AIMC Topic: Middle Aged

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Machine-learning modeL based on computed tomography body composition analysis for the estimation of resting energy expenditure: A pilot study.

Clinical nutrition ESPEN
BACKGROUND & AIMS: The assessment of resting energy expenditure (REE) is a challenging task with the current existing methods. The reference method, indirect calorimetry (IC), is not widely available, and other surrogates, such as equations and bioim...

Anatomical Considerations for Achieving Optimized Outcomes in Individualized Cochlear Implantation.

Otology & neurotology : official publication of the American Otological Society, American Neurotology Society [and] European Academy of Otology and Neurotology
HYPOTHESIS: Machine learning models can assist with the selection of electrode arrays required for optimal insertion angles.

Clinical validation of a proposed diagnostic classification for pulpitis.

International endodontic journal
AIM: Determine the reliability and clinical validity of the Wolters classification of pulpitis.

Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.

Academic radiology
RATIONALE AND OBJECTIVES: End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among ...

Deep learning dosiomics for the pretreatment prediction of radiation dermatitis in nasopharyngeal carcinoma patients treated with radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To develop a combined dosiomics and deep learning (DL) model for predicting radiation dermatitis (RD) of grade ≥ 2 in patients with nasopharyngeal carcinoma (NPC) after radiation therapy (RT) based on radiation dose distribution.

Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis.

Kidney international
BACKGROUND: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. Here, we investigated if computationally quantified tubular features can enhance prognostication and reveal sp...

Deep learning Radiopathomics based on pretreatment MRI and whole slide images for predicting overall survival in locally advanced nasopharyngeal carcinoma.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To develop an integrative radiopathomic model based on deep learning to predict overall survival (OS) in locally advanced nasopharyngeal carcinoma (LANPC) patients.

Deep-learning based automated pancreas segmentation on CT scans of chronic pancreatitis patients.

European journal of radiology
OBJECTIVES: This study aimed to develop an artificial intelligence (AI)-based segmentation model for accurate delineation of the complex pancreas in patients with chronic pancreatitis (CP) using computer tomography (CT) scans obtained during routine ...

Associations of the Hs-CRP/HDL-C ratio with stroke among US adults: Evidence from NHANES 2015-2018.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND: The high-sensitivity C-reactive protein (Hs-CRP)-to-high-density lipoprotein cholesterol (HDL-C) ratio, which integrates insights into inflammation and lipid metabolism, serves as a comprehensive indicator. The association between this ra...

Comparison of clinical, radiomics, deep learning, and fusion models for predicting early recurrence in locally advanced rectal cancer based on multiparametric MRI: a multicenter study.

European journal of radiology
OBJECTIVE: Predicting early recurrence (ER) in locally advanced rectal cancer (LARC) is critical for clinical decision-making. This study aimed at comparing clinical, deep learning (DL), radiomics, and two fusion models for ER prediction based on mul...