AIMC Topic: Deep Learning

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Development and validation of a combined nomogram model based on deep learning contrast-enhanced ultrasound and clinical factors to predict preoperative aggressiveness in pancreatic neuroendocrine neoplasms.

European radiology
OBJECTIVES: This study aimed to develop and validate a combined nomogram model based on deep learning (DL) contrast-enhanced ultrasound (CEUS) and clinical factors to preoperatively predict the aggressiveness of pancreatic neuroendocrine neoplasms (P...

Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol.

European radiology
OBJECTIVES: The aim of this study was to evaluate the image quality and diagnostic performance of a deep-learning (DL)-accelerated two-dimensional (2D) turbo spin echo (TSE) MRI of the knee at 1.5 and 3 T in clinical routine in comparison to standard...

Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: Several algorithms have been proposed for differentiating the right and left outflow tracts (RVOT/LVOT) arrhythmia origins from 12-lead electrocardiograms (ECGs); however, the procedure is complicated. A deep learning (DL) model, a form o...

A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach.

Computers in biology and medicine
Lymphomas, or cancers of the lymphatic system, account for around half of all blood cancers diagnosed each year. Lymphoma is a condition that is difficult to diagnose, and accurate diagnosis is critical for effective treatment. Manual microscopic ana...

A Single Model Deep Learning Approach for Alzheimer's Disease Diagnosis.

Neuroscience
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients' life. The emerging computer-aided diagnostic meth...

Novel computer aided diagnostic models on multimodality medical images to differentiate well differentiated liposarcomas from lipomas approached by deep learning methods.

Orphanet journal of rare diseases
BACKGROUND: Deep learning methods have great potential to predict tumor characterization, such as histological diagnosis and genetic aberration. The objective of this study was to evaluate and validate the predictive performance of multimodality imag...

Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC.

Frontiers in immunology
BACKGROUND: Programmed death-ligand 1 (PD-L1) assessment of lung cancer in immunohistochemical assays was only approved diagnostic biomarker for immunotherapy. But the tumor proportion score (TPS) of PD-L1 was challenging owing to invasive sampling a...

Evaluation Analysis of the Nephrotoxicity of Preparations with CONSORT Harms Statement Based on Deep Learning.

Journal of healthcare engineering
In this paper, the safety of polyglycoside (TW) preparation was evaluated by combining literature research and evidence-based evaluation research, so as to provide evidence-based safety information of polyglycoside preparation (nephroptosis) for go...

Evaluation of market risk and resource allocation ability of green credit business by deep learning under internet of things.

PloS one
The research expects to evaluate the capital market risk and resource allocation ability of green credit business exploration based on neural network algorithm by deep learning in the context of the Internet of things, increase the funds flowing to g...

Assessment of deep convolutional neural network models for mandibular fracture detection in panoramic radiographs.

International journal of oral and maxillofacial surgery
The aim of this study was to develop automated models for the identification and detection of mandibular fractures in panoramic radiographs using convolutional neural network (CNN) algorithms. A total of 1710 panoramic radiograph images from the year...