Background: The progression of scoliosis has been observed in skeletally mature patients with cerebral palsy (CP). The aims of this systematic review were to determine the incidence of curve progression of untreated scoliosis after skeletal maturity, to estimate the average annual increase and to identify factors that influence the progression. Methods: A systematic literature search was performed in PubMed, Embase and the Cochrane Library for original research articles published between 1968 and May 2024 with a retrospective, prospective or cross-sectional design, investigating CP patients that were followed up beyond the age of 15 years. The search was limited to articles in English, French, German and Dutch. Articles were excluded if the study population concerned neuromuscular diseases other than CP. After an assessment of the methodological quality of each study, estimates of annual curve progression and the effect of the investigated risk factors for progression were recorded systematically and synthetized. Results: Fifteen studies met the inclusion criteria, resulting in a total sample size of 2569 participants. The study populations of the included original research articles were small and heterogeneous in terms of patient age and the type and severity of CP. Curve progression after skeletal maturity occurred in all included studies. A greater curve magnitude at the end of adolescence and a severe motor deficit (an inability to walk or GMFCS IV-V) were identified as significant risk factors for the progression of scoliosis after skeletal maturity. If at least one of these risk factors was present, scoliotic curves progressed after skeletal maturity in up to 74% of patients, with an average annual increase of 1.4 to 3.5 degrees per year. No significant association was found between curve progression and the physiologic type of CP, the type of scoliotic curve, previous hip surgery, positioning and gravity, weight and length, sex, epilepsy, or pelvic obliquity. Findings on the effect of hip instability were inconsistent: a positive correlation was found with the progression of scoliosis overall, but not after skeletal maturity in particular. A significant selection bias should be considered in the calculation of average annual curve progression, as patients that received interventions to halt curve progression were excluded from follow-up. Conclusions: The identification of risk factors in patients with CP and scoliosis can aid in predicting curve progression and managing follow-ups in clinical practice. Based on the findings in this review a radiographic follow-up once every 3 years is recommended for skeletally mature CP patients with at least one risk factor, and once every 5 years if no risk factors are present.

Background/Objectives: To systematically review and summarize the existing scientific evidence on the diagnostic performance of artificial intelligence (AI) in assessing cervical vertebral maturation (CVM). This review aimed to evaluate the accuracy and reliability of AI algorithms in comparison to those of experienced clinicians. Methods: Comprehensive searches were conducted across multiple databases, including PubMed, Scopus, Web of Science, and Embase, using a combination of Boolean operators and MeSH terms. The inclusion criteria were cross-sectional studies with neural network research, reporting diagnostic accuracy, and involving human subjects. Data extraction and quality assessment were performed independently by two reviewers, with a third reviewer resolving any disagreements. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool was used for bias assessment. Results: Eighteen studies met the inclusion criteria, predominantly employing supervised learning techniques, especially convolutional neural networks (CNNs). The diagnostic accuracy of AI models for CVM assessment varied widely, ranging from 57% to 95%. The factors influencing accuracy included the type of AI model, training data, and study methods. Geographic concentration and variability in the experience of radiograph readers also impacted the results. Conclusions: AI has considerable potential for enhancing the accuracy and reliability of CVM assessments in orthodontics. However, the variability in AI performance and the limited number of high-quality studies suggest the need for further research.

OBJECTIVE: The objective was to critically analyze the published literature accounting for sex differences and skeletal age (open vs. closed physis) in preclinical animal models of OA, including the disaggregation of data by sex and skeletal maturity when data is generated from combined sex and/or multi-aged cohorts without proper confounding.
METHODS: A scoping literature review of PubMed, Web of Science, EMBASE, and SCOPUS was performed for studies evaluating the effect of sex and age in experimental studies and clinical trials utilizing preclinical large animal models of OA.
RESULTS: A total of 9727 papers were identified in large animal (dog, pig, sheep, goat, horse) models for preclinical OA research, of which 238 ex vivo and/or in vivo studies disclosed model type, animal species, sex, and skeletal age sufficient to analyze their effect on outcomes. Dogs, followed by pigs, sheep, and horses, were the most commonly used models. A paucity of preclinical studies evaluated the effect of sex and age in large animal models of naturally occurring or experimentally induced OA: 26 total studies reported some kind of analysis of the effects of sex or age, with 4 studies discussing the effects of sex only, 11 studies discussing the effects of age only, and 11 studies analyzing both the effects of age and sex.
CONCLUSIONS: Fundamental to translational research, OARSI is uniquely positioned to develop recommendations for conducting preclinical studies using large animal models of OA that consider biological mechanisms linked to sex chromosomes, skeletal age, castration, and gonadal hormones affecting OA pathophysiology and treatment response.

To assess the reliability of different salivary biomarkers as skeletal maturity indicators when compared with other methods of skeletal maturity assessment.
A comprehensive search was conducted on three electronic databases: PUBMED, Google scholar and Cochrane library for the articles published from 2000 to July 2021. Assessment of skeletal age on the basis of levels of different salivary biomarkers at different pubertal stages was considered as the primary outcome. Electronic search, data collection and risk of bias assessment were performed by two authors with conflict resolution by the third author.
Total 158 articles were retrieved after screening of titles, abstracts and full texts of all articles, of which 15 articles were selected for qualitative synthesis. All these studies were cross-sectional in design. These studies compared the levels of different salivary biomarkers as Alkaline Phosphatase (ALP), Insulin-like Growth Factor - I (IGF-I), Insulin-like Growth Factor Binding Protein-3 (IGFBP-3), Cortisol, Indian Hedgehog (IHH) protein and Dehydroepiandrosterone sulphate (DHEAS) with other methods of skeletal age estimation. Out of these six biomarkers salivary IGF-1 is a reliable indicator for skeletal maturity assessment.
The current evidence suggests that salivary biomarkers can be used as an adjunct for growth prediction during orthodontic treatment planning along with other methods of skeletal maturation assessment. Still there is need for further research with longitudinal studies in this field.

To assess the accuracy of identification and/or classification of the stage of cervical vertebrae maturity on lateral cephalograms by neural networks as compared with the ground truth determined by human observers.
Search results from four electronic databases (PubMed [MEDLINE], Embase, Scopus, and Web of Science) were screened by two independent reviewers, and potentially relevant articles were chosen for full-text evaluation. Articles that fulfilled the inclusion criteria were selected for data extraction and methodologic assessment by the QUADAS-2 tool.
The search identified 425 articles across the databases, from which 8 were selected for inclusion. Most publications concerned the development of the models with different input features. Performance of the systems was evaluated against the classifications performed by human observers. The accuracy of the models on the test data ranged from 50% to more than 90%. There were concerns in all studies regarding the risk of bias in the index test and the reference standards. Studies that compared models with other algorithms in machine learning showed better results using neural networks.
Neural networks can detect and classify cervical vertebrae maturation stages on lateral cephalograms. However, further studies need to develop robust models using appropriate reference standards that can be generalized to external data.

As youth sports participation has increased in recent years, injuries to the anterior cruciate ligament (ACL) have become increasingly common in pediatric patients. Historically, ACL reconstruction was delayed in pediatric patients to avoid physeal damage with the potential for leg-length discrepancy or angular deformity. Current research shows that delaying reconstruction or choosing nonoperative management is associated with increased rates of meniscal and chondral injuries, persistent knee instability, and low rates of return to previous activity. Early ACL reconstruction using techniques to avoid physeal growth disturbance is now widely accepted among physicians. The purpose of this review was to describe the pediatric ACL in terms of the relevant anatomy and biomechanics, physical examination, and diagnostic imaging. In addition, the importance of skeletal age and possible physeal injury is discussed in the context of ACL reconstruction options by skeletal age and remaining growth potential.

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Idiopathic scoliosis, defined as a > 10° curvature of the spine in the frontal plane, is one of the most common spinal deformities. Age, initial curve magnitude and other parameters define whether a scoliotic deformity will progress or not. Still, their interactions and amounts of individual contribution are not fully elaborated and were the aim of this systematic review.
A systematic literature search was conducted in the common databases using MESH terms, searching for predictive factors of curve progression in adolescent idiopathic scoliosis (\"adolescent idiopathic scoliosis\" OR \"ais\" OR \"idiopathic scoliosis\") AND (\"predictive factors\" OR \"progression\" OR \"curve progression\" OR \"prediction\" OR \"prognosis\"). The identified and analysed factors of each study were rated to design a top five scale of the most relevant factors.
Twenty-eight investigations with 8255 patients were identified by literature search. Patient-specific risk factors for curve progression from initial curve were age (at diagnosis < 13 years), family history, bone mineral status (< 110 mg/cm3 in quantitative CT) and height velocity (7-8 cm/year, peak 11.6 ± 1.4 years). Relevant radiological criteria indicating curve progression included skeletal maturity, marked by Risser stages (Risser < 1) or Sanders Maturity Scale (SMS < 5), the initial extent of the Cobb angle (> 25° progression) and curve location (thoracic single or double curve).
This systematic review summarised the current state of knowledge as the basis for creation of patient-specific algorithms regarding a risk calculation for a progressive scoliotic deformity. Curve magnitude is the most relevant predictive factor, followed by status of skeletal maturity and curve location.