AIMC Topic: Deep Learning

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Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.

PloS one
BACKGROUND: Identification of accelerated aging and its biomarkers can lead to more timely therapeutic interventions and decision-making. Therefore, we sought to predict aging-related slow gait, a known predictor of accelerated aging, and its determi...

The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography.

European journal of radiology
PURPOSE: To evaluate the value of deep learning image reconstruction (DLIR) in improving image quality of virtual non-hydroxyapatite (VNHAP) and virtual monoenergetic images (VMIs), and radiologists' performance in detecting acute vertebral compressi...

Classification of glioma grade and Ki-67 level prediction in MRI data: A SHAP-driven interpretation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and...

The continuous evolution of biomolecular force fields.

Structure (London, England : 1993)
Biomolecular force fields have continuously evolved to improve their accuracy and broaden their applications in biological and therapeutic discoveries. The rapid adaptation of advanced computational technology, in particular the recent deep learning ...

Rate of brain aging associates with future executive function in Asian children and older adults.

eLife
Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore...

Fast and accurate lung cancer subtype classication and localization based on Intraoperative frozen sections of lung adenocarcinoma.

Biomedical physics & engineering express
Current lung cancer diagnostic techniques primarily focus on tissue subtype classification, yet remain inadequate in distinguishing pathological progression subtypes (particularly between adenocarcinomaand invasive adenocarcinoma) on frozen sections....

MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.

Physics in medicine and biology
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...

Application Value of Deep Learning-Based AI Model in the Classification of Breast Nodules.

British journal of hospital medicine (London, England : 2005)
Breast nodules are highly prevalent among women, and ultrasound is a widely used screening tool. However, single ultrasound examinations often result in high false-positive rates, leading to unnecessary biopsies. Artificial intelligence (AI) has dem...

FFLUNet: Feature Fused Lightweight UNet for brain tumor segmentation.

Computers in biology and medicine
Brain tumors, particularly glioblastoma multiforme, are considered one of the most threatening types of tumors in neuro-oncology. Segmenting brain tumors is a crucial part of medical imaging. It plays a key role in diagnosing conditions, planning tre...

FDTooth: Intraoral Photographs and CBCT Images for Fenestration and Dehiscence Detection.

Scientific data
Fenestration and dehiscence (FD) pose significant challenges in dental treatments as they adversely affect oral health. Although cone-beam computed tomography (CBCT) provides precise diagnostics, its extensive time requirements and radiation exposure...