System recommends disease treatments based on big data and cloud computing.
problem Inaccurate disease classification and treatment recommendations due to complex symptoms and multi-pathogenesis.
method DPCA for disease-symptom clustering, Apriori for D-D and D-T rules, parallel Apache Spark implementation.
result Effective disease-symptom clustering and accurate treatment recommendations for inexperienced doctors.
Study improves CAD diagnosis accuracy by selecting significant features.
problem Improving accuracy of CAD diagnosis through feature selection.
method Integrated machine learning approach using random trees (RTs), C5.0, SVM, and CHAID.
result Random trees model outperforms other models in CAD diagnosis.
AI framework diagnoses Parkinson's disease with 100% accuracy.
problem Expertise-demanding medical imaging procedures for Parkinson's disease diagnosis.
method End-to-end, multi-modality diagnosis framework using T1-MRI and 11C-CFT PET.
result 100% accuracy in PD/NL classification.
Low-cost device diagnoses diseases using deep learning.
problem Limited healthcare access and variable disease properties.
method Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) on a Raspberry Pi Zero.
result Achieves 90% accuracy for symptom-based diagnoses and 91% for visual skin diseases.
High-throughput machine learning predicts thousands of diagnosis codes with high accuracy.
problem Predicting disease risk for thousands of diagnosis codes at various time points.
method Training machine learning algorithms on EHR data to predict diagnosis risks.
result Achieved AUCs of 0.803 and 0.758 for 1 and 6-month predictions, respectively.
Deep learning improves retinal fundus image analysis for eye diseases.
problem Improving accuracy in diagnosing eye diseases using retinal images.
method Review of deep learning models and datasets for retinal image analysis.
result Deep learning enhances detection and classification of eye diseases.
System diagnoses Alzheimer's disease from spoken language using multi-modal features.
problem Early diagnosis of Alzheimer's disease from spoken language.
method Classification system based on spoken language using three approaches (N-gram, i-vector, x-vector).
result Accuracy of 83.6% on the cookie picture description task from Pitt Corpus dementia bank.
Hierarchical CNNs improve diagnosis of GI diseases from histopathological images.
problem Diagnosing GI diseases from histopathological images is challenging due to heterogeneity and shared features.
method Embedded a class hierarchy into a VGGNet to address the hierarchical structure of GI diseases.
result The hierarchical model achieved better results than a flat model for multi-category diagnosis of GI disorders.
Machine learning aids in diagnosing Parkinson's disease with higher accuracy.
problem Subjectivity in traditional PD diagnosis methods and missed early symptoms.
method Machine learning applied to various data modalities for PD and control group classification.
result Machine learning methods show high potential for improving PD diagnosis.
Given genetic variations and various phenotypical traits, such as Magnetic Resonance Imaging (MRI) features, we consider two important and related tasks in biomedical research: i)to select genetic and phenotypical markers for disease diagnosis and ii) to identify associations between genetic and phenotypical data. Thes…
Study proposes deep learning techniques to diagnose and differentiate Celiac Disease and Environmental Enteropathy from biopsy images.
problem Challenging histopathologic overlap between Celiac Disease and Environmental Enteropathy in biopsy images.
method Color balancing and Random Multimodel Deep Learning (RMDL) architecture to address staining variability.
result Proposed deep learning techniques improve diagnosis accuracy of Celiac Disease and Environmental Enteropathy.
AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.
problem Misdiagnosis of heart disease leads to unnecessary deaths.
method Developed an AI application using ML and DNN algorithms on a dataset from the Cleveland Clinic Foundation.
result DNN model achieved a 92% accuracy rate, reducing misdiagnosis.
A hybrid deep learning model improves ESD diagnosis accuracy.
problem Automated diagnosis of Erythemato-Squamous Disease (ESD) is challenging.
method Proposes Derm2Vec, a hybrid model combining Autoencoders and Deep Neural Networks.
result Derm2Vec outperforms other methods in real-world dermatology dataset.
Proposes a new method for medical diagnosis using network-based representation learning.
problem Improving medical diagnosis accuracy through better data representation.
method Heterogeneous network-based model and modified metapath2vec algorithm for learning latent node representations.
result Significant performance boost in symptom/disease classification and disease prediction tasks.
Quick and accurate medical diagnosis is crucial for the successful treatment of a disease. Using machine learning algorithms, we have built two models to predict a hematologic disease, based on laboratory blood test results. In one predictive model, we used all available blood test parameters and in the other a reduced…
Deep learning model diagnoses celiac disease severity from intestinal biopsy images.
problem Diagnosing celiac disease severity from biopsy images, especially mild cases.
method Deep residual networks trained on a modified Marsh score histological scoring system.
result Model achieved AUC > 0.96 in all classes for CD severity classification.
Novel method reduces radiomic data annotation needs.
problem Insufficient labeled radiomic data for disease diagnosis.
method Collaborative self-supervised learning with two pretext tasks.
result Outperforms other self-supervised methods on radiomic data.
Paper proposes abstention in machine learning for better liver disease diagnosis accuracy.
problem Improving accuracy in automated liver disease diagnosis using ultrasound images.
method Implemented a machine learning model with abstention capability, using stochastic gradient solver.
result The proposed abstention model outperforms existing models in liver disease classification.
Optimal model diagnoses funduscopic images for ocular diseases.
problem Binary classification of funduscopic images for ocular diseases.
method Transfer learning using Xception base architecture, Adam optimizer, mean squared error loss function, and custom heuristic equation.
result 90% accuracy, 94% sensitivity, and 86% specificity achieved.
A new framework decouples instance representation learning from subject-level supervision in EEG-based disease diagnosis.
problem Inherently assigning subject labels to all instances in EEG-based disease diagnosis leads to unreliable representations.
method BridgeMIL, a two-stage framework that pretrains an encoder without inherited instance labels and then applies subject-level supervision.
result BridgeMIL achieves the highest mean accuracy in 14 of 15 dataset-backbone settings, with an overall mean accuracy of 76.57%.
Convolutional neural networks improve biopsy image classification for diagnosing Celiac Disease and Environmental Enteropathy.
problem Diagnosing Celiac Disease and Environmental Enteropathy from biopsy images due to histopathologic overlap.
method Proposed a convolutional neural network (CNN) to classify duodenal biopsy images.
result The proposed model achieves high accuracy in classifying biopsy images for CD, EE, and healthy controls.
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies h…
Deep reinforcement learning improves medical test suggestions.
problem Improving accuracy of disease diagnosis through better medical test recommendations.
method Formulated as a stage-wise Markov decision process, trained using reinforcement learning with a new action policy representation and exploration scheme.
result Significant improvement in disease diagnosis accuracy with better medical test suggestions.
In this paper, the development of a probabilistic network for the diagnosis of acute cardiopulmonary diseases is presented. This paper is a draft version of the article published after peer review in 2018 (https://doi.org/10.1002/bimj.201600206). A panel of expert physicians collaborated to specify the qualitative part…
Paper proposes CNN for diagnosing rare muscular dystrophies from images.
problem Challenges in diagnosing rare diseases due to lack of data.
method Uses a Convolutional Neural Network (CNN) for patch-based classification of confocal microscopy images.
result CNN system identifies problematic areas and provides global patient evaluation.
Counterfactual diagnosis improves medical accuracy and safety.
problem Existing diagnostic algorithms struggle with distinguishing correlation from causation.
method Reformulated diagnosis as a counterfactual inference task and derived new counterfactual diagnostic algorithms.
result Counterfactual diagnostic algorithms significantly improve accuracy and safety compared to standard Bayesian algorithms.
THS-GAN uses tensorizing and high-order pooling for AD diagnosis.
problem Early diagnosis of Alzheimer's Disease (AD) using MRI images.
method Tensorizing a three-player cooperative game framework with high-order pooling for MRI images.
result THS-GAN achieves superior performance in AD diagnosis compared to existing methods.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
Improved sex and age-specific model for Parkinson's diagnosis.
problem Accurate diagnosis of Parkinson's disease for better patient care.
method Sex-specific and age-dependent handwriting classification model.
result Significantly improved accuracy (83.75% for females, 79.55% for older age group) compared to generalized model.
Proposes a new method for explaining deep CNNs used in MRI-based AD diagnosis.
problem Lack of transparency and interpretability in deep CNN models for AD diagnosis.
method Swap Test method designed specifically for brain scan task.
result Proposed method provides more suitable explanations for AD diagnosis using MRI.
WEST uses EHRs and expert cases to improve rare disease phenotyping.
problem Limited labeled data for rare diseases.
method Weakly supervised transformer model trained on probabilistic silver-standard labels.
result WEST outperforms existing methods in phenotype classification and subphenotyping.
Quantitative CT predicts ILD patterns and prognosis.
problem Diagnosing and predicting prognosis of fibrosing ILD patterns.
method High-resolution CT texture features, TM model for classification and survival analysis.
result TM model outperforms histogram-based model in distinguishing UIP from non-UIP patterns and allows for survival group partitioning.
Paper classifies Parkinson's disease from speech in three languages using CNNs and transfer learning.
problem Classifying Parkinson's disease from speech in multiple languages.
method Convolutional Neural Networks (CNNs) with transfer learning among Spanish, German, and Czech.
result Transfer learning improves model accuracy by up to 8% and balances specificity-sensitivity.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
This study evaluates adversarial attacks and defenses for chest X-ray disease classification.
problem Vulnerability of deep neural networks to adversarial examples in chest X-ray disease detection.
method Detailed introduction and evaluation of various attack and defense methods.
result Attack and defense methods perform poorly with excessive iterations and large perturbations.
This study evaluates handwriting features to diagnose Parkinson's disease.
problem Diagnosing Parkinson's disease through handwriting analysis.
method Kinematic, geometrical, and non-linear features were evaluated using K-nearest neighbors, support vector machines, and random forest classifiers.
result Up to 93.1% accuracy in classifying Parkinson's disease and healthy subjects.
Machine learning detects subtle glucose changes for early diabetes diagnosis.
problem Challenging early-stage diabetes diagnosis due to subtle glucose changes.
method Applied machine learning to synthetic glucose profiles generated by a biophysical model.
result High accuracy (above 85%) in detecting insulin resistance using various neural networks.
CNNs accurately measure airways and vessels on CT images, improving lung disease diagnosis.
problem Accurately characterizing small pulmonary structures from CT images for disease diagnosis.
method Generative model combined with Convolutional Neural Regressor (CNR) for cross-sectional measurements.
result CNNs provide accurate measurements with physiological correlates.
Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data ana…
Deep neural network classifies DaTscan SPECT images for Parkinson's Disease.
problem Early diagnosis of Parkinson's Disease through objective analysis of SPECT images.
method InceptionV3 architecture with custom binary classifier, 10-fold cross validation.
result Deep neural network achieves high accuracy in classifying DaTscan SPECT images.
Deep learning model improves medical diagnosis and reduces patient time.
problem Ineffective and slow methods for synthesizing multimodal medical data.
method Cross-modal deep learning architecture with co-attention mechanism.
result Model outperforms previous methods by 2.35% and is 53% more efficient.
Deep learning model improves X-ray disease detection accuracy in Thai patients.
problem Lack of large-scale validation of AI algorithms for medical image diagnosis in Thailand.
method Development and testing of a deep learning algorithm using 421,859 local chest radiographs.
result Convolutional neural networks achieve remarkable performance in detecting 13 common abnormality conditions on chest X-ray.
Pairwise ranking aligns subjective clinical evaluations with objective indicators.
problem Aligning subjective clinical evaluations with objective indicators for improved diagnosis.
method Pairwise ranking methods to align subjective evaluations with objective indicators.
result The resulting score improves classification accuracy and provides a nuanced severity assessment.
Bayesian model identifies health disparities in disease progression.
problem Health disparities bias disease progression models.
method Interpretable Bayesian model accounting for three disparities.
result Model identifies and corrects for health disparities.
Deep learning boosts rare disease detection from medical claims.
problem Improving diagnosis and treatment of rare diseases.
method Generative adversarial networks (GANs) and recurrent neural networks for sequence modeling.
result Accurate prediction with 0.56 PR-AUC, outperforming benchmarks.
PETNet improves AD diagnosis using graph-based CNN on PET images.
problem Early diagnosis of Alzheimer's Disease using PET imaging.
method PETNet, a graph-based CNN architecture for 3D PET image analysis.
result PETNet shows improved performance over deep learning and other methods on ADNI dataset.
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual chec…
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the di…