Search Results (4)

Background/Aims: The aim of this study was to evaluate the effectiveness of large language model (LLM)-based chatbot systems in predicting bone age and identifying growth stages, and to explore their potential as practical, infrastructure-independent alternatives to conventional methods and convolutional neural network (CNN)-based deep learning models. Methods: This study evaluated the performance of three ChatGPT-based models (GPT-4o, GPT-o4-mini-high, and GPT-o1-pro) in predicting bone age and growth stage using 90 anonymized hand–wrist radiographs (30 from each growth stage—pre-peak, peak, and post-peak—with equal male and female distribution). Reference standards were ensured by expert orthodontists using Fishman’s Skeletal Maturity Indicators (SMI) system and the Greulich–Pyle Atlas, with each radiograph analyzed by three GPT models using standardized prompts. Model performances were evaluated through statistical analyses assessing agreement and prediction accuracy. Results: All models showed significant agreement with the reference values in bone age prediction (p < 0.001), with GPT-o1-pro having the highest concordance (Pearson r = 0.546). No statistically significant difference was observed in the mean absolute error (MAE) among the models (p > 0.05). The GPT-o4-mini-high model achieved an accuracy rate of 72.2% within a ±2 year deviation range for bone age prediction. The GPT-o1-pro and GPT-o4-mini-high models showed bias in the Bland–Altman analysis of bone age predictions; however, GPT-o1-pro yielded more reliable predictions with narrower limits of agreement. In terms of growth stage classification, the GPT-4o model achieved the highest agreement with the reference values (κ = 0.283, p < 0.001). Conclusions: This study shows that general-purpose GPT models can support bone age and growth stages prediction, with each model having distinct strengths. While GPT models do not replace clinical examination, their contextual reasoning and ability to perform preliminary assessments without domain-specific training make them promising tools, though further development is needed. Full article

The purpose of this study was to investigate changes in bone trabecular structure during adolescence using the fractal analysis (FA) method on hand–wrist radiographs (HWRs) and to evaluate the relationship of these changes with pubertal growth stages. HWRs of healthy individuals aged 8–18 years were included (N = 600). Pubertal stages were determined by the Fishman method and divided into 10 groups (early puberty [EP], pre-peak [PRPK], peak [PK], post-peak [PTPK], late puberty [LP]). FA was performed using FIJI (ImageJ) software and the BoneJ plugin on circular regions of interest (ROIs) selected from the first metacarpal bone head and distal radius. Image processing steps were applied according to the White and Rudolph method. Differences between groups were statistically evaluated. Fractal dimension (FD) values of the distal radius (RAFAM) and metacarpal bone head (MAFAM) showed significant differences according to pubertal growth stages (p < 0.05). The highest FD value was observed in the LP group, and the lowest FD value was observed in the EP group (except MAFAM in females). FD generally increased from EP to LP in the whole population, but a significant decrease was observed in all groups during the PK period. This decrease was more pronounced in RAFAM of males. These findings suggest a potential decrease of bone mechanical properties in the PK, which is found the be more suitable for orthodontic treatment in the literature. FA on HWRs is a useful and sensitive tool for quantitatively assessing pubertal changes in trabecular bone microarchitecture. The findings demonstrate a significant decrease in FD in both bone regions during the pubertal growth spurt, particularly at the peak period. This may indicate a temporary reduction in bone mechanical strength during this critical stage and could contribute to increased distal radius fracture incidence. Clinically, the relationship between FD and pubertal stages suggests this method could serve as a valuable biomarker in orthodontic treatment planning, allowing for optimized timing of interventions. Furthermore, it may aid in pediatric fracture risk assessment, potentially leading to preventative strategies for high-risk individuals. Full article

Long-term natural history studies are important in rare disease research. This study aimed to assess electrophysiological and fundus autofluorescence (FAF) progression rate in 18 genetically confirmed Stargardt disease (STGD1) patients with a minimum follow-up of 10 years. Age at the first and last exams, age at onset, Snellen decimal visual acuity (VA), electroretinography (ERG), and FAF images were evaluated. Patients were classified into four Fishman stages and three electroretinography groups, and areas of definitely decreased autofluorescence (DDAF) were measured. Patients were further substratified based on genotype, and phenotype-genotype correlations were performed. The median follow-up was 18 (range 10–26) years. The median yearly VA loss was 0.009 (range 0.002–0.071), while the median progression rate of the DDAF area was 0.354 (range 0.002–4.359) mm² per year. Patients harbouring p.(Gly1961Glu) or p.(Asn1868Ile) allele had significantly slower DDAF area progression when compared to patients with other genotypes (0.07 mm² vs. 1.03 mm², respectively), as well as significantly later age at onset (20 years vs. 13 years, respectively). Results showed that structural and functional parameters, together with genotype, should be considered when counselling patients regarding prognosis and monitoring disease progression. Patients harbouring hypomorphic variants p.(Gly1961Glu) or p.(Asn1868Ile) presented with overall milder disease than patients with other genotypes. Full article

The research aims to verify the concordance between the skeletal maturity index (SMI) measured on the hand and wrist X-rays using Fishman method and the cervical vertebral maturation stage (CVMS), measured on the lateral cephalometric X-rays using Baccetti method. The concordance of the two indexes (SMI and CVMS) has been statistically verified with the help of the Cohen’s kappa coefficient, by relating them to the growth stages, within a longitudinal study done upon a group of 38 patients, 22 female and 16 male, aged between 8–18 y, the analyzed investigations being done in series, along the orthodontic treatment. The research showed a strong correlation between the SMI and CVMS indexes within the analyzed group, confirmed by the obtained values (k = 0.84 for female and k = 0.85 for male). Full article