AIMC Topic: Liver
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Machine Learning Approaches to Predict Hepatotoxicity Risk in Patients Receiving Nilotinib.
Molecules (Basel, Switzerland)
May 31, 2021
Although nilotinib hepatotoxicity can cause severe clinical conditions and may alter treatment plans, risk factors affecting nilotinib-induced hepatotoxicity have not been investigated. This study aimed to elucidate the factors affecting nilotinib-i...
Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model.
European radiology
May 20, 2021
OBJECTIVES: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation...
Exploring the relationship between the dielectric properties and viability of human normal hepatic tissues from 10 Hz to 100 MHz based on grey relational analysis and BP neural network.
Computers in biology and medicine
May 15, 2021
Liver is an important parenchyma organ, and its tissue viability plays an important role in liver transplantation and liver ischemic injury assessment. Dielectric property is a useful biophysical feature that provides insights into the structure and ...
Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks.
Artificial intelligence in medicine
May 14, 2021
Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. Th...
Which GAN? A comparative study of generative adversarial network-based fast MRI reconstruction.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
May 10, 2021
Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate motion artefacts and increase patient throughput. -space undersampling is an obvious approach to accelerate MR acquisition. However, undersampling of -space...
Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.
European radiology
May 7, 2021
OBJECTIVE: To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T...
Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique.
Magnetic resonance imaging
May 7, 2021
PURPOSE: To evaluate the feasibility of High-resolution (HR) magnetic resonance imaging (MRI) of the liver using deep learning reconstruction (DLR) based on a deep learning denoising technique compared with standard-resolution (SR) imaging.
Application of Supervised SOM Algorithms in Predicting the Hepatotoxic Potential of Drugs.
International journal of molecular sciences
Apr 24, 2021
The hepatotoxic potential of drugs is one of the main reasons why a number of drugs never reach the market or have to be withdrawn from the market. Therefore, the evaluation of the hepatotoxic potential of drugs is an important part of the drug devel...
[Minimally invasive liver surgery].
Der Chirurg; Zeitschrift fur alle Gebiete der operativen Medizen
Mar 30, 2021
Minimally invasive liver surgery is safe and can be performed with results practically equal to those in open surgery. There are different techniques of parenchyma dissection and hemostasis available for the safe performance of minor and major resect...
Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.
BMC cancer
Mar 16, 2021
BACKGROUND: The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural n...
Proportional Hazards Models
Carcinoma, Hepatocellular
Humans
Postoperative Period
Nomograms
Male
Neural Networks, Computer
Neoplasm Recurrence, Local
Follow-Up Studies
Middle Aged
Retrospective Studies
Liver
Disease-Free Survival
Liver Neoplasms
Female
Predictive Value of Tests
Hepatectomy
Risk Factors
Neoplasm Staging
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