AIMC Topic: Reproducibility of Results

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Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Nature communications
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment....

CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases.

BMC medical informatics and decision making
BACKGROUND: Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and pati...

Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods.

Scientific reports
Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we ...

Involvement of Machine Learning Tools in Healthcare Decision Making.

Journal of healthcare engineering
In the present day, there are many diseases which need to be identified at their early stages to start relevant treatments. If not, they could be uncurable and deadly. Due to this reason, there is a need of analysing complex medical data, medical rep...

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network.

Magnetic resonance in medicine
PURPOSE: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squ...

The accuracy and predictability of micro Doppler radar signature projection algorithm measuring functional movement in NCAA athletes.

Gait & posture
BACKGROUND: Development of accessible cost-effective technology to objectively, reliably, and accurately predict musculoskeletal injury risk could aid the effort to prevent chronic pain and disability. Recent work on micro-Doppler radar suggests it m...

Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods.

Scientific reports
Metastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the qua...

A deep learning-based model for screening and staging pneumoconiosis.

Scientific reports
This study aims to develop an artificial intelligence (AI)-based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs. The model, based on chest radiographs, was developed using a training cohort and validated ...

A Novel Block Imaging Technique Using Nine Artificial Intelligence Models for COVID-19 Disease Classification, Characterization and Severity Measurement in Lung Computed Tomography Scans on an Italian Cohort.

Journal of medical systems
Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COV...

Denoising non-steady state dynamic PET data using a feed-forward neural network.

Physics in medicine and biology
The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies h...