Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction
Cardiovascular disease, the leading global cause of death, needed better risk identification from accessible data.
Papers
1,138 of 1,138 verified cases.
A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach
Medicine & Health
Artificial Intelligence in Healthcare
partially solved
importance 10/10
scale stated
Accurate early prediction of stroke is needed to enable timely treatment, but medical data is imbalanced.
Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning
Medicine & Health
Artificial Intelligence in Healthcare
partially solved
importance 10/10
scale stated
Cardiovascular disease often goes undiagnosed until serious events; non-invasive diagnosis from retinal images and DXA scans was lacking in Qatar.
Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization
Detecting tuberculosis accurately and early from chest X-ray images, since late diagnosis can be life-threatening.
A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases
Medicine & Health
Artificial Intelligence in Healthcare
partially solved
importance 10/10
scale stated
Cardiovascular disease is the leading cause of death worldwide (~17.9 million deaths/year), and early detection, prediction and monitoring are needed.
Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential
Predicting and managing COVID-19 disease severity was difficult, and mechanisms underlying severity were unclear.
Forecasting of COVID-19 epidemic size in four high hitting nations (USA, Brazil, India and Russia) by Fb-Prophet machine learning model
Forecasting the 60-day trajectory of total COVID-19 infections in the USA, Brazil, India and Russia to support pandemic planning.
Evolutionary-scale prediction of atomic-level protein structure with a language model
Predicting the 3D structures of the exponentially growing catalog of protein sequences was too slow with existing methods, requiring a breakthrough in folding speed.
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models
Structural coverage of known protein-sequence space was limited; the paper delivers an openly accessible database of high-accuracy predicted protein structures.
Accurate prediction of protein structures and interactions using a three-track neural network
Predicting accurate 3D protein structures and protein-protein complexes from sequence information alone remained a major challenge.
De novo design of protein structure and function with RFdiffusion
Designing new proteins with specified structures and functions (binders, symmetric assemblies, enzyme scaffolds) lacked a general deep-learning framework.
Artificial Intelligence and COVID-19 Using Chest CT Scan and Chest X-ray Images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
Fast, reliable diagnosis of COVID-19 from chest CT scans and X-ray images was needed during the pandemic.
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Predicting cardiovascular disease from clinical data to support earlier decisions and interventions.
SIMLR: Machine Learning inside the SIR Model for COVID-19 Forecasting
Forecasting the number of newly infected people one to four weeks ahead during the COVID-19 epidemic is hard because government policies change over time.
Genomic Surveillance of COVID-19 Variants With Language Models and Machine Learning
Public health needs early prediction of new SARS-CoV-2 variant surges to prepare for immune escape and increased transmissibility.
Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins
Predicting protein structures and designing functional proteins (efficient biocatalysts, bioactive proteins) is key to medicine, diagnostics and sustainable chemical manufacture. The paper reviews computational tools addressing this.
MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis
Sepsis kills about 5.4 million people worldwide each year and costs over 16 billion dollars in the USA alone, and early detection in hospitals is difficult.
An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning
Healthcare systems under pandemic pressure needed fast, early assessment of COVID-19 patient severity to allocate resources and reduce mortality.
Deep Learning Based Lung Cancer Detection and Classification
Lung cancer must be detected early from CT scans, since late diagnosis sharply reduces survival rates.
Detection and analysis of COVID-19 in medical images using deep learning techniques
Rapid, automatic detection of COVID-19 from X-ray and CT medical images.
Deep learning approaches for breast cancer detection in histopathology images: A review
Detecting and classifying breast cancer from histopathology images automatically, since manual analysis is essential but challenging.
A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)
During the COVID-19 pandemic, clinicians needed rapid automatic diagnosis from medical imaging such as CT and X-ray.
Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging
COVID-19 testing demand overwhelmed capacity; RT-PCR is definitive but chest X-ray is fast and affordable, so AI detection of COVID-19 from CXR images was investigated.
Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals
Heartbeat events need accurate classification from single- or multi-lead ECG signals to support cardiovascular disease screening and diagnosis.