Diagnosis Of Disease Research Articles

Comparative Analysis of VGG16, RESNET50, AND CNN Models for Lung Disease Prediction: A Deep Learning Approach

Abstract: Utilizing deep learning methods in medical image analysis has shown promise in enhancing disease detection and diagnosis. In this study, we conducted a detailed comparative analysis of threewidely recognized convolutional neural network (CNN) architectures: VGG16, ResNet50, and a custom CNN model tailored for pneumonia and COVID-19 detection from chest X-ray images. Leveraging transfer learning techniques and meticulously curated datasets, we evaluated the models' performance in accurately identifying respiratory diseases. Our investigation utilized two publicly available datasets, the ChestX-ray14 dataset and the NIH Chest X-ray Dataset, both annotated for pneumonia and COVID-19. Prior to model training, we conducted thorough preprocessing to ensure optimal data quality and consistency. Through rigorous experimentation, we assessed the models' accuracy, sensitivity, and specificity in disease detection. The results revealed nuanced differences in model performance across disease categories. While VGG16 demonstrated robust accuracy in pneumonia detection, ResNet50 exhibited enhanced sensitivity and specificity in identifying COVID-19 cases. Our custom CNN model, leveraging insights from both architectures, showcased competitive performance, emphasizing the importance of tailored model design for optimal diagnostic outcomes. Through comprehensive analysis and discussion, we elucidated the strengths and limitations of each model, considering factors such as computational efficiency, interpretability, and generalizability. Our findings underscore the potential of deep learningbased diagnostic tools in supporting healthcare professionals in timely and accurate disease diagnosis

Leaf Disease Detection Using Convolutional Neural Network

Leaf Disease Detection Using Convolutional Neural Network

Machine Learning Techniques for the Classification of Thyroid Disease

Abstract: In the healthcare system, dealing with a large amount of data is challenging. The techniques of machine learning are used in dealing with such data. As per NFHS – 5 statistics, thyroid diseases are increasing in India. Roughly 1 in 10 Indian adults suffer from a thyroid disorder. It has been expected that more than 42 million peoples suffer from thyroid disease. For the proper diagnosis of disease, it is vital to process the medical data accurately. This study classified thyroid disease cases into hyperthyroid, euthyroid, hypothyroid, and sick. This paper aims to inspect Logistic regression for multiclass categorizing the thyroid dataset. This logistic regression model is evaluated based on its precision, recall, F measure, ROC, RMS Error, and accuracy metrics. Based on the thyroid dataset, we find that logistic regression using the One-vs.-Rest heuristic is 85% accurate, while logistic regression using the multinomial is 86% accurate.

Implementation Paper on Dermatological Proriasis and other Skin Diseases Diagnosis Using Machine Learning

Abstract: This paper "Dermatological Psoriasis and Other Skin Diseases Diagnosis Using Machine Learning" aims to develop an efficient and accurate system for identifying psoriasis, a chronic skin condition, through the utilization of machine learning techniques. Psoriasis affects millions worldwide, presenting challenges in its diagnosis and treatment. Leveraging advanced algorithms, this project seeks to analyse various skin images and clinical data to create a robust model capable of distinguishing psoriatic lesions from other dermatological conditions with high precision. By employing machine learning algorithms such as convolutional neural networks (CNNs) and support vector machines (SVMs), alongside feature extraction methods, the system aims to automate and streamline the diagnostic process, potentially reducing both time and burdens on healthcare professionals. Ultimately, the implementation of such a tool could significantly enhance early detection rates, improve patient outcomes, and facilitate more targeted treatment strategies for individuals affected by psoriasis

Artificial Intelligence and Machine Learning in Hepatocellular Carcinoma Screening, Diagnosis and Treatment - A Comprehensive Systematic Review

Background: Medical image analysis plays a crucial role in the screening, monitoring, diagnosis, and prognosis of diseases. Hepatocellular Carcinoma (HCC) often remain asymptomatic in their early stages. Timely identification and intervention are crucial to prevent the progression to decompensated liver diseases and advanced-stage HCC, minimizing morbidity and mortality. Methodology: This study examined twenty relevant research articles that met the inclusion criteria. Utilizing the PRISMA criteria, a systematic search was conducted on PubMed/MEDLINE and Google Scholar for studies on HCC screening employing Artificial Intelligence (AI) and Machine Learning (ML). The search focused on the keywords "artificial intelligence" and "hepatocellular carcinoma," with inclusion criteria specifying studies in the English language published in and after 2020. Exclusions were made for histology, animal research, and investigations conducted before 2020. Titles and abstracts were thoroughly reviewed, and any discrepancies were discussed. Results: The comprehensive review reveals the transformative impact of AI and ML on HCC screening, diagnosis, and therapy. ML models demonstrated effectiveness in early HCC diagnosis, distinguishing hepatic lesions, predicting treatment responses, and assessing recurrence risks across various techniques. Integration of mass spectrometry-based technologies, advanced imaging, and real-world data significantly improved diagnostic accuracy and clinical decision support. AI models exhibited diagnostic expertise with potential applications in therapy suggestions, personalized surveillance, and prognostic evaluations. However, further validation and seamless integration into clinical practice are essential for realizing their full potential. Conclusion: This systematic study underscores the progress made in the application of AI and ML in HCC screening, diagnosis, and treatment. Numerous studies highlight the capability of AI and ML systems to enhance hepatocellular carcinoma diagnosis, predict outcomes, and optimize therapy. The enduring and versatile nature of these technologies points towards a revolutionary future in personalized and efficient HCC management.

Differential Artery–Vein Analysis Improves OCTA Classification of Diabetic Retinopathy

This study investigates the impact of differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) on machine learning classification of diabetic retinopathy (DR). Leveraging deep learning for arterial-venous area (AVA) segmentation, six quantitative features, including perfusion intensity density (PID), blood vessel density (BVD), vessel area flux (VAF), blood vessel caliber (BVC), blood vessel tortuosity (BVT), and vessel perimeter index (VPI) features, were derived from OCTA images before and after AV differentiation. A support vector machine (SVM) classifier was utilized to assess both binary and multiclass classifications of control, diabetic patients without DR (NoDR), mild DR, moderate DR, and severe DR groups. Initially, one-region features, i.e., quantitative features extracted from the entire OCTA, were evaluated for DR classification. Differential AV analysis improved classification accuracies from 78.86% to 87.63% and from 79.62% to 85.66% for binary and multiclass classifications, respectively. Additionally, three-region features derived from the entire image, parafovea, and perifovea, were incorporated for DR classification. Differential AV analysis further enhanced classification accuracies from 84.43% to 93.33% and from 83.40% to 89.25% for binary and multiclass classifications, respectively. These findings highlight the potential of differential AV analysis in augmenting disease diagnosis and treatment assessment using OCTA.

Association Between Autoimmune Diseases and Monoclonal Gammopathy of Undetermined Significance : An Analysis From a Population-Based Screening Study.

Monoclonal gammopathy of undetermined significance (MGUS) is a precursor of multiple myeloma (MM) and related conditions. In previous registry-based, retrospective studies, autoimmune diseases have been associated with MGUS. However, these studies were not based on a screened population and are therefore prone to ascertainment bias. To examine whether MGUS is associated with autoimmune diseases. A cross-sectional study within iStopMM (Iceland Screens, Treats, or Prevents MM), a prospective, population-based screening study of MGUS. Icelandic population of adults aged 40 years or older. 75 422 persons screened for MGUS. Poisson regression for prevalence ratios (PRs) of MGUS among persons with or without an autoimmune disease, adjusted for age and sex. A total of 10 818 participants had an autoimmune disorder, of whom 599 had MGUS (61 with a prior clinical diagnosis and 538 diagnosed at study screening or evaluation). A diagnosis of an autoimmune disease was not associated with MGUS (PR, 1.05 [95% CI, 0.97 to 1.15]). However, autoimmune disease diagnoses were associated with a prior clinical diagnosis of MGUS (PR, 2.11 [CI, 1.64 to 2.70]). Registry data were used to gather information on autoimmune diseases, and the homogeneity of the Icelandic population may limit the generalizability of these results. The study did not find an association between autoimmune disease and MGUS in a systematically screened population. Previous studies not done in systematically screened populations have likely been subject to ascertainment bias. The findings indicate that recommendations to routinely screen patients with autoimmune disease for MGUS may not be warranted. The International Myeloma Foundation and the European Research Council.

An ultrasensitive electrochemical biosensor based on logic transcription circuit-activated DNAzyme amplifier for flap endonuclease 1 activity detection

The accurate detection of flap endonuclease 1 (FEN1) activity is crucial for early cancer diagnosis and therapy. Herein, we have established a “target-substrate double-enhanced” DNA electrochemical biosensor by integrating a cleavage-ligation logic transcription circuit-activated DNAzyme amplifier with the transition-metal carbides-gold nanoparticles (MXene-Au) composites for the accurate detection of FEN1 activity. The FEN1 can be translated into a stable transcription hairpin DNA (TH), incorporating the catalytic strand of the DNAzyme via the logic transcription circuit. Then, the TH hybridizes with the substrate strand of DNAzyme and the DNAzyme amplifier can be activated, thus producing a significant quantity of single-stranded DNA (sDNA) and achieving the efficient amplification of the FEN1. Finally, the sDNA loaded with electroactive substances is immobilized on the capture DNA-anchored MXene-Au modified electrode, significantly enhancing the electrochemical signal intensity for highly sensitive FEN1 activity detection. The excellent selectivity and sensitivity of the proposed electrochemical biosensor can facilitate the accurate detection of FEN1 activity in biological samples and aid in screening FEN1-releated drugs. Additionally, three-input concatenated logic gates (AND-AND, INHIBIT-AND, and AND-OR) are constructed for FEN1 activity detection. Particularly, the AND-AND gate achieves logic detection of FEN1 activity in complex samples. Consequently, this bidirectionally enhanced electrochemical biosensor offers a novel analytical method for FEN1 activity detection, showing substantial promise for early diagnosis and drug discovery of FEN1-related disease.

Regular use of fish oil supplements and course of cardiovascular diseases: prospective cohort study

ObjectiveTo examine the effects of fish oil supplements on the clinical course of cardiovascular disease, from a healthy state to atrial fibrillation, major adverse cardiovascular events, and subsequently death.DesignProspective cohort...

Neurological symptoms in adults with Gaucher disease: a systematic review.

Gaucher disease (GD) is classically divided into three types, based on the presence or absence of neurological signs and symptoms. However, presentation can be highly variable in adulthood, and this aspect has not been adequately addressed in the literature so far. We performed a systematic literature review to analyze the entire spectrum of neurological manifestations in adult patients previously classified as GD type I, II, or III, evaluating the role of variants in different neurological manifestations. We searched databases for studies reporting clinical data of adult GD patients (age ≥ 18). Data extraction included GD types, GBA1 variants, age at disease onset and diagnosis, duration of GD, and age at onset and type of neurological symptoms reported. Among 4190 GD patients from 85 studies, 555 exhibited neurological symptoms in adulthood. The median age at evaluation was 46.8years (IQR 26.5), age at neurological symptoms onset was 44years (IQR 35.1), and age at GD clinical onset was 23years (IQR 23.4). Parkinsonism, including Parkinson's disease and Lewy Body dementia, was the most reported neurological manifestation. Other symptoms and signs encompassed oculomotor abnormalities, peripheral neuropathy, seizures, myoclonus, and cerebellar, cognitive and psychiatric symptoms. The genotype N370S/N370S mostly presented with Parkinsonism and the L444P variant with severe and earlier neurological symptoms. The findings of this systematic review highlight: (1) the relevance of a comprehensive neurological assessment in GD patients, and (2) the importance of considering possible undiagnosed GD in adult patients with mild systemic symptoms presenting unexplained neurological symptoms.

Toehold Strand Displacement-Mediated Exponential HCR for Highly Sensitive and Specific Analysis of miRNA in Living Cells.

As an important disease biomarker, the development of sensitive detection strategies for miRNA, especially intracellular miRNA imaging strategies, is helpful for early diagnosis of diseases, pathological research, and drug development. Hybridization chain reaction (HCR) is widely used for miRNA imaging analysis because of its high specificity and lack of biological enzymes. However, the classic HCR reaction exhibits linear amplification with low efficiency, limiting its use for the rapid analysis of trace miRNA in living cells. To address this problem, we proposed a toehold-mediated exponential HCR (TEHCR) to achieve highly sensitive and efficient imaging of miRNA in living cells using β-FeOOH nanoparticles as transfection vectors. The detection limit of TEHCR was as low as 92.7 fM, which was 8.8 × 103 times lower compared to traditional HCR, and it can effectively distinguish single-base mismatch with high specificity. The TEHCR can also effectively distinguish the different expression levels of miRNA in cancer cells and normal cells. Furthermore, TEHCR can be used to construct OR logic gates for dual miRNA analysis without the need for additional probes, demonstrating high flexibility. This method is expected to play an important role in clinical miRNA-related disease diagnosis and drug development as well as to promote the development of logic gates.

Ultrasensitive Graphene Field-Effect Biosensors Based on Ferroelectric Polarization of Lithium Niobate for Breast Cancer Marker Detection.

Herein, we present a novel ultrasensitive graphene field-effect transistor (GFET) biosensor based on lithium niobate (LiNbO3) ferroelectric substrate for the application of breast cancer marker detection. The electrical properties of graphene are varied under the electrostatic field, which is generated through the spontaneous polarization of the ferroelectric substrate. It is demonstrated that the properties of interface between graphene and solution are also altered due to the interaction between the electrostatic field and ions. Compared with the graphene field-effect biosensor based on the conventional Si/SiO2 gate structure, our biosensor achieves a higher sensitivity to 64.7 mV/decade and shows a limit of detection down to 1.7 fM (equivalent to 12 fg·mL-1) on the detection of microRNA21 (a breast cancer marker). This innovative design combining GFETs with ferroelectric substrates holds great promise for developing an ultrahigh-sensitivity biosensing platform based on graphene that enables rapid and early disease diagnosis.

Stakeholder perspectives on contributors to delayed and inaccurate diagnosis of cardiovascular disease and their implications for digital health technologies: a UK-based qualitative study

ObjectiveThe aim of this study is to understand stakeholder experiences of diagnosis of cardiovascular disease (CVD) to support the development of technological solutions that meet current needs. Specifically, we aimed...

Diagnosis of Nonalcoholic Fatty Liver Disease via a H2S-Responsive Bioluminescent Probe Combined with Firefly Luciferase mRNA Delivery.

The early detection of nonalcoholic fatty liver disease (NAFLD) through bioluminescent probes is of great significance. However, there remains a challenge to apply them in nontransgenic natural animals due to the lack of exogenous luciferase. To address this issue, we herein report a new strategy for in situ monitoring of endogenous hydrogen sulfide (H2S) in the liver of NAFLD mice by leveraging a H2S-responsive bioluminescent probe (H-Luc) combined with firefly luciferase (fLuc) mRNA delivery. The probe H-Luc was created by installing a H2S recognition moiety, 2,4-dinitrophenol, onto the luciferase substrate (d-luciferin), which is allowed to release cage-free d-luciferin in the presence of H2S via a nucleophilic aromatic substitution reaction. In the meantime, the intracellular luciferase was introduced by lipid nanoparticle (LNP)-mediated fLuc mRNA delivery, rendering it suitable for bioluminescence (BL) imaging in vitro and in vivo. Based on this luciferase-luciferin system, the endogenous H2S could be sensitively and selectively detected in living cells, showing a low limit of detection (LOD) value of 0.72 μM. More importantly, after systematic administration of fLuc mRNA-loaded LNPs in vivo, H-Luc was able to successfully monitor the endogenous H2S levels in the NAFLD mouse model for the first time, displaying a 28-fold higher bioluminescence intensity than that in the liver of normal mice. We believe that this strategy may shed new light on the diagnosis of inflammatory liver disease, further elucidating the roles of H2S.

A General Cyanine‐Based Platform for Designing Robust Dual‐Channel Near‐Infrared Fluorescent and Photoacoustic Probes

AbstractThe creation of versatile platforms for developing dual‐channel near‐infrared fluorescent (NIRF) and photoacoustic (PA) probes, especially those engineered to minimize channel crosstalk, is crucial for precise biomarker detection. However, such platforms remain scarce. To bridge this gap, this study introduces an innovative cyanine‐based platform, CySN. The CySN platform showcases remarkable wavelength‐shifting properties, including large fluorescent modality shift (68 nm) and PA modality shift (145 nm) after the decaging reaction. These substantial changes lead to an exceptionally high ratiometric NIRF change of 603‐fold and ratiometric PA change of 261‐fold. Leveraging the CySN platform, dual‐channel NIRF/PA probes have been successfully developed for detecting both small molecule biomarker (H2O2) and enzyme biomarker (esterase). These probes demonstrate the ability to detect their targets through dual‐channel NIRF/PA detection with high sensitivity and selectivity in vitro. Furthermore, the probes effectively harness NIRF signals to image target analytes in living cells. Notably, the probes demonstrate the capability to accurately diagnose tumors by detecting tumor markers (H2O2 and esterase), revealing a 3.6 to 7‐fold ratiometric PA enhancement over normal tissue. Therefore, the CySN platform holds the potential to further advance the development of dual‐channel NIRF/PA probes for biomolecule detection in disease diagnosis.

How Does the Concentration of Technetium-99m Radiolabeled Gold Nanoparticles Affect Their In Vivo Biodistribution?

Gold nanoparticles (AuNPs) radiolabeled with therapeutic and diagnostic radioisotopes have been broadly studied as a promising platform for early diagnosis and treatment of many diseases including cancer. Our main goal for this study was the comparison of the biodistribution profiles of four different concentrations of gold nanoconjugates radiolabeled with Technetium-99m (99mTc). More specifically, AuNPs with an average diameter of 2 nm were functionalized with a tridentate thiol ligand. Four different concentrations were radiolabeled with 99mTc-tricarbonyls with high radiolabeling yields (>85%) and were further purified, leading to radiochemical purity of >95%. In vitro stability of the radiolabeled nanoconstructs was examined in cysteine and histidine solutions as well as in human serum, exhibiting robust radiolabeling up to 24 h post-preparation. Moreover, in vitro cytotoxicity studies were carried out in 4T1 murine mammary cancer cells. In vivo tracking of the radiolabeled nanoconjugates at both concentrations was examined in normal mice in order to examine the effect of AuNPs’ concentration on their in vivo kinetics. Our work demonstrates that varying concentrations of radiolabeled AuNPs lead to notably different biodistribution profiles.

Application of metagenomic next-generation sequencing in optimizing the diagnosis of ascitic infection in patients with liver cirrhosis

BackgroundMetagenomic next-generation sequencing (mNGS) is an emerging technique for the clinical diagnosis of infectious disease that has rarely been used for the diagnosis of ascites infection in patients with cirrhosis. This study compared mNGS detection with conventional culture methods for the on etiological diagnosis of cirrhotic ascites and evaluated the clinical effect of mNGS.MethodsA total of 109 patients with ascites due to cirrhosis were included in the study. We compared mNGS with conventional culture detection by analyzing the diagnostic results, pathogen species and clinical effects. The influence of mNGS on the diagnosis and management of ascites infection in patients with cirrhosis was also evaluated.ResultsAscites cases were classified into three types: spontaneous bacterial peritonitis (SBP) (16/109, 14.7%), bacterascites (21/109, 19.3%) and sterile ascites (72/109, 66.1%). In addition, 109 patients were assigned to the ascites mNGS-positive group (80/109, 73.4%) or ascites mNGS-negative group (29/109, 26.6%). The percentage of positive mNGS results was significantly greater than that of traditional methods (73.4% vs. 28.4%, P < 0.001). mNGS detected 43 strains of bacteria, 9 strains of fungi and 8 strains of viruses. Fourteen bacterial strains and 3 fungal strains were detected via culture methods. Mycobacteria, viruses, and pneumocystis were detected only by the mNGS method. The mNGS assay produced a greater polymicrobial infection rate than the culture method (55% vs. 16%). Considering the polymorphonuclear neutrophil (PMN) counts, the overall percentage of pathogens detected by the two methods was comparable, with 87.5% (14/16) in the PMN ≥ 250/mm3 group and 72.0% (67/93) in the PMN < 250/mm3 group (P > 0.05). Based on the ascites PMN counts combined with the mNGS assay, 72 patients (66.1%) were diagnosed with ascitic fluid infection (AFI) (including SBP and bacterascites), whereas based on the ascites PMN counts combined with the culture assay, 37 patients (33.9%) were diagnosed with AFI (P < 0.05). In 60 (55.0%) patients, the mNGS assay produced positive clinical effects; 40 (85.7%) patients had their treatment regimen adjusted, and 48 patients were improved. The coincidence rate of the mNGS results and clinical findings was 75.0% (60/80).ConclusionsCompared with conventional culture methods, mNGS can improve the detection rate of ascites pathogens, including bacteria, viruses, and fungi, and has significant advantages in the diagnosis of rare pathogens and pathogens that are difficult to culture; moreover, mNGS may be an effective method for improving the diagnosis of ascites infection in patients with cirrhosis, guiding early antibiotic therapy, and for reducing complications related to abdominal infection. In addition, explaining mNGS results will be challenging, especially for guiding the treatment of infectious diseases.

Primary adrenal insufficiency developed 22 years after the diagnosis of light and heavy chain deposition disease: a case report.

Monoclonal immunoglobulin deposition diseases (MIDDs), including light and heavy chain deposition disease (LHCDD), are rare and heterogeneous disorders associated with underlying B-cell clonal disorders. Adrenal involvement is a potential extrarenal manifestation of MIDDs; however, limited data are available regarding its prevalence and clinical presentation. Herein, the present report describes, for the first time, a case of primary adrenal insufficiency that developed twenty-two years after a diagnosis of LHCDD had been made. A 69year-old woman with a 10year history of hemodialysis suddenly became bedridden after falling down stairs in the absence of focal neurological deficits. Subsequently, she experienced appetite loss, nausea, vomiting, a fever of unknown origin, and unexplained hypotension. Several months later, primary adrenal insufficiency and normal pressure hydrocephalus were diagnosed and successfully managed. The long-term clinical prognosis of MIDDs has not been fully elucidated despite recent advances in the management of the disorders. This report may contribute to improving our understanding of the disease course.

High-Order line graphs of fMRI data in major depressive disorder.

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting.