Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. Purpose The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. Methods This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. Results The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. Conclusion This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.
Muscle loss after radiotherapy is associated with poor overall survival in patients with oral cavity cancer. In this study, we aimed to develop a machine learning model for predicting muscle loss after radiotherapy. This study included patients with oral cavity cancer who underwent surgery and post-operative radiotherapy at two tertiary centers between 2010 and 2020. Muscle loss was determined by comparing pre- and post-radiotherapy computed tomography scans. The Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were trained to predict muscle loss using clinical and toxicity features. Model performance was evaluated using the area under the curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to interpret the model. Of 903 eligible patients (median age: 55 years), 572 and 331 were in the derivation and external validation cohorts, with 144 (25.2
Bronchiectasis has traditionally been characterized as a neutrophil-driven disease, yet emerging evidence suggested inflammatory heterogeneities. The prognostic significance of elevated serum immunoglobulin E (IgE) in patients without peripheral eosinophilia remains unclear. We conducted a multicenter prospective cohort study between 2017 and 2020 across 16 institutions in Taiwan. Individuals with bronchiectasis but without allergic bronchopulmonary aspergillosis were included. Patients were stratified by baseline absolute eosinophil count (cutoff 300 /uL) and serum IgE level (≤ 100, 100–500, > 500 IU/mL). The primary endpoint was severe exacerbations resulting in hospitalization at one year. Secondary endpoints included all-cause mortality, distribution of sputum pathogen, imaging pattern, and lung function. A total of 579 individuals were enrolled. Nontuberculous mycobacteria (10.7
Background:Colonoscopy plays a vital role in assessing disease activity in ulcerative colitis (UC), and biopsy via colonoscopy helps to evaluate its histological activity. Endoscopists must report the endoscopic activity and rely on the biopsy results to predict the histological activity. Methods:We aimed to develop a deep learning-based algorithm to evaluate the disease and histological activities of UC based on white-light endoscopic images obtained during the procedure in this research. A deep learning system for classifying the colonoscopic images for assessing the endoscopic and histological activities of UC patients was developed. Its performance was evaluated with an independent dataset. The system was utilized to analyze the captured video segments, and the results were compared with those of human endoscopists. Results:A total of 375 video segments from 82 patients were utilized to develop the endoscopic and histological activity prediction assurance algorithm. Among the 375 video segments, 60%, 20%, and 20% were used for training, validation, and testing the proposed vision transformer (ViT) model, respectively. Moreover, four senior and six young endoscopists reviewed and scored the endoscopic and histological activities based on 77 testing video clips. The accuracies were 77.92%, 71.00%, and 83.12% for histological healing; and 74.35%, 72.51%, and 92.21% for complete mucosal healing (Mayo Endoscopic Score 0 vs 1-3), among senior endoscopists, junior endoscopists, and the ViT model, respectively. Conclusions:Our novel deep learning-based model, based on endoscopic videos, was comparable to that of experienced endoscopists and surpassed that of young endoscopists in predicting histological remission and complete mucosal healing.
Dermal fibroblasts are pivotal in maintaining skin integrity through extracellular matrix (ECM) production, a process compromised during aging due to oxidative stress from excessive reactive oxygen species (ROS). Salvia miltiorrhiza Bunge (Danshen, DS), a medicinal plant rich in bioactive compound-rich dried roots, has demonstrated broad therapeutic potential. This study investigates the anti-aging properties of Danshen callus (DSC)-an undifferentiated cell mass derived from leaf tissue culture, as a sustainable and controlled source of bioactive compounds. Using hydrogen peroxide (H2O2)-induced premature aging and chronological aging models in human dermal fibroblasts (HDFs), we evaluated DSC effects on redox homeostasis and senescence. Pretreatment and posttreatment with DSC significantly enhanced HDF viability, restored ECM synthesis, suppressed MMP-1 secretion, and reduced senescence-associated markers in H2O2-induced premature aging HDFs. Mechanistically, DSC activated the Nrf2/ARE pathway, mitigating ROS accumulation and reinforcing antioxidant defenses. Crucially, comparative analysis revealed DSC superior efficacy over native Danshen (DS) in both aging paradigms. These findings highlight DSC potential as a novel, plant-based therapeutic agent for anti-aging cosmetic formulations, leveraging agricultural waste for sustainable skincare solutions.
