Menopause

Assessing Disorders of Consciousness Using Temporal Sleep Dynamics Extracted From Whole-Night PSG.

Accurate assessment of patients with disorders of consciousness (DoC) remains a major clinical challenge due to the limitations of behavior-based evaluations and task-dependent neurophysiological paradigms. Whole-night polysomnography (PSG), a passive and noninvasive monitoring tool, offers unique potential for revealing residual brain function during sleep. In this study, we propose a temporal-dy...

Mandibular Distraction Osteogenesis as Paradigm Shift for Treatment of Tongue-Based Obstructive Apnea: Results of a 16-Year, Hospital-Wide Intervention Trial.

BACKGROUND: Mandibular distraction osteogenesis (MDO) has emerged as the preferred surgical treatment for neonatal tongue-based airway obstruction (TBAO), yet comprehensive outcomes data comparing surgical and non-surgical approaches remain limited. METHODS: We conducted a retrospective cohort study of 579 patients with congenital micrognathia from 2007-2023 at a single tertiary pediatric care ins...

A metabolomics-driven machine learning model for osteoporosis risk prediction.

Osteoporosis, marked by decreased bone mineral density (BMD), poses a major public health concern by increasing fracture risk, lowering quality of lif...

Portable microrobotic platform for non-prehensile mechanophenotyping of biopsy-derived human cancer tissues.

Mechanical characterization of cancer tissues is crucial for understanding tumor progression and response to therapy. However, common mechanophenotypi...

Use of artificial intelligence and radiomics for diagnosis and prediction of osteoporotic fractures: a systematic review.

AIM: Osteoporosis (OP) is a prevalent metabolic bone disease causing millions of fractures annually, leading to significant healthcare and economic bu...

Machine learning and the role of the vaginal and fecal microbiome in miscarriage: a matched case-control study.

Miscarriage occurs in approximately 15% of all pregnancies, and recent studies have suggested a potential role of the microbiome. A nested case-contro...

Advanced Prompting Techniques Informed by Clinical Expertise Improve the Accuracy of LLM Data Extraction but Increase Non-Determinism.

Prompt engineering techniques which aid in the use of generative artificial intelligence to address classification tasks have expanded considerably in...

An interpretable nomogram for predicting sleep disturbance risk in maintenance hemodialysis patients.

BACKGROUND: This study aimed to develop and validate an interpretable nomogram to predict the risk of sleep disturbance in maintenance hemodialysis (M...

Molecular-level understanding of the aging bone and regeneration mechanisms using computational methods.

Bone degeneration diseases, such as osteoporosis, are skeletal disorders characterized by diminished bone mass and increased susceptibility to fractur...

Clinical implementation of AI for vertebral fracture detection in CT aligned with fracture liaison services: high prevalence of undiagnosed vertebral fractures.

UNLABELLED: We assessed feasibility and effectiveness of AI-based VF screening in CT, integrated with a local FLS. The system identified VFs in 14% of...

Functional brain network modulation following conventional and optimized HD-tDCS in major depressive disorder: A machine learning prediction of treatment response.

BACKGROUND: Major depressive disorder (MDD) is a prevalent and disabling condition that remains inadequately treated in many patients. Transcranial di...

Predictive biomarkers of response to chimeric antigen receptor (CAR) T-cell therapy for pan-haematologic cancer.

Chimeric antigen receptor (CAR) T-cell therapy holds great promise for patients with cancer, and the identification of predictive biomarkers is crucia...

Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.

Volumetric-modulated arc therapy (VMAT) planning for locally advanced non-small cell lung cancer (NSCLC) is an iterative and planner-dependent process...

The application of machine learning-assisted serum SERS technology in the early screening and prognosis evaluation of osteoporosis.

Early detection, early intervention, early treatment, and timely prognostic monitoring of osteoporosis are crucial for improving patients' quality of ...

Artificial intelligence for accurate tumor size assessment and non-invasive adenocarcinoma prediction in small-sized lung cancer.

INTRODUCTION: Accurate preoperative imaging is essential for improving the treatment of small lung cancers. Precise identification of non-invasive ade...

[Deep learning-based assessment of periodontal ligament area changes in maxillary central incisors under different orthodontic regimens using cone-beam CT images].

Objective: To investigate the differences in the changes of periodontal ligament area (PDLA) and related clinical indicators before and after maxillar...

A data-centric approach to detecting and mitigating demographic bias in pediatric mental health text.

BACKGROUND: Healthcare Artificial Intelligence (AI) offers transformative potential but often inherits biases from training data, worsening disparitie...

Machine learning model integrating oral microbiota and clinical features for predicting osteoporosis and bone loss in high-altitude populations.

Noninvasive Blood-Based Detection of Endometriosis Can Improve Standard-of-Care by Facilitating Early Diagnosis and Clinical Management Among Symptomatic Women.

STUDY OBJECTIVE: To develop and validate a non-invasive, blood-based diagnostic assay for endometriosis that performs accurately across menstrual cycl...

Flow by design: a guided review of microfluidics for wearable biosensors.

The integration of microfluidics into wearable biosensors has enabled real-time, non-invasive access to physiological information through biofluids su...