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421 results for “Disease Diagnosis”
M3-OCTA:Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
<p>Ultra-wide optical coherence tomography angiography (UW-OCTA) is an emerging imaging technique that offers significant advantages over traditional OCTA by providing an exceptionally wide scanning range of up to 24 x 20 mm^{2}, covering both the anterior and posterior regions of the retina. However, the currently accessible UW-OCTA datasets suffer from limited comprehensive hierarchical information and corresponding disease annotations. To address this limitation, we have curated the pioneering M3OCTA dataset, which is the first multimodal (i.e., multilayer), multi-disease, and widest field-of-view UW-OCTA dataset. Furthermore, the effective utilization of multi-layer ultra-wide ocular vasculature information from UW-OCTA remains underdeveloped. To tackle this challenge, we propose the first cross-modal fusion framework that leverages multi-modal information for diagnosing multiple diseases. Through extensive experiments conducted on our openly available M3OCTA dataset, we demonstrate the effectiveness and superior performance of our method, both in fixed and varying modalities settings. The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.</p> <p>Our proposed M3OCTA is the first multi-modal based ultra-wide retinal OCTA dataset, involving 1637 scans from 1046 eyes of 620 individuals imaged in Zigong First People’s Hospital through 24×20 scan mode. Specifically, 1067 scans contains choroid large vessel image; images of 1310 scans from 496 people are labeled as six classes in multi-label setting, including healthy, diabetic retinopathy (DR), diabetic macular edema (DME), Retinal Vein Occlusion (RVO), Hypertension (HBP) and Vitreous Hemorrhage (VH), and then split into train, validation and test set as 6:2:2. The remaining unlabeled data are only used in the pretraining step. Details of our M3OCTA and other public ones are listed in Table.1. Compared with others, M3OCTA dataset demonstrates superiorities in several aspects including the number of modalities, number of patients, image resolution, and FOV.</p> <p> </p> <p><strong>You can request this dataset through signing the attached agreement. The download link will send to you. </strong></p>
Figure 2 Bar chart represents the percentage of the correct diagnosis using fuzzy diagnosis, K- nearest neighbor and Naïve Bayes classifiers.-Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis
<p>Figure 2 Bar chart represents the percentage of the correct diagnosis using fuzzy<br> diagnosis, K- nearest neighbor and Naïve Bayes classifiers.</p>
Figure 1. The comparison of the Area Under the ROC Curve (AUC) for fuzzy Diagnosis, KNN and NB-Comparison of Fuzzy Diagnosis with K-Nearest Neighbor and Naïve Bayes Classifiers in Disease Diagnosis
<p>The area under the receiver operating characteristic (ROC) curve (AUC) is used to measure<br> the performance of fuzzy diagnosis, KNN and NB. In order to show the difference between AUC<br> for the three methods, a single figure which combines the three AUC for the three methods was<br> used for comparison as shown in figure 1 below.</p>
Figure 4 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 4. Identification of PCR inhibitors in 18 biological samples positive for Aspergillus. Graph A: Ct values obtained from pure and diluted DNA samples (dilution rate 1/20). Graph B: Ct values obtained with 20 copies of a plasmid DNA systematically added to the same biological samples (undiluted and diluted) and a negative control sample (NC).
Figure 2 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 2. Influence of proteinase K digestion (56 °C overnight) on DNA extraction. Graph A shows the Ct values obtained by quantifying THP1 cell DNA derived from direct extraction with the NucliSENS easyMAG system and extraction performed on the same quantity of cells following overnight (ON) digestion with Proteinase K. Graph B shows Leishmania quantification after extraction with the NucliSENS easyMAG system both with and without PK and quantification after extraction using a QIAamp DNA Mini kit after ON digestion with PK.
Figure 7 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 7. Variation of the ratio between kinetoplastic DNA and nuclear DNA extraction with various Leishmania quantities in the presence of 103 THP1 cells.
Figure 5 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 5. Yield of DNA extraction from Leishmania and THP1 cells using the NucliSENS easyMAG system.
Figure 3 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 3. Results of the extraction experiments performed on yeast (Candida albicans) and filamentous fungi (Aspergillus fumigatus). A presents the kinetics of the extraction process after vortexing and glass-bead treatment. B shows the differences in DNA quantity obtained from fungal cells using the FastPrep system (with) compared to the same process without grinding.
Figure 6 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 6. Influence of the quantity of human cells (THP1 cells) on Leishmania quantification at various concentrations of host cells and parasites.
BRAIN Journal-New Computer Assisted Diagnostic to Detect Alzheimer Disease-Figure 3.Proposed Computer Assisted Diagnosis
<p>The figure below presents our proposed Computer Assisted Diagnosis. Our CAD includes 3 steps: Preprocessing, Segmentation and Classification. For the step of preprocessing, we used the NLMS (Non Local Means) to improve the quality of image. For the step of segmentation: we have a learning phase to extract the different shapes and to determine the average shape. Our proposed automatic method is based on the deformable model. For the step of classification, we present a new supervised method to distinguish between Normal, MCI and AD. The figure below presents our proposed system.</p>
PolyMed: A Medical Dataset Addressing Disease Imbalance for Robust Automatic Diagnosis Systems
<p>We introduce the PolyMed dataset, designed to address the limitations of existing medical case data for Automatic Diagnosis Systems (ADS). ADS assists doctors by predicting diseases based on patients' basic information, such as age, gender, and symptoms. However, these systems face challenges due to imbalanced disease label data and difficulties in accessing or collecting medical data. To tackle these issues, the PolyMed dataset has been developed to improve the evaluation of ADS by incorporating medical knowledge graph data and diagnosis case data. The dataset aims to provide comprehensive evaluation, include diverse disease information, effectively utilize external knowledge, and perform tasks closer to real-world scenarios.</p> <p>We have also made the data collection tools publicly available to enable researchers and other interested parties to contribute additional data in a standardized format. These tools feature a range of customizable input fields that can be selectively utilized according to the user's specific requirements, ensuring consistency and professionalism in the data collection process.</p> <p>All train and test code of our data available in https://github.com/krchanyang/PolyMed</p>
Diagnosis of Aspirin Hypersensitivity in Aspirin Exacerbated Respiratory Disease
ClinicalTrials.gov study NCT01320072. IPD Sharing: NO. Countries: 1. Publications: 1.
Diagnosis of prion diseases by RT-QuIC results in improved surveillance
<p><span><u>Objective:</u> We present the National Prion Disease Pathology Surveillance Center's (NPDPSC) experience using cerebrospinal fluid (CSF) real time quaking induced conversion (RT-QuIC) as a diagnostic test, examine factors associated with false negative RT-QuIC results, and investigate RT-QuIC's impact on prion disease surveillance. </span></p> <p> </p> <p><span><u>Methods:</u> Between May 2015-April 2018, the NPDPSC received 10,498 CSF specimens that were included in the study. Sensitivity and specificity analyses were performed using 567 autopsy verified cases. Prion disease type, demographic characteristics, specimen color, and time variables were examined for association with RT-QuIC results. The effect of including positive RT-QuIC cases in prion disease surveillance was examined.</span></p> <p> </p> <p><span><u>Results:</u> The diagnostic sensitivity and specificity of RT-QuIC across all prion diseases was 90.3% and 98.5%, respectively. Diagnostic sensitivity was lower for fatal familial insomnia, Gerstmann-Sträussler-Scheinker disease, sporadic fatal insomnia, variably protease sensitive prionopathy, and the VV1 and MM2 subtypes of sCJD. Individuals with prion disease and negative RT-QuIC results were younger, had elevated tau levels, and non-elevated 14-3-3 levels compared to RT-QuIC positive cases. Sensitivity was high throughout the disease course. Some cases that initially tested RT-QuIC negative had a subsequent specimen test positive. Including positive RT-QuIC cases in surveillance statistics increased laboratory-based case ascertainment of prion disease by 90% over autopsy alone. </span></p> <p> </p> <p><span><u>Conclusions:</u> RT-QuIC has high sensitivity and specificity for diagnosing prion diseases. Sensitivity limitations are associated with prion disease type, age, and related CSF diagnostic results. RT-QuIC greatly improves laboratory-based prion disease ascertainment for surveillance purposes. </span></p> <p> </p> <p><span><u>Classification of Evidence:</u> This study provides Class III evidence that 2<sup>nd</sup> generation real time quaking-induced conversion (RT-QuIC) identifies prion disease with sensitivity of 90.3% and specificity of 98.5%, among patients being screened for these diseases due to concerning symptoms. </span></p>
Pathformer: a biological pathway informed Transformer for disease diagnosis and prognosis using multi-omics data
<p>Integrating multi-omics data offers a more comprehensive view of gene regulation, which would be helpful in achieving accurate diagnosis of diseases like cancer. To improve the accuracy of disease diagnosis and prognosis, we developed Pathformer, a multi-omics integration method for both tissue and liquid biopsy data. We implemented Pathformer's network architecture using the “PyTorch” package in Python v3.6.9, and our codes can be found in the GitHub repository (https://github.com/lulab/Pathformer). This repository contains preprocessed TCGA dataset data, preprocessedliquid biopsy dataset data, result of Pathformer and comparison_methods, mentioned in GitHub project and article.</p>
ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)
<p><strong>ALAMEDA</strong> is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke.</p> <p>To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires.</p> <p>In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically:</p> <ul> <li>For <strong>Parkinson's disease</strong>: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings.</li> <li>For <strong>Multiple Sclerosis</strong>: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings.</li> <li>For <strong>Stroke</strong>: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings.</li> </ul> <p>More information about the datasets provided can be found in the respective READ ME files that are included in the current record.</p>
Characterizing Metabolic Alterations in Early-stage chronic kidney disease (CKD) patients: A Pathway for Improved Diagnosis and Personalized Treatment.
<p>The raw NMR data that I have uploaded contains the final concentration results that have been used for this study.</p>
The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer's Disease: A Review- Screening
<p>The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer’s Disease: A Review- Screening file</p>
Figure 1 in Application of the NucliSENS easyMAG system for nucleic acid extraction: optimization of DNA extraction for molecular diagnosis of parasitic and fungal diseases
Figure 1. Study design.
DiaMOS Plant Dataset: A Dataset for Diagnosis and Monitoring Plant Disease
<p>DiaMOS Plant, is a dataset for diagnosis and monitoring plant disease, collected in the field, consisting of 3505 images, depicting 4 leaf diseases with 4 level of severity and 4 fruit stages.</p> <p> </p> <p>Cite as:</p> <p>Fenu, G.; Malloci, F.M. DiaMOS Plant: A Dataset for Diagnosis and Monitoring Plant Disease. Agronomy 2021, 11, 2107. https://doi.org/10.3390/agronomy11112107</p>
PCP Use of a Gene Expression Test (Corus CAD or ASGES) in Coronary Artery Disease Diagnosis
ClinicalTrials.gov study NCT01594411. IPD Sharing: Not stated. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.