AIMC Topic: Middle Aged

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External validation of deep learning-derived 18F-FDG PET/CT delta biomarkers for loco-regional control in head and neck cancer.

Acta oncologica (Stockholm, Sweden)
BACKGROUND AND PURPOSE: Delta biomarkers that reflect changes in tumour burden over time can support personalised follow-up in head and neck cancer. However, their clinical use can be limited by the need for manual image segmentation. This study exte...

Distinct 3-Dimensional Morphologies of Arthritic Knee Anatomy Exist: CT-Based Phenotyping Offers Outlier Detection in Total Knee Arthroplasty.

The Journal of bone and joint surgery. American volume
BACKGROUND: There is no foundational classification that 3-dimensionally characterizes arthritic anatomy to preoperatively plan and postoperatively evaluate total knee arthroplasty (TKA). With the advent of computed tomography (CT) as a preoperative ...

Effects of attractions and social attributes on peoples' usage intention and media dependence towards chatbot: The mediating role of parasocial interaction and emotional support.

BMC psychology
PURPOSE: It is important to explore the relationship between humans and chatbots to improve human-robot interaction in the era of artificial intelligence. This study aims to explore the effects of attractions and social attributes of chatbots on user...

Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study aimed to develop a predictive model integrating multi-sequence MRI radiomics, deep learning features, and habitat imaging to forecast pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant therapy...

Clinical-transcriptomic classification of lumbar disc degeneration enhanced by machine learning.

Military Medical Research
BACKGROUND: Lumbar disc degeneration (LDD) displays considerable heterogeneity in terms of clinical features and pathological changes. However, researchers have not clearly determined whether the transcriptome variations in LDD could be used to ident...

Pulse wave-driven machine learning for the non-invasive assessment of coronary artery calcification in patients with end-stage renal disease undergoing hemodialysis.

Biomedical engineering online
BACKGROUND: Coronary artery calcification (CAC) represents a major cardiovascular risk in patients with end-stage renal disease (ESRD) undergoing hemodialysis. Given that radial artery pulse waveforms can reflect vascular status, this study aimed to ...

Integrating multiple feature assessment methods to identify key predictors of repeat suicide attempts in Taiwan.

BMC psychiatry
BACKGROUND: The high rate of repeat attempts among individuals who have previously attempted suicide presents a critical challenge in public health and suicide prevention. While early and targeted intervention is crucial for this high-risk group, eff...

DCNN models with post-hoc interpretability for the automated detection of glossitis and OSCC on the tongue.

Scientific reports
This study aimed to develop and evaluate deep convolutional neural network (DCNN) models with Grad-CAM visualization for the automated classification with interpretability of tongue conditions-specifically glossitis and oral squamous cell carcinoma (...

Adipose tissue gene expression and longitudinal clinical phenotypes are early biomarkers of lipid-regulating drug usage.

Scientific reports
Cardiovascular disease progression is characterised by the dysregulation of lipid metabolism and pro-atherogenic effects of adipose tissue signalling. Recent findings from the analysis of transcriptomic data in bulk tissue has enabled these insights ...

Synthetic data generation method improves risk prediction model for early tumor recurrence after surgery in patients with pancreatic cancer.

Scientific reports
Pancreatic cancer is aggressive with high recurrence rates, necessitating accurate prediction models for effective treatment planning, particularly for neoadjuvant chemotherapy or upfront surgery. This study explores the use of variational autoencode...