AIMC Topic: Deep Learning

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Improved detection of air trapping on expiratory computed tomography using deep learning.

PloS one
BACKGROUND: Radiologic evidence of air trapping (AT) on expiratory computed tomography (CT) scans is associated with early pulmonary dysfunction in patients with cystic fibrosis (CF). However, standard techniques for quantitative assessment of AT are...

A hybrid deep learning technology for PM air quality forecasting.

Environmental science and pollution research international
The concentration of PM is one of the main factors in evaluating the air quality in environmental science. The severe level of PM directly affects the public health, economics and social development. Due to the strong nonlinearity and instability of ...

Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists.

Computers in biology and medicine
UNLABELLED: A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading system.

Predicting drug release from diazepam FDM printed tablets using deep learning approach: Influence of process parameters and tablet surface/volume ratio.

International journal of pharmaceutics
The aim of this study was to apply artificial neural networks as deep learning tools in establishing a model for understanding and prediction of diazepam release from fused deposition modeling (FDM) printed tablets. Diazepam printed tablets of variou...

Added Value of Deep Learning-based Detection System for Multiple Major Findings on Chest Radiographs: A Randomized Crossover Study.

Radiology
Background Previous studies assessing the effects of computer-aided detection on observer performance in the reading of chest radiographs used a sequential reading design that may have biased the results because of reading order or recall bias. Purpo...

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Radiology
Background Conventional radiologic modalities perform poorly in the radiated rectum and are often unable to differentiate residual cancer from treatment scarring. Purpose To report the development and initial patient study of an imaging system compri...

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures.

Sensors (Basel, Switzerland)
Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This f...

Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging.

Scientific reports
The goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previous...

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.

BMC medicine
BACKGROUND: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals...