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ObjectivesThis study quantified trends in the burden of lower respiratory infections from 1990 to 2023 in five East Asian countries, projected disease trajectories from 2024 to 2033 using machine learning, and identified key risk factors and their associations with the sociodemographic…
Breast cancer management still depends on early detection, risk assessment, and individualized treatment. Imaging is used throughout screening, diagnosis, treatment evaluation, and follow-up.
CONCLUSIONS: CTG-FRAME is designed to facilitate the escalation of clinical review rather than autonomous clinical action by defining a clear decision-analytic operating region and generating interpretable, context-linked evidence.
The recent rapid growth of wearable biometric technologies has enabled the continuous acquisition of physiological data. Clinicians now have access to a vast amount of data collected during their patients' daily lives.
Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely owing to profound therapeutic resistance driven by tumor heterogeneity, a highly immunosuppressive tumor microenvironment (TME), dense desmoplastic stroma, immune exclusion, and impaired antitumor immune…
A growing number of artificial intelligence (AI) systems for melanoma diagnosis now match dermatologist-level performance. Early detection is a driving factor, yet it is rarely validated beyond aggregate diagnostic accuracy.
CONCLUSION: Routine public health survey data can support the identification of latent adult subgroups associated with cognitive health-related vulnerability and provide exploratory evidence for population monitoring, priority screening, resource allocation, and stratified intervention design.
Artificial intelligence is increasingly being investigated as a tool for supporting personalized perioperative care. This narrative review, guided by the Scale for the Assessment of Narrative Review Articles principles, examines clinically relevant applications of artificial intelligence and…
CONCLUSIONS: The proposed method combined local CNN predictions into a registration-level μ score and showed strong performance on held-out and clinical cases.
We respond to concerns regarding fundamental data quality issues in datasets used for machine learning prediction of enzyme optimal pH (pHopt). We re-examined six questioned protein entries by tracing their annotated pH values to the original literature.
CONCLUSIONS: The findings of our study demonstrate the feasibility of applying sCT generated from MRI using a deep learning model to pelvic radiotherapy planning.
Automated segmentation of diabetic foot ulcers (DFUs) supports clinical diagnosis, treatment planning, and longitudinal wound monitoring, but remains difficult owing to the heterogeneous appearance, irregular morphology, and cluttered backgrounds of ulcers in clinical photographs.
CONCLUSIONS: The XGBoost model demonstrated the best predictive performance among the evaluated algorithms. The findings indicate that a substantial proportion of reproductive-age women experience barriers to health care access, although no formal subnational "extreme risk" classification was…
CONCLUSION: The DAPR-PSHO framework successfully addresses the limitations of global-feature-focused models by eff ectively isolating localized, early-stage abnormalities in perivascular zones, significantly improving RNV detection accuracy and anatomical fidelity.
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