using a combination of clinical images post-processing and inverse algorithms implementation. Energy distribution patterns can then be evaluated at the global spine scale to investigate given time patient-specific features. To verify the reliability of the numerical methods, a simplified model of spine was implemented. The methodology was then applied to a clinical case of AIS (13-year-old girl, Lenke 1A). Comparisons of the numerical spine geometry with clinical data equilibria showed numerical calculations were performed with great accuracy. The patient follow-up allowed us to highlight the energetic role of the apical and junctional zones of the deformed spine, the repercussion of sagittal bending in sacro-illiac junctions and the significant role of torsion with scoliosis aggravation. Tangible comparisons of output measures with clinical pathology knowledge provided a reliable basis for further use of those numerical developments in AIS classification, scoliosis evolution prediction and potentially surgical planning.

Selecting the "Touched Vertebra" as the Lowest Instrumented Vertebra in Patients with Lenke Type-1 and 2 Curves: Radiographic Results After a Minimum 5-Year Follow-up.

The selection of the lowest instrumented vertebra (LIV) in patients with adolescent idiopathic scoliosis (AIS) is still controversial. Although multiple radiographic methods have been proposed, there is no universally accepted guideline for appropriate selection of the LIV. We developed a simple and reproducible method for selection of the LIV in patients with Lenke type-1 (main thoracic) and 2 (double thoracic) curves and investigated its effectiveness in producing optimal positioning of the LIV at 5 years of follow-up. The radiographs for 299 patients with Lenke type-1 or 2 AIS curves that were included in a multicenter database were evaluated after a minimum duration of follow-up of 5 years. The "touched vertebra" (TV) was selected on preoperative radiographs by 2 independent examiners. The LIV on postoperative radiographs was compared with the preoperative TV. The final LIV position in relation to the center sacral vertical line (CSVL) was assessed. The CSVL-LIV distance and coronal balance in patients who had fusion to the TV were compared with those in patients who had fusion cephalad and caudad to the TV. The sagittal plane was also reviewed. In 86.6% of patients, the LIV was selected at or immediately adjacent to the TV. Among patients with an "A" lumbar modifier, those who had fusion cephalad to the TV had a significantly greater CSVL-LIV distance than those who had fusion to the TV (p = 0.006) or caudad to the TV (p = 0.002). In the groups with "B" (p = 0.424) and "C" (p = 0.326) lumbar modifiers, there were no differences among the TV groups. We recommend the TV rule as a third modifier in the Lenke AIS classification system. Selecting the TV as the LIV in patients with Lenke type-1 and 2 curves provides acceptable positioning of the LIV at long-term follow-up. The position of the LIV was not different when fusion was performed caudad to the TV but came at the expense of fewer motion segments. Patients with lumbar modifier "A" who had fusion cephalad to the TV had greater translation of the LIV, putting these patients at risk for poor long-term outcomes. Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

A new modular radiographic classification of adult idiopathic scoliosis as an extension of the Lenke classification of adolescent idiopathic scoliosis.

To propose and test the reliability of a radiographic classification system for adult idiopathic scoliosis. A three-component radiographic classification for adult idiopathic scoliosis consisting of curve type, a lumbosacral modifier, and a global alignment modifier is presented. Twelve spine surgeons graded 30 pre-marked cases twice, approximately 1week apart. Case order was randomized between sessions. The interrater reliability (Fleiss' kappa coefficient) for curve type was 0.660 and 0.798, for the lumbosacral modifier 0.944 and 0.965, and for the global alignment modifier 0.922 and 0.916, for round 1 and 2 respectively. Mean intrarater reliability was 0.807. This new radiographic classification of adult idiopathic scoliosis maintains the curve types from the Lenke classification and introduces the lumbosacral and global alignment modifiers. The reliability of the lumbosacral modifier and global alignment modifier shows near perfect agreement, and sets the foundation for further studies to validate the reliability, utility, and applicability of this classification system.

Surgical Treatment of Lenke Type 5 Adolescent Idiopathic Scoliosis: A Systematic Review.

A systematic review. To systemically review the previous literature regarding surgical treatment of Lenke type 5 adolescent idiopathic scoliosis (AIS). The Lenke classification was published in 2001 as the classification of AIS. Since then, numerous articles have been published reporting the outcomes of surgical treatment of Lenke type 5 AIS. The electronic databases PubMed, EMBASE, and Web of Science were queried up to Aug 2018 for articles regarding surgical treatment of Lenke type 5 AIS. Surgical variables, radiographic assessments, and clinical outcomes of surgical treatment of Lenke type 5 AIS were summarized. Fifty studies met the inclusion criteria. The average fused levels, % correction of thoracolumbar/lumbar curve at final follow-up, and % correction of thoracic curve at final follow-up for anterior and posterior procedures were reported to be 3.6-5.3 and 4.3-7.8 levels, 53-86 and 55-94% and 17-52 and 19-67%, respectively. Average coronal balance was imbalanced (≥20 mm) at preoperation in 22/43 reporting study groups and balanced (<20 mm) at final follow-up in all 37 reporting study groups. Scoliosis Research Society Version 22 scores showed no difference between anterior and posterior procedures in most of the reporting studies (5/6). Overall, the outcomes of surgical treatment of Lenke type 5 AIS are excellent. The thoracic curve was spontaneously corrected after surgery and coronal balance after surgery was better than before surgery. Both anterior and posterior procedures demonstrated satisfactory outcomes. 4.

An ontology-based module of the information system ScolioMedIS for 3D digital diagnosis of adolescent scoliosis

Background and objectiveConventional information systems are built on top of a relational database. The main weakness of these systems is impossibility to define stable data schema ahead when the knowledge of the system is evolving and dynamic. The widely accepted alternatives to relational databases are ontologies that can be used for designing information systems. Many research papers describe various methods for improving reliability and precision in generating the type of the Lenke classification based on the image processing techniques or a computer program, but all of them require radiograph images. The main objective of this paper is to demonstrate the development of an ontology-based module of the information system ScolioMedIS for adolescent idiopathic scoliosis (AIS) diagnosis and monitoring, which uses optical 3D methods to determine the Lenke classification of AIS and to avoid harmful effects of traditional radiation diagnosis. MethodsFor creating an ontology-based module of the ScolioMedIS we used the following steps: specification, conceptualization, formalization and implementation. In the specification and conceptualization phase we performed data collection and analysis to define domain, concepts and relationships for ontology design. In the formalization and implementation stage we developed the OBR-Scolio ontology and the ontology-based module of the ScolioMedIS. The module employs the Protégé-OWL API, as a collection of Java interfaces for the OBR-Scolio ontology, which enables the creating, deleting, and editing of the basic elements of the OBR-Scolio ontology, as well as the querying of the ontology. ResultsThe ontology-based module of ScolioMedIS is tested on the datasets of 20 female and 15 male patients with AIS between the ages of 11 and 18, to categorize spinal curvatures and to automatically generate statistical indicators about the frequency of the basic spinal curvatures, degree of progression or regression of deformity and statistical indicators about curvature characteristics according to the Lenke classification system and Lenke scoliosis types. Results are then compared with analysis of the Lenke classification of 315 observed patients, performed using traditional radiation techniques. ConclusionsThis part of the system allows continuous monitoring of the progression/regression of spinal curvatures for each registered patient, which may provide a better management of scoliosis (diagnosis and treatment).

Modified PUMC classification for adolescent idiopathic scoliosis

BACKGROUNDThe original Peking Union Medical College (PUMC) classification of adolescent idiopathic scoliosis (AIS) is one system to combine each type with corresponding surgical fusion guidance, presenting comparable interobserver reliability, and reproducibility with Lenke classification. However, during its application in previous over 10 years, we found 2 main problems of this classification, which required modification. PURPOSE(1) To evaluate the interobserver and intraobserver reliability, (2) to assess the effects of the added fusion criteria of proximal thoracic (PT) curve on improving postoperative shoulder balance of the modified PUMC classification of AIS. STUDY DESIGN/SETTINGRetrospective analysis of our AIS cohort and prospective validation of its effectiveness. PATIENT SAMPLEFifty sets of preoperative radiographs of AIS patients were randomly chosen from our AIS database. Furthermore, 46 consecutive AIS cases with PT curve were enrolled who underwent surgeries in our center from July 2007 to July 2013, with at least 2-year follow-up. OUTCOME MEASURESThe classification results of 50 sets of preoperative radiographs by 5 surgeons. The shoulder balance was evaluated using radiographic shoulder height. METHODSFive surgeons independently evaluated and classified presurgical radiographs of 50 AIS patients based on the modified PUMC classification. Inter- and intraobserver reliabilities were calculated. Furthermore, the post-op shoulder balance was investigated in 46 consecutive cases of AIS with PT curve who were treated strictly based on the modified PUMC classification. RESULTSThe Kappa coefficients of inter- and intraobserver reliability of the overall modified PUMC classification are 0.889 and 0.865, respectively. The Kappa coefficients of inter- and intraobserver reliability for the type II are 0.791 and 0.746, respectively. In addition, the shoulder balance rate of the 46 AIS patients with PT curve at the final follow-up was 95.7%. CONCLUSIONSModified PUMC classification presents incremental improvement compared to our original published version, with high interobserver and intraobserver reliability and better performance of postoperative shoulder balance. Furthermore, the modified PUMC classification provides corresponding surgical fusion guidance for each subtype. Multicenter prospective studies with larger samples are still needed to further improve this system.

A hierarchical classification of adolescent idiopathic scoliosis: Identifying the distinguishing features in 3D spinal deformities.

This study aimed to identify the differentiating parameters of the spinal curves’ 2D projections through a hierarchical classification of the 3D spinal curve in adolescent idiopathic scoliosis (AIS). A total number of 103 right thoracic left lumbar pre-operative AIS patients were included retrospectively and consecutively. A total number of 20 non-scoliotic adolescents were included as the control group. All patients had biplanar X-rays and 3D reconstructions of the spine. The 3D spinal curve was calculated by interpolating the center of vertebrae and was isotropically normalized. A hierarchical classification of the normalized spinal curves was developed to group the patients based on the similarity of their 3D spinal curve. The spinal curves’ 2D projections and clinical spinal measurements in the three anatomical planes were then statistically compared between these groups and between the scoliotic subtypes and the non-scoliotic controls. A total of 5 patient groups of right thoracic left lumbar AIS patients were identified. The characteristics of the posterior-anterior and sagittal views of the spines were: Type 1: Normal sagittal profile and S shape axial view. T1 is leveled or tilted to the right in the posterior view. Type 2: Hypokyphotic and a V shape axial view. T1 is tilted to the left in the posterior view. Type 3: Hypokyphotic (only T5-T10) and frontal imbalance, S shape axial view. T1 is leveled or tilted to the right, and 3 frontal curves. Type 4: Flat sagittal profile (T1-L2), slight frontal imbalance with a V shape axial view, T1 tilted to the left. Type 5: flat sagittal profile and forward trunk shift with a proximal kyphosis and S shape axial view. T1 is leveled or tilted to the right. In conclusion, a hierarchical classification of the 3D scoliotic spine allowed identifying various distinguishing features of the spinal curves in patients with a right thoracic curve in an orderly fashion. The subtypes’ characteristics resulting from this 3D classification can be identified from the pairs of the frontal and sagittal spinal curves i.e. X-rays in right thoracic AIS patients.

Customized k-nearest neighbourhood analysis in the management of adolescent idiopathic scoliosis using 3D markerless asymmetry analysis

Adolescent Idiopathic Scoliosis (AIS) is a 3D spinal deformity characterized by curvature and rotation of the spine. Markerless surface topography (ST) analysis has been proposed for diagnosing and monitoring AIS to reduce the X-ray radiation exposure to patients. This method captures scans of the cosmetic deformity of the torso using visible, radiation-free light. The asymmetry analysis of the torso, represented as a deviation contour map with deviation patches outlining the areas of cosmetic asymmetries, has previously been shown to predict the severity and progression of the condition in comparison with radiographs, by using classification trees. While the classification results were promising, it was reported that some mild curves were erroneously diagnosed. Furthermore, this approach is highly sensitive to threshold values selected in the decision trees. Therefore, this study aims to define a custom Neighbourhood Classifier algorithm for AIS classification to improve the accuracy, sensitivity, and specificity of predicting curve severity and curve progression in AIS. Curve severity was predicted with 80% accuracy (sensitivity = 81%; specificity = 79%) for thoracic-thoracolumbar curves and 72% (sensitivity = 93%; specificity = 53%) for lumbar curves. This represents an improvement over the previous method with curve severity accuracies of 77% and 63% for thoracic-thoracolumbar and lumbar curves, respectively. Additionally, curve progression was predicted with 93% accuracy (sensitivity = 83%; specificity = 95%) representing a substantial improvement over the previous method with an accuracy of 59%. The current method has shown the potential to further reduce radiation exposure for AIS patients by avoiding X-rays for mild and non-progressive curves identified using ST analysis.

Preoperative Sagittal Spinal Profile of Adolescent Idiopathic Scoliosis Lenke Types and Non-Scoliotic Adolescents: A Systematic Review and Meta-Analysis.

Systematic review and meta-analysis. This study aims to determine the differences in sagittal spinopelvic parameters between adolescent idiopathic scoliosis (AIS) Lenke types and non-scoliotic controls through a systematic review and meta-analysis of the available literature. AIS classification mainly focuses on frontal curve differences; however, the variations in the sagittal spinopelvic alignment in the current classification system is not fully established. Following preferred reporting items for systematic reviews and meta-analyses guidelines, searches were performed for sagittal spinal and pelvic parameters of Lenke types and non-scoliotic controls in PubMed, Scopus, EMBASE, and Cochrane databases. Selection criteria were: (1) age range 10 to 21 years; (2) Lenke types 1-6 (for AIS group) or non-scoliotic adolescents (for the control group); (3) preoperative data for T5-T12 thoracic kyphosis (TK), L1-S1 lumbar lordosis (LL), pelvic incidence (PI), sacral slope (SS), and sagittal vertical axis (SVA). (4) Written in English language. PI-LL mismatch was calculated from the weighted average of PI and LL. Publication bias between studies and within studies quality were assessed. A meta-regression compared each measured variable between groups. Thoracic (Lenke1 and 2) and thoracolumbar/lumbar (Lenke 5 and 6) scoliosis were combined and statistically compared with the control group. Meta-analysis, including 81 AIS and 18 control studies, showed no significant differences in sagittal parameters between Lenke types and controls for LL, PI, PI-LL mismatch, SS, and SVA (P > 0.05). Publication bias was significant in Lenke 1 TK, Control LL, and Lenke 1, and 5 SVA. Stratification based on deformity region (thoracic vs. thoracolumbar/lumbar) showed no significant differences in sagittal spinopelvic parameters (P > 0.05). No definitive difference was found between non-scoliotic adolescents and Lenke types in sagittal spinal and pelvic parameters. Future studies on developing a sagittal classification specific to AIS patients with a goal to improve surgical planning and outcome prediction are highly encouraged. 4.

Description and Reliability of Rigo Classification of Adolescent Idiopathic Scoliosis Associated with Brace Therapy

Objectives To develop the Rigo classification system for scoliosis brace design and treatment and to compare the reliability and reproducibility of the Rigo classification system for scoliosis within and across observers related to orthopedic treatment. Methods The imaging data of 136 cases of adolescent idiopathic scoliosis were collected, and a Rigo classification system was established to determine the specific corrective principles and methods needed for the effective design and manufacturing of braces. Three observers, one spine surgeon, one imaging surgeon and one orthopedic brace surgeon, diagnosed and classified 136 AIS patients. The collected data were used to analyze the reliability across different observers and the repeatability within the same observer, and kappa values were calculated to confirm the consistency. Results The kappa value of the classification results of the spine surgeons and radiologists was 0.936, that of the spine surgeons and brace surgeons was 0.901, that of the imaging surgeons and brace surgeons was 0.886, the average repeatability was 90%, and the average Kappa was 0.887. Conclusion The Rigo classification system for scoliosis brace therapy has good practicability and consistency. The reliability of the Rigo classification system is acceptable both within and across observers. The system is suitable for clinical applications.

Classifications in Brief: The Lenke Classification for Adolescent Idiopathic Scoliosis.

Classifications in Brief: The Lenke Classification for Adolescent Idiopathic Scoliosis.