AIMC Topic: Humans

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Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis.

Radiology
Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for...

Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?

NeuroImage
Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological proces...

Development and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and key mutations in colorectal cancer from routine histology images: a retrospective study.

The Lancet. Digital health
BACKGROUND: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pa...

Environmentally Adaptive Shape-Morphing Microrobots for Localized Cancer Cell Treatment.

ACS nano
Microrobots have attracted considerable attention due to their extensive applications in microobject manipulation and targeted drug delivery. To realize more complex micro-/nanocargo manipulation (., encapsulation and release) in biological applicati...

Deep learning for lung disease segmentation on CT: Which reconstruction kernel should be used?

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to determine whether a single reconstruction kernel or both high and low frequency kernels should be used for training deep learning models for the segmentation of diffuse lung disease on chest computed tomograp...

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

EBioMedicine
BACKGROUND: To reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

IEEE transactions on bio-medical engineering
OBJECTIVE: Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been develope...

Adaptive Control Improves Sclera Force Safety in Robot-Assisted Eye Surgery: A Clinical Study.

IEEE transactions on bio-medical engineering
The integration of robotics into retinal microsurgery leads to a reduction in surgeon perception of tool-to-tissue interaction forces. This blunting of human tactile sensory input, which is due to the inflexible mass and large inertia of the robotic ...

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification.

IEEE transactions on bio-medical engineering
OBJECTIVES: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used sup...