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Found 28 papers, 3 papers with code
Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina
With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.
Current and future roles of artificial intelligence in retinopathy of prematurity
Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness.
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis
To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes.
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
It breaks down the interpretability process into data pre-processing, model selection, and post-processing, aiming to foster a comprehensive understanding of the crucial role of a robust interpretability approach in healthcare and to guide future research in this area.
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
This paper provides an overview of the recent developments and difficulties in using artificial intelligence and retinal imaging to diagnose cardiovascular diseases.
Solving the multiplication problem of a large language model system using a graph-based method
The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication.
Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023
Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form.
A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals
The results of this work have significant implications for patient treatment and for ongoing investigations into the early detection of Parkinson's disease.
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
The adoption of artificial intelligence (AI) in healthcare is growing rapidly.
RECOMED: A Comprehensive Pharmaceutical Recommendation System
Secondly, the patients and drugs were clustered, and then the recommendation was performed using different ratings provided by patients, and importantly by the knowledge obtained from patients and drug specifications, and considering drug interactions.
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined.
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities.
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression
So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians.
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands.
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction.
UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection
Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images.
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities are discussed.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented.
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging.
ECG Language Processing (ELP): a New Technique to Analyze ECG Signals
For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies.
HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability.
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.
A deep convolutional neural network model to classify heartbeats
The CNN was trained using the augmented data and achieved an accuracy of 94. 03% and 93. 47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively.
Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network
We have developed and trained a convolutional neural network to automatically and simultaneously segment optic disc, fovea and blood vessels.
Active spline model: A shape based model-interactive segmentation
Rarely in literature a method of segmentation cares for the edit after the algorithm delivers.
