AIMC Topic: Liver
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Artificial intelligence in precision medicine in hepatology.
Journal of gastroenterology and hepatology
Mar 1, 2021
The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline-specific therapy toward patient-specific precision medicine. The multiparametric and large detailed information necessitates novel anal...
Prediction of impacts on liver enzymes from the exposure of low-dose medical radiations through artificial intelligence algorithms.
Revista da Associacao Medica Brasileira (1992)
Feb 1, 2021
OBJECTIVES: This study aimed to develop artificial intelligence and machine learning-based models to predict alterations in liver enzymes from the exposure of low annual average effective doses in radiology and nuclear medicine personnel of Institute...
Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study.
The Lancet. Digital health
Feb 1, 2021
BACKGROUND: Ocular changes are traditionally associated with only a few hepatobiliary diseases. These changes are non-specific and have a low detection rate, limiting their potential use as clinically independent diagnostic features. Therefore, we ai...
Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.
Journal of X-ray science and technology
Jan 1, 2021
OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with usi...
Single-cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs.
Blood
Dec 17, 2020
Hematopoietic stem and progenitor cells (HSPCs) develop in distinct waves at various anatomical sites during embryonic development. The in vitro differentiation of human pluripotent stem cells (hPSCs) recapitulates some of these processes; however, i...
Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides.
Hepatology (Baltimore, Md.)
Dec 1, 2020
BACKGROUND AND AIMS: Standardized and robust risk-stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resect...
Comparison between atlas and convolutional neural network based automatic segmentation of multiple organs at risk in non-small cell lung cancer.
Medicine
Aug 21, 2020
Delineation of organs at risk (OARs) is important but time consuming for radiotherapy planning. Automatic segmentation of OARs based on convolutional neural network (CNN) has been established for lung cancer patients at our institution. The aim of th...
Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images.
Korean journal of radiology
Aug 1, 2020
OBJECTIVE: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application...
[Volume Measurements of Post-transplanted Liver of Pediatric Recipients Using Workstations and Deep Learning].
Nihon Hoshasen Gijutsu Gakkai zasshi
Jan 1, 2020
PURPOSE: The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant recipients on digital imaging and communications in medicine (DICOM) -format images using U-Net and t...
Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 1, 2020
PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver trans...
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