AIMC Topic: Humans

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Use of machine learning method on automatic classification of motor subtype of Parkinson's disease based on multilevel indices of rs-fMRI.

Parkinsonism & related disorders
OBJECTIVE: This study aimed to develop an automatic classifier to distinguish different motor subtypes of Parkinson's disease (PD) based on multilevel indices of resting-state functional magnetic resonance imaging (rs-fMRI).

Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network ...

A benchmark for neural network robustness in skin cancer classification.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: One prominent application for deep learning-based classifiers is skin cancer classification on dermoscopic images. However, classifier evaluation is often limited to holdout data which can mask common shortcomings such as susceptibility t...

A Machine Learning Approach in Predicting Mortality Following Emergency General Surgery.

The American surgeon
BACKGROUND: There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to develop and validate the use of a machine learning approach to predict mortality following EGS.

Managing tumor changes during radiotherapy using a deep learning model.

Medical physics
PURPOSE: We propose a treatment planning framework that accounts for weekly lung tumor shrinkage using cone beam computed tomography (CBCT) images with a deep learning-based model.

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.

Sensors (Basel, Switzerland)
Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obst...

A bagging dynamic deep learning network for diagnosing COVID-19.

Scientific reports
COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagg...

dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.

BMC medical imaging
BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly c...

A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study.

EBioMedicine
BACKGROUND: Induction chemotherapy (ICT) plus concurrent chemoradiotherapy (CCRT) and CCRT alone were the optional treatment regimens in locoregionally advanced nasopharyngeal carcinoma (NPC) patients. Currently, the choice of them remains equivocal ...

Diffusion enables integration of heterogeneous data and user-driven learning in a desktop knowledge-base.

PLoS computational biology
Integrating reference datasets (e.g. from high-throughput experiments) with unstructured and manually-assembled information (e.g. notes or comments from individual researchers) has the potential to tailor bioinformatic analyses to specific needs and ...