AIMC Topic: Deep Learning

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Development of MRI-Based Deep Learning Signature for Prediction of Axillary Response After NAC in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To develop a MRI-based deep learning signature for predicting axillary response after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients.

Improving accuracy of early dental carious lesions detection using deep learning-based automated method.

Clinical oral investigations
OBJECTIVE: To investigate the effectiveness of a convolutional neural network (CNN) in detecting healthy teeth and early carious lesions on occlusal surfaces and to assess the applicability of this deep learning algorithm as an auxiliary aid.

Multifunctional, Self-Adhesive MXene-Based Hydrogel Flexible Strain Sensors for Hand-Written Digit Recognition with Assistance of Deep Learning.

Langmuir : the ACS journal of surfaces and colloids
The conductive hydrogel as a flexible sensor not only has certain mechanical flexibility but also can be used in the field of human health detection and human-computer interaction. Herein, by introduction of tannic acid (TA) with MXene into the polya...

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Sensors (Basel, Switzerland)
Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of c...

MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis.

Scientific reports
The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood test have been demonstrated to have predictive value on Coronavirus Disease 2019 (COVID-19) diagnos...

Artificial Intelligence-Assisted Sac Diameter Assessment for Complex Endovascular Aortic Repair.

Journal of endovascular therapy : an official journal of the International Society of Endovascular Specialists
PURPOSE: Artificial intelligence (AI) using an automated, deep learning-based method, Augmented Radiology for Vascular Aneurysm (ARVA), has been verified as a viable aide in aneurysm morphology assessment. The aim of this study was to evaluate the ac...

Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography.

Academic radiology
RATIONALE AND OBJECTIVES: Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count i...

Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data.

Computer methods and programs in biomedicine
BACKGROUND: White matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted wi...

Vascular Age Assessed From an Uncalibrated, Noninvasive Pressure Waveform by Using a Deep Learning Approach: The AI-VascularAge Model.

Hypertension (Dallas, Tex. : 1979)
BACKGROUND: Aortic stiffness, assessed as carotid-femoral pulse wave velocity, provides a measure of vascular age and risk for adverse cardiovascular disease outcomes, but it is difficult to measure. The shape of arterial pressure waveforms conveys i...

Deep learning and radiomics of longitudinal CT scans for early prediction of tuberculosis treatment outcomes.

European journal of radiology
BACKGROUND: To predict tuberculosis (TB) treatment outcomes at an early stage, prevent poor outcomes ofdrug-resistant tuberculosis(DR-TB) and interrupt transmission.