AIMC Topic: Deep Learning

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Predicting prognosis of nasopharyngeal carcinoma based on deep learning: peritumoral region should be valued.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: The purpose of this study was to explore whether incorporating the peritumoral region to train deep neural networks could improve the performance of the models for predicting the prognosis of NPC.

Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study.

Endoscopy
BACKGROUND : Deep learning models have previously been established to predict the histopathology and invasion depth of gastric lesions using endoscopic images. This study aimed to establish and validate a deep learning-based clinical decision support...

Visual classification of pressure injury stages for nurses: A deep learning model applying modern convolutional neural networks.

Journal of advanced nursing
AIMS: To develop a deep learning model for pressure injury stages classification based on real-world photographs and compare its performance with that of clinical nurses to seek the opportunity of its application in clinical settings.

Detection of aspiration from images of a videofluoroscopic swallowing study adopting deep learning.

Oral radiology
OBJECTIVES: A videofluoroscopic swallowing study (VFSS) is conducted to detect aspiration. However, aspiration occurs within a short time and is difficult to detect. If deep learning can detect aspirations with high accuracy, clinicians can focus on ...

Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.

JACC. Cardiovascular imaging
BACKGROUND: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been ...

Development of a deep learning-based tool to assist wound classification.

Journal of plastic, reconstructive & aesthetic surgery : JPRAS
This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely deep wound, infected wound, arterial wound, venous wound, and pressur...

Automatic assessment of pain based on deep learning methods: A systematic review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The automatic assessment of pain is vital in designing optimal pain management interventions focused on reducing suffering and preventing the functional decline of patients. In recent years, there has been a surge in the ado...

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals.

Computers in biology and medicine
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess t...

Synthetic cranial MRI from 3D optical surface scans using deep learning for radiation therapy treatment planning.

Physical and engineering sciences in medicine
BACKGROUND: Optical scanning technologies are increasingly being utilised to supplement treatment workflows in radiation oncology, such as surface-guided radiotherapy or 3D printing custom bolus. One limitation of optical scanning devices is the abse...

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning.

Scientific reports
Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age assessment can be performed using CT scout views from...