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1382 published stories · page 53 of 93

Sleep Diagnostics and Monitoring Technology in Obstructive Sleep Apnea
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Sleep Diagnostics and Monitoring Technology in Obstructive Sleep Apnea

Objective This article helps neurologists understand modern approaches to diagnosing obstructive sleep apnea, including the clinical role and limitations of home sleep apnea testing, and learn how they can integrate wearable and noncontact technologies into patient care to improve diagnostic efficiency, monitor treatment, and reduce health disparities. Latest developments Advances in home sleep apnea testing have expanded beyond traditional type III monitors to include wearable devices such as wrist sensors and…

Continuum
Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis
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Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis

Objective Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers. Methods A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligi…

Functional outcome prediction after traumatic cervical spinal cord injury using ensemble machine learning: a three‑center validation study
Evidence-backed gain

Functional outcome prediction after traumatic cervical spinal cord injury using ensemble machine learning: a three‑center validation study

Background Traumatic cervical spinal cord injury (TCSCI) often causes severe neurological dysfunction. Accurate prediction of functional recovery is essential for clinical decision‑making and rehabilitation planning. Objective To develop an ensemble learning model integrating baseline clinical data, neurological assessments, and cervical MRI features to predict neurological recovery and functional outcomes at one year post‑injury in TCSCI patients. Methods We retrospectively collected data from 410 TCSCI patient…

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review
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Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer-reviewed studies applying DL to predict KOA progression from medical imaging. Two reviewers independently screened studies, extracted data, and assessed risk of bias using PROBAST-AI. The primary outcome was…

Health

Establishing diagnostic thresholds for adult ADHD screening in taxi drivers: validation of the CAARS-S via Item Response Theory and Machine Learning

Background Adult Attention-Deficit/Hyperactivity Disorder (ADHD) is under-recognized in professional drivers, yet it poses significant safety risks. The Conners' Adult ADHD Rating Scale Short Version (CAARS-S:SV) lacks empirically validated cutoff scores for occupational screening in Iran. This study aimed to assess the psychometric properties of the Persian CAARS-S:SV in taxi drivers and to determine optimal ADHD diagnostic thresholds using both traditional and machine-learning methods. Methods A sample of 298…

Establishing diagnostic thresholds for adult ADHD screening in taxi drivers: validation of the CAARS-S via Item Response Theory and Machine Learning
Climate

Coupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions

Background Accurate prediction of apple fruit maturity date is essential for optimizing harvest timing, fruit quality and market value under climate change. However, process-based crop models often show limited performance when extrapolated across large spatial scales, whereas machine learning models lack physiological interpretability. To address these limitations, this study has developed a hybrid framework integrating the process-based STICS model with machine learning approaches across China's apple planting…

Coupling machine learning with a biophysical model for maturity date prediction of apple fruit across China's apple planting regions
Health

Exploratory machine learning-based early post-treatment assessment of willingness to reuse rubber dam isolation after microscopic root canal treatment

ObjectiveTo explore factors associated with willingness to reuse rubber dam isolation after microscopic root canal treatment and develop an exploratory machine learning-based early post-treatment assessment model.MethodsThis retrospective cross-sectional study included 306 patients who underwent microscopic root canal treatment with rubber dam isolation from May 2025 to November 2025. The outcome was the willingness to reuse rubber dam isolation at the 1-week follow-up. Forty-seven newly enrolled patients were r…

Exploratory machine learning-based early post-treatment assessment of willingness to reuse rubber dam isolation after microscopic root canal treatment
Health

From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024)

Background The diagnosis of attention deficit hyperactivity disorder (ADHD) has traditionally relied on subjective clinical interviews. Recent years have witnessed a paradigm shift toward objective, data-driven diagnostics powered by artificial intelligence (AI). Objective This study provides a comprehensive bibliometric review of AI and machine learning (ML) applications in ADHD prediction to map the field's evolution, current trends, and future directions. Methods A structured search of the Scopus database ret…

From machine learning to deep learning in attention deficit hyperactivity disorder diagnosis: A bibliometric analysis of global trends (2011-2024)