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Fully automated evaluation of condylar remodeling after orthognathic surgery in skeletal class II patients using deep learning and landmarks.

Journal of dentistry
OBJECTIVE: Condylar remodeling is a key prognostic indicator in maxillofacial surgery for skeletal class II patients. This study aimed to develop and validate a fully automated method leveraging landmark-guided segmentation and registration for effic...

Assessing real-life food consumption in hospital with an automatic image recognition device: A pilot study.

Clinical nutrition ESPEN
BACKGROUND AND AIMS: Accurate dietary intake assessment is essential for nutritional care in hospitals, yet it is time-consuming for caregivers and therefore not routinely performed. Recent advancements in artificial intelligence (AI) offer promising...

Portable Ultrasound Bladder Volume Measurement Over Entire Volume Range Using a Deep Learning Artificial Intelligence Model in a Selected Cohort: A Proof of Principle Study.

Neurourology and urodynamics
OBJECTIVE: We aimed to prospectively investigate whether bladder volume measured using deep learning artificial intelligence (AI) algorithms (AI-BV) is more accurate than that measured using conventional methods (C-BV) if using a portable ultrasound ...

Infrared and Raman spectroscopy of blood plasma for rapid endometrial cancer detection.

British journal of cancer
BACKGROUND: Endometrial cancer (EC) is the 6th most common cancer among women worldwide. No effective non-invasive screening methods or approved blood biomarkers for EC exist. Previous research explored Attenuated Total Reflection-Fourier Transform I...

The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of individuals at heightened risk of such complications or their exacerbation can be crucial to set a co...

Application of Quantitative CT and Machine Learning in the Evaluation and Diagnosis of Polymyositis/Dermatomyositis-Associated Interstitial Lung Disease.

Academic radiology
RATIONALE AND OBJECTIVES: To investigate lung changes in patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) using quantitative CT and to construct a diagnostic model to evaluate the application of quantitative...

Diagnosis of thyroid cartilage invasion by laryngeal and hypopharyngeal cancers based on CT with deep learning.

European journal of radiology
OBJECTIVES: To develop a convolutional neural network (CNN) model to diagnose thyroid cartilage invasion by laryngeal and hypopharyngeal cancers observed on computed tomography (CT) images and evaluate the model's diagnostic performance.

Perception and sentiment analysis of palliative care in Chinese social media: Qualitative studies based on machine learning.

Social science & medicine (1982)
BACKGROUND: Traditional Chinese culture makes death a sensitive and taboo topic, leading patients and family members to refuse to choose palliative care.

Prediction of operation time in percutaneous nephrolithotomy (PCNL) patients: A machine learning approach.

Urologia
PURPOSE: To investigate the factors influencing the length of percutaneous nephrolithotomy (PCNL) procedures and identify predictive variables for operation time using machine learning models.

Evaluating prediction of short-term tolerability of five type 2 diabetes drug classes using routine clinical features: UK population-based study.

Diabetes, obesity & metabolism
AIMS: A precision medicine approach in type 2 diabetes (T2D) needs to consider potential treatment risks alongside established benefits for glycaemic and cardiometabolic outcomes. Considering five major T2D drug classes, we aimed to describe variatio...