AIMC Topic: Aged

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Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features.

GeroScience
To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A r...

Combining deep neural networks, a rule-based expert system and targeted manual coding for ICD-10 coding causes of death of French death certificates from 2018 to 2019.

International journal of medical informatics
OBJECTIVE: For ICD-10 coding causes of death in France in 2018 and 2019, predictions by deep neural networks (DNNs) are employed in addition to fully automatic batch coding by a rule-based expert system and to interactive coding by the coding team fo...

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

International journal of medical informatics
OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF.

Predicting chronic kidney disease progression with artificial intelligence.

BMC nephrology
BACKGROUND: The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validat...

Artificial Intelligence-Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Quantification of total cardiovascular risk is essential for individualizing hypertension treatment. This study aimed to develop and validate a novel, machine-learning-derived model to predict cardiovascular mortality risk using office bl...

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

Academic radiology
RATIONALE AND OBJECTIVES: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to uti...

Deep learning-accelerated T2WI: image quality, efficiency, and staging performance against BLADE T2WI for gastric cancer.

Abdominal radiology (New York)
PURPOSE: The purpose of our study is to investigate image quality, efficiency, and diagnostic performance of a deep learning-accelerated single-shot breath-hold (DLSB) against BLADE for T-weighted MR imaging (TWI) for gastric cancer (GC).

A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or the...

Predicting osteoporosis from kidney-ureter-bladder radiographs utilizing deep convolutional neural networks.

Bone
Osteoporosis is a common condition that can lead to fractures, mobility issues, and death. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis, it is expensive and not widely available. In contrast, kidney-ureter-bla...

Risk Classification for Interstitial Cystitis/Bladder Pain Syndrome Using Machine Learning Based Predictions.

Urology
OBJECTIVE: To improve diagnosis of interstitial cystitis (IC)/bladder pain syndrome(IC) we hereby developed an improved IC risk classification using machine learning algorithms.