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Found 28 papers, 3 papers with code
Benchmarking Ophthalmology Foundation Models for Clinically Significant Age Macular Degeneration Detection
Self-supervised learning (SSL) has enabled Vision Transformers (ViTs) to learn robust representations from large-scale natural image datasets, enhancing their generalization across domains.
HYAMD High-Resolution Fundus Image Dataset for age related macular degeneration (AMD) Diagnosis
The Hillel Yaffe Age Related Macular Degeneration (HYAMD) dataset is a longitudinal collection of 1, 560 Digital Fundus Images (DFIs) from 325 patients examined at the Hillel Yaffe Medical Center (Hadera, Israel) between 2021 and 2024.
Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers
There is a need for a fast and reliable method to aid in the early identification of LVO.
Enhancing Retinal Vessel Segmentation Generalization via Layout-Aware Generative Modelling
Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability.
GONet: A Generalizable Deep Learning Model for Glaucoma Detection
Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated.
SHDB-AF: a Japanese Holter ECG database of atrial fibrillation
Atrial fibrillation (AF) is a common atrial arrhythmia that impairs quality of life and causes embolic stroke, heart failure and other complications.
SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography
Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series.
RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection from the Raw ECG
Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG.
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images
Diabetic retinopathy (DR) is a prevalent complication of diabetes associated with a significant risk of vision loss.
Generalization in medical AI: a perspective on developing scalable models
The scientific community is increasingly recognizing the importance of generalization in medical AI for translating research into practical clinical applications.
End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks
Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular diseases such as stroke and heart failure.
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
Using active learning, we created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations performed by fifteen medical students and reviewed by an ophthalmologist, and developed LUNet, a novel deep learning architecture for high resolution A/V segmentation.
PhysioZoo: The Open Digital Physiological Biomarkers Resource
PhysioZoo is a collaborative platform designed for the analysis of continuous physiological time series.
Machine Learning for Ranking f-wave Extraction Methods in Single-Lead ECGs
The approach is well-suited for processing large Holter data sets annotated with respect to the presence of AF.
Estimation of f-wave Dominant Frequency Using a Voting Scheme
Using these three algorithms in a voting scheme, the classifier obtained AUC=0. 60 and the DAFs were mostly spread around 6 Hz, 5. 66 (4. 83-7. 47).
Lirot.ai: A Novel Platform for Crowd-Sourcing Retinal Image Segmentations
Discussion and future work: We will use active learning strategies to continue enlarging our retinal fundus dataset by including a more efficient process to select the images to be annotated and distribute them to annotators.
Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure
Therefore, an AFR risk prediction algorithm could enable a better management of CAP patients.
ArNet-ECG: Deep Learning for the Detection of Atrial Fibrillation from the Raw Electrocardiogram
We further hypothesize that the performance reached leveraging the raw ECG will be superior to previously developed methods using the beat-to-beat interval variation time series.
PVBM: A Python Vasculature Biomarker Toolbox Based On Retinal Blood Vessel Segmentation
Results: We built a fully automated vasculature biomarker toolbox based on DFI segmentations and provided a proof of usability to characterize the vascular changes in glaucoma.
Generalizable and Robust Deep Learning Algorithm for Atrial Fibrillation Diagnosis Across Ethnicities, Ages and Sexes
This retrospective study is, to the best of our knowledge, the first to develop and assess the generalization performance of a deep learning (DL) model for AF events detection from long term beat-to-beat intervals across ethnicities, ages and sexes.
Building Trust: Lessons from the Technion-Rambam Machine Learning in Healthcare Datathon Event
A datathon is a time-constrained competition involving data science applied to a specific problem.
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task.
Machine Learning to Support Triage of Children at Risk for Epileptic Seizures in the Pediatric Intensive Care Unit
This research aims to develop a computer aided tool to improve seizures risk assessment in critically-ill children, using an ubiquitously recorded signal in the PICU, namely the electrocardiogram (ECG).
FundusQ-Net: a Regression Quality Assessment Deep Learning Algorithm for Fundus Images Quality Grading
We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale.
SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography
We hypothesize that it is possible to perform robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL).
From sleep medicine to medicine during sleep: A clinical perspective
Sleep has a profound influence on the physiology of body systems and biological processes.
Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers
Approach: We hypothesize that patients with COPD will exert certain patterns and/or dynamics of their overnight oximetry time series that are unique to this condition.
Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing
However, there is incentive in performing screening for specific at risk groups such as individuals suspected of sleep-disordered breathing where an important association between AF and obstructive sleep apnea (OSA) has been demonstrated.
