AIMC Topic: Machine Learning

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Prediction of 6-Mo Poststroke Spasticity in Patients With Acute First-Ever Stroke by Machine Learning.

American journal of physical medicine & rehabilitation
OBJECTIVE: Poststroke spasticity reduces arm function and leads to low levels of independence. This study suggested applying machine learning from routinely available data to support the clinical management of poststroke spasticity.

Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment.

The Journal of arthroplasty
BACKGROUND: Osteophytes are commonly used to diagnose and guide knee osteoarthritis (OA) treatment, but their causes are unclear. Although they are not typically the focus of knee arthroplasty surgeons, they can predict case difficulty and length. Fu...

Artificial Intelligence in Laryngology, Broncho-Esophagology, and Sleep Surgery.

Otolaryngologic clinics of North America
Technological advancements in laryngology, broncho-esophagology, and sleep surgery have enabled the collection of increasing amounts of complex data for diagnosis and treatment of voice, swallowing, and sleep disorders. Clinicians face challenges in ...

Artificial intelligence as a prediction tool for orthognathic surgery assessment.

Orthodontics & craniofacial research
INTRODUCTION: An ideal orthodontic treatment involves qualitative and quantitative measurements of dental and skeletal components to evaluate patients' discrepancies, such as facial, occlusal, and functional characteristics. Deciding between orthodon...

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity.

Journal of computational chemistry
The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key c...

New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers.

Journal of stomatology, oral and maxillofacial surgery
This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (...

RSV Severe Infection Risk Stratification in a French 5-Year Birth Cohort Using Machine-learning.

The Pediatric infectious disease journal
BACKGROUND: Respiratory syncytial virus (RSV) poses a substantial threat to infants, often leading to challenges in hospital capacity. With recent pharmaceutical developments to be used during the prenatal and perinatal periods aimed at decreasing th...

Pain prediction model based on machine learning and SHAP values for elders with dementia in Taiwan.

International journal of medical informatics
INTRODUCTION: Pain conditions are common in elderly individuals, including those with dementia. However, symptoms associated with dementia may lead to poor recognition, assessment and management of pain. In this study, we incorporated the variables b...

A machine learning personalized treatment rule to optimize assignment to psychotherapies for grief among veterans.

Journal of affective disorders
BACKGROUND: Complex grief patterns are associated with significant suffering, functional impairments, health and mental health problems, and increased healthcare use. This burden may be even more pronounced among veterans. Behavioral Activation and T...

Rapid multi-catheter segmentation for magnetic resonance image-guided catheter-based interventions.

Medical physics
BACKGROUND: Magnetic resonance imaging (MRI) is the gold standard for delineating cancerous lesions in soft tissue. Catheter-based interventions require the accurate placement of multiple long, flexible catheters at the target site. The manual segmen...