Judith Sánchez-Raya, Judith Salat-Batlle, Diana Castilla, Irene Zaragozá, Azucena García-Palacios, Carlos Suso-Ribera 

Pers Med. 2025 Sep 1;15(9):405.doi: 10.3390/jpm15090405

ABSTRACT

Background/Objectives: Adolescent idiopathic scoliosis (AIS) often requires prolonged brace use to prevent curve progression. However, adherence is challenging due to discomfort, mobility restrictions, and psychosocial stressors. This study evaluated the feasibility and clinical utility of a mobile health (mHealth) system for real-time tracking of brace adherence and treatment-related experiences in adolescents with AIS. 

Methods: Thirty adolescents with AIS (mean age = 12.9, SD = 1.8) undergoing brace treatment at a tertiary care center used a custom app for 90 days. The app collected daily self-reports on brace wear duration, discomfort, movement limitations, emotional distress, and social challenges. A clinical alarm system alerted providers when patient input indicated potential concerns. Primary outcomes were feasibility (adherence to daily use and usability ratings) and brace adherence. Secondary outcomes included the app’s capacity to identify treatment-related challenges and its association with changes in stress, quality of life, anxiety, and depression. 

Results: Participants reported meeting recommended brace wear time (≥16 h/day) on 84.8% of days. The app triggered 186 clinical alarms, with the most frequent related to emotional distress (23.1%) and pain (15.6%). Alarm frequency declined over time. Improvements of ≥20% in psychological outcomes were observed in 20-26.7% of participants, while group-level changes were nonsignificant. 

Conclusions: mHealth-based monitoring appears feasible and acceptable for digitally engaged adolescents with AIS. The app supported early detection of treatment barriers and prompted timely clinical responses. Despite limitations, it shows promise as a tool to improve treatment engagement and address psychosocial challenges in scoliosis care.

Keywords: adolescent idiopathic scoliosis; brace adherence; digital health monitoring; mobile health (mHealth); patient-reported outcomes; personalized medicine.

https://pubmed.ncbi.nlm.nih.gov/41003108/

Vojtech Capek, Helena Brisby, Olof Westin

Spine (Phila Pa 1976). 2025 Jun 9.doi: 10.1097/BRS.0000000000005419. Online ahead of print.

ABSTRACT

Study design: Agreement analysis of retrospectively collected data.

Objective: To perform an agreement analysis between Sanders maturity scale and pelvic maturity indicators in patients with idiopathic scoliosis.

Summary of background data: A reliable maturity assessment is mandatory for Adolescent Idiopathic Scoliosis treatment. The two most common skeletal maturity surrogates are the Sanders maturity scale (SMS) and Risser staging (RS). The relationship between these has been scarcely investigated.

Methods: From a previous brace study, 130 consecutive patients with both pelvic and hand radiographs with a maximum 3-month interval between examinations were selected. The pelvic maturity indicators (PMI) scale combining the Risser sign and triradiate cartilage stage was developed. The PMI consist of 6 stages (2-7) that were tested against corresponding SMS stages. An agreement analysis between the PMI based on pelvic radiograph and SMS of the hand image was conducted. The Spearman’s rank correlation and Cohen’s kappa with Gwet’s AC1 coefficient were the measures of correlation and agreement, respectively.

Results: The PMI and SMS showed a strong correlation (Spearman’s rho= 0.79; 95% CI [0.72, 0.85]) as well as substantial agreement (Gwet’s AC1 = 0.76; 95% CI [0.70, 0.82]). The highest accuracy in matching PMI to SMS was observed for PMI 2, 3 and 7, with 83.3%, 80.0% and 88.9% correctly matched SMS stage to corresponding PMI stage. The regression model accurately predicted five of six SMS stages from pelvic radiographs.

Conclusions: The triradiate cartilage staging facilitated the maturity assessment of the pelvic radiographs and showed a strong correlation to and substantial agreement with the SMS. The most important stages for AIS development, SMS 2, 3 and 7, showed the best matching accuracy to PMI. The pelvic image of PMI 3 with closing triradiate cartilage corresponds to SMS stage 3.

Level of evidence: III.

Keywords: growth; idiopathic scoliosis; maturity; radiology; risser sign; sanders maturity scale; scoliosis; skeletal maturity.

Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.

https://pubmed.ncbi.nlm.nih.gov/40492476/

Yuan Gao, Jia Ying, Bowen Lai, Rui Gao, Heng Jiang, Xuhui Zhou 

World Neurosurg. 2025 Apr 29:198:124025.doi: 10.1016/j.wneu.2025.124025

ABSTRACT

Objective: While the etiology of adolescent idiopathic scoliosis (AIS) remains unclear, it is assumed that paraspinal muscle imbalance contribute to curve progression. Previous studies have found the paraspinal muscles imbalance, but no study comprehensively analyzed the roles of different paraspinal muscle layers. Our study distinguished the thoracic paraspinal muscle into superficial, intermediate, and deep layers, and analyzed the imbalance of the 3 layers.

Methods: We retrospectively included AIS patients with thoracic curve. The paraspinal muscle parameters including relative muscle cross-section area (rmCSA) and fatty infiltration (FI) in upper end vertebra (UEV), apical vertebra (AV), and lower end vertebra (LEV) regions and in superficial, intermediate, and deep layers were calculated. The parameters between convex and concave sides were compared by paired t-test. The correlation between paraspinal muscle parameters and radiographic parameters was tested by spearman correlation analysis.

Results: A total of 46 patients were included. In AV region, the rmCSA in superficial (P=0.038), intermediate (P=0.001), and deep (P=0.001) layers on convex side was greater than that on concave side, while the FI in intermediate (P=0.023) and deep layers (P=0.000) on concave side was greater than that on convex side. Radiographic parameters were associated with both intermediate ΔrmCSA (P=0.002) and deep ΔrmCSA (P=0.000).

Conclusion: Our research indicates that the severity of paraspinal muscle imbalance is different in three layers, with the deep layer most severe, followed by the intermediate layer, and the superficial layer least severe. The imbalance of deep muscle correlates strongly with radiographic parameters, suggesting its role as a compensatory adaptation to curve magnitude.

Keywords: Adolescent idiopathic scoliosis; Fatty infiltration; Paraspinal muscle imbalance; Relative muscle cross-sectional area; Three muscle layers.

Copyright © 2025. Published by Elsevier Inc.

https://pubmed.ncbi.nlm.nih.gov/40311801/

Shuhei Ohyama, Satoshi Maki, Toshiaki Kotani, Yosuke Ogata, Tsuyoshi Sakuma, Yasushi Iijima, Tsutomu Akazawa, Kazuhide Inage, Yasuhiro Shiga, Masahiro Inoue, Takahito Arai, Noriyasu Toshi, Soichiro Tokeshi, Kohei Okuyama, Susumu Tashiro, Noritaka Suzuki, Yawara Eguchi, Sumihisa Orita, Shohei Minami, Seiji Ohtori

Eur Spine. 2025 Feb 4.doi: 10.1007/s00586-025-08680-9. Online ahead of print.

Abstract

Purpose: This study was designed to develop a machine learning (ML) model that predicts future Cobb angle in patients with adolescent idiopathic scoliosis (AIS) using minimal radiographs and simple questionnaires during the first and second visits.

Methods: Our study focused on 887 female patients with AIS who were initially consulted at a specialized scoliosis center from July 2011 to February 2023. Patient data, including demographic and radiographic data based on anterior-posterior and lateral whole-spine radiographs, were collected at the first, second, and final visits. ML algorithms were employed to develop individual regression models for future Cobb angles of each curve type (proximal thoracic: PT, main thoracic: MT, and thoracolumbar/lumbar: TLL) using PyCaret in Python. Multiple models were explored and analyzed, with the selection of optimal models based on the coefficient of determination (R²) and median absolute error (MAE).

Results: For the future curve of PT, MT, and TLL, the top-performing models exhibit R² of 0.73, 0.63, and 0.61 and achieve MAE of 2.3°, 4.0°, and 4.2°.

Conclusions: The ML-based model using items commonly evaluated at the first and second visits accurately predicted future Cobb angles in female patients with AIS.

Keywords: Adolescent idiopathic scoliosis; Classification model; Machine learning algorithms; Prediction model; Regression model.

https://pubmed.ncbi.nlm.nih.gov/39903251/

Siegmund Lang, Jacopo Vitale, Fabio Galbusera, Tamás Fekete, Louis Boissiere, Yann Philippe Charles, Altug Yucekul, Caglar Yilgor, Susana Núñez-Pereira, Sleiman Haddad , Alejandro Gomez-Rice, Jwalant Mehta, Javier Pizones, Ferran Pellisé, Ibrahim Obeid, Ahmet Alanay, Frank Kleinstück, Markus Loibl ; ESSG European Spine Study Group

Spine Deform. 2024 Nov 4. doi: 10.1007/s43390-024-00955-3. Online ahead of print

Abstract

Purpose: Large language models (LLM) have the potential to bridge knowledge gaps in patient education and enrich patient-surgeon interactions. This study evaluated three chatbots for delivering empathetic and precise adolescent idiopathic scoliosis (AIS) related information and management advice. Specifically, we assessed the accuracy, clarity, and relevance of the information provided, aiming to determine the effectiveness of LLMs in addressing common patient queries and enhancing their understanding of AIS.

Methods: We sourced 20 webpages for the top frequently asked questions (FAQs) about AIS and formulated 10 critical questions based on them. Three advanced LLMs-ChatGPT 3.5, ChatGPT 4.0, and Google Bard-were selected to answer these questions, with responses limited to 200 words. The LLMs’ responses were evaluated by a blinded group of experienced deformity surgeons (members of the European Spine Study Group) from seven European spine centers. A pre-established 4-level rating system from excellent to unsatisfactory was used with a further rating for clarity, comprehensiveness, and empathy on the 5-point Likert scale. If not rated ‘excellent’, the raters were asked to report the reasons for their decision for each question. Lastly, raters were asked for their opinion towards AI in healthcare in general in six questions.

Results: The responses among all LLMs were ‘excellent’ in 26% of responses, with ChatGPT-4.0 leading (39%), followed by Bard (17%). ChatGPT-4.0 was rated superior to Bard and ChatGPT 3.5 (p = 0.003). Discrepancies among raters were significant (p < 0.0001), questioning inter-rater reliability. No substantial differences were noted in answer distribution by question (p = 0.43). The answers on diagnosis (Q2) and causes (Q4) of AIS were top-rated. The most dissatisfaction was seen in the answers regarding definitions (Q1) and long-term results (Q7). Exhaustiveness, clarity, empathy, and length of the answers were positively rated (> 3.0 on 5.0) and did not demonstrate any differences among LLMs. However, GPT-3.5 struggled with language suitability and empathy, while Bard’s responses were overly detailed and less empathetic. Overall, raters found that 9% of answers were off-topic and 22% contained clear mistakes.

Conclusion: Our study offers crucial insights into the strengths and weaknesses of current LLMs in AIS patient and parent education, highlighting the promise of advancements like ChatGPT-4.o and Gemini alongside the need for continuous improvement in empathy, contextual understanding, and language appropriateness.

Keywords: Adolescent idiopathic scoliosis (AIS); Artificial intelligence (AI); Large language models (LLMs); Patient education; Spine surgery.

https://pubmed.ncbi.nlm.nih.gov/39495402/