AIMC Topic: Humans

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Natural language processing with machine learning to predict outcomes after ovarian cancer surgery.

Gynecologic oncology
OBJECTIVE: To determine if natural language processing (NLP) with machine learning of unstructured full text documents (a preoperative CT scan) improves the ability to predict postoperative complication and hospital readmission among women with ovari...

Recovery of reward function in problematic substance users using a combination of robotics, electrophysiology, and TMS.

International journal of psychophysiology : official journal of the International Organization of Psychophysiology
BACKGROUND: Theoretical and empirical work suggest that addictive drugs potentiate dopaminergic reinforcement learning signals and disrupt the reward function of its neural targets, including the anterior midcingulate cortex (aMCC) and the basal gang...

Deep learning for elective neck delineation: More consistent and time efficient.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND/PURPOSE: Delineation of the lymph node levels of the neck for irradiation of the elective clinical target volume in head and neck cancer (HNC) patients is time consuming and prone to interobserver variability (IOV), although international ...

Recurrent Hemoptysis After Bronchial Artery Embolization: Prediction Using a Nomogram and Artificial Neural Network Model.

AJR. American journal of roentgenology
The purpose of this study was to develop an effective nomogram and artificial neural network (ANN) model for predicting recurrent hemoptysis after bronchial artery embolization (BAE). The institutional ethics review boards of the two participating ...

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

AJR. American journal of roentgenology
The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dos...

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study.

AJR. American journal of roentgenology
Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination ...

[Gerontology, geriatric medicine and robot research : Look back to the future].

Zeitschrift fur Gerontologie und Geriatrie
Assistive robotics as a gerontological geriatric field of research so far seem to be perceived more as "recalcitrant". Predominant is a reserved attitude as to whether this should be considered a research topic to be taken seriously. The reliability ...

Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning.

ACS sensors
Identification and classification of leukemia cells in a rapid and label-free fashion is clinically challenging and thus presents a prime arena for implementing new diagnostic tools. Quantitative phase imaging, which maps optical path length delays i...

[Epicardial fat Tissue Volumetry: Comparison of Semi-Automatic Measurement and the Machine Learning Algorithm].

Kardiologiia
Aim        To compare assessments of epicardial adipose tissue (EAT) volumes obtained with a semi-automatic, physician-performed analysis and an automatic analysis using a machine-learning algorithm by data of low-dose (LDCT) and standard computed to...