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Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.

Academic radiology
RATIONALE AND OBJECTIVES: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat of the tumor and its peritumoral microenvironment to predict progression-free survival (PFS) in patie...

Deep Learning-Based Three-Dimensional Analysis Reveals Distinct Patterns of Condylar Remodelling After Orthognathic Surgery in Skeletal Class III Patients.

Orthodontics & craniofacial research
OBJECTIVE: This retrospective study aimed to evaluate morphometric changes in mandibular condyles of patients with skeletal Class III malocclusion following two-jaw orthognathic surgery planned using virtual surgical planning (VSP) and analysed with ...

Cognitive load detection through EEG lead wise feature optimization and ensemble classification.

Scientific reports
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying featur...

Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.

Scientific reports
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindicati...

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.

Scientific reports
Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely identification of potential populations of OA. This allows for further diagnosis and intervention, wh...

Prediction of pulmonary embolism by an explainable machine learning approach in the real world.

Scientific reports
In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this...

What is the influence of psychosocial factors on artificial intelligence appropriation in college students?

BMC psychology
BACKGROUND: In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian univers...

Forecasting cardiovascular disease mortality using artificial neural networks in Sindh, Pakistan.

BMC public health
Cardiovascular disease (CVD) is a leading cause of death and disability worldwide, and its incidence and prevalence are increasing in many countries. Modeling of CVD plays a crucial role in understanding the trend of CVD death cases, evaluating the e...

Preoperative prediction of the selection of the NOTES approach for patients with symptomatic simple renal cysts via an interpretable machine learning model: a retrospective study of 264 patients.

Langenbeck's archives of surgery
BACKGROUND: There are multiple surgical approaches for treating symptomatic simple renal cysts (SSRCs). The natural orifice transluminal endoscopic surgery (NOTES) approach has gradually been applied as an emerging minimally invasive approach for the...