Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
Estimating 10-hour dead fuel moisture content across the contiguous US is needed to manage and mitigate wildland fires.
Papers
1,138 of 1,138 verified cases.
Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach
Jointly detecting pneumonia, COVID-19 and tuberculosis from chest X-rays, since patients may have more than one lung disease at once.
A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer’s Disease using MRI Images
Medicine & Health
Dementia and Cognitive Impairment Research
partially solved
importance 7/10
scale stated
Early diagnosis of Alzheimer's disease is difficult, and its growing prevalence imposes major costs on individuals and society.
Machine Learning and Prediction of Infectious Diseases: A Systematic Review
Predicting infectious disease outbreaks early enough to act is difficult; this systematic review assessed whether machine learning can do it.
Deep learning-based framework for slide-based histopathological image analysis
Whole-slide histopathology image analysis lacked a general slide-based framework, with existing methods being task-dependent.
Integrating central nervous system metagenomics and host response for diagnosis of tuberculosis meningitis and its mimics
Medicine & Health
Infectious Diseases and Tuberculosis
partially solved
importance 7/10
scale stated
Tuberculosis meningitis is notoriously hard to diagnose in sub-Saharan Africa, and causes of meningitis there are poorly understood.
Alzheimer’s disease diagnosis and classification using deep learning techniques
Medicine & Health
Dementia and Cognitive Impairment Research
ai assisted
importance 7/10
scale stated
Manual diagnosis of Alzheimer's disease is error-prone and time-consuming, and early accurate detection is needed.
Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images
Classifying lung diseases such as pneumonia, pneumothorax and tuberculosis from chest X-rays is slow and error-prone, limiting computer-aided diagnosis.
A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
No simple screening model exists for Alzheimer's disease; diagnosis relies on expensive, sometimes invasive tests unavailable outside specialised settings.
Predicting malaria epidemics in Burkina Faso with machine learning
Malaria outbreaks in Burkina Faso previously required complicated numerical transmission simulations, preventing simple data-driven early warning.
Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models
Preventable or undiagnosed visual impairment and blindness affect billions worldwide, needing automated multi-disease detection in retinal imaging.
Acute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms
Acute myocardial infarction (AMI) needs rapid, accurate diagnosis from 12-lead ECGs so the culprit lesion can be treated early.
Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach
Physics & Astronomy
Pulsars and Gravitational Waves Research
partially solved
importance 7/10
scale stated
Detecting weak gravitational-wave signals from compact binary coalescences in noisy single-interferometer LIGO detector data.
DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration
Predicting how cancer cell lines respond to anticancer drugs is bottlenecked by the lack of effective methods for integrating multimodal, multisource omics data.
Prediction of dengue outbreak in Selangor Malaysia using machine learning techniques
Predicting dengue fever outbreaks in five high-incidence districts of Selangor, Malaysia using climate variables.
Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans
Detecting malignant lung nodules in CT scans is hard and time-consuming for radiologists, motivating computer-aided diagnosis systems.
Combining Docking Pose Rank and Structure with Deep Learning Improves Protein–Ligand Binding Mode Prediction over a Baseline Docking Approach
Biology & Life Sciences
Computational Drug Discovery Methods
partially solved
importance 7/10
scale stated
Predicting how drug-like ligands bind to target proteins (binding mode and activity) is difficult, and standard docking approaches alone are inaccurate.
Data-driven Chemical Reaction Prediction and Retrosynthesis
Predicting organic chemical reactions and designing synthesis routes, which traditionally requires chemists with years of knowledge and experience.
Mortality Prediction of Septic Patients in the Emergency Department Based on Machine Learning
Predicting mortality of suspected infected patients in hospital emergency departments, where sepsis is the most common cause of death.
On classifying sepsis heterogeneity in the ICU: insight using machine learning
Current ML models predicting sepsis from electronic health records ignore the condition's heterogeneity, limiting risk recognition in the ICU.
Faculty Opinions recommendation of A clinically applicable approach to continuous prediction of future acute kidney injury.
About 11% of hospital deaths follow failure to promptly recognize deteriorating patients; continuous, accurate, individual-level risk prediction is needed, using acute kidney injury as an exemplar.
Lung Cancer Detection and Classification Using Deep Learning
Detecting lung nodules in CT scans and classifying their malignancy level at an early stage is difficult and important for diagnosis.
Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect
Engineering & Materials
Advanced Battery Technologies Research
ai assisted
importance 7/10
scale stated
Traditional trial-and-error methods are inadequate for predicting battery lifetime and guiding design of next-generation battery systems.
Design of Cyclic Peptides Targeting Protein–Protein Interactions Using AlphaFold
Biology & Life Sciences
Protein Structure and Dynamics
partially solved
importance 7/10
scale stated
Predicting structures of protein–cyclic peptide complexes and designing cyclic peptides that bind target proteins was computationally infeasible, blocking cyclic peptides as a modality for protein-protein interaction drug targets.