Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)
Ontology highlight
ABSTRACT:
DISEASE(S): Cade,Colonic Polyps,Artificial Intelligence,Surveillance Endoscopy For Colorectal Cancer,Colorectal Neoplasms,Polyps Of Colon
PROVIDER: 2386702 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:Interventions: Use the Artificial Intelligence Colonoscopy first to CRC screening, if the polyp or adenoma was seen, remove it
After that, use the conventional colonoscopy to screening again for detection the polyp or adenoma,Use the Conventional colonoscopy first to CRC screening, if the polyp or adenoma was seen, remove it
After that, use the Artificial intelligence colonoscopy to screening again for detection the polyp or adenoma;Active Comparator Device,Active Comparator Device;CADe-Conventional,Conventional-CADe
Primary outcome(s): Adenoma Miss Rate the day colonoscopy the number adenoma detected at second colonoscopy divided by the total number of lesions detected at first and second colonoscopy
Study Design: Randomized
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Project description:Artificial Intelligence-powered Drug Discovery against CTLA-4 in Cancer
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Project description:We have performed paired-end RNA-sequencing on budding yeast (BY4741), exposed to 4-nitrocatechol and 3,4-Dihydroxyphenylacetic acid (DOPAC). These two human metabolites were predicted as potential carcinogens using Metabokiller, an artificial intelligence-driven carcinogenicity predictor. The dataset contains 3 biological conditions, i.e. untreated (NC), DOPAC treated (DP), and 4-nitrocatechol treated (4NC). Two rounds of selection procedures have been introduced. The first selection was for canavanine resistance, and the followed selection was for rapid cell division (inferred using growth assay). The experiment was performed in biological triplicates.
2022-06-09 | E-MTAB-11179 | biostudies-arrayexpress
Project description:Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide, with rates of CRC predicted to increase. Colonoscopy is currently the gold standard of screening for CRC. Artificial intelligence (AI) is seen as a solution to bridge this gap in adenoma detection, which is a quality indicator in colonoscopy. AI systems utilize deep neural networks to enable computer-aided detection (CADe) and computer-aided classification (CADx). CADe is concerned with the detection of polyps during colonoscopy, which in turn is postulated to help decrease the adenoma miss-rate.
In contrast, CADx deals with the interpretation of polyp appearance during colonoscopy to determine the predicted histology. Prediction of polyp histology is crucial in helping Clinicians decide on a "resect and discard" or "diagnose and leave strategy". It is also useful for the Clinician to be aware of the predicted histology of a colorectal polyp in determining the appropriate method of resection in terms of safety and efficacy. While CADe has been studied extensively in randomized controlled trials, there is a lack of prospective data validating the use of CADx in a clinical setting to predict polyp histology.
The investigators plan to conduct a prospective, multi-centre clinical trial to validate the accuracy of CADx support for prediction of polyp histology in real-time colonoscopy.
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Project description:Cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with B7 proteins CD80 and CD86. CTLA-4 is the first immune checkpoint targeted with a monoclonal antibody inhibitor. Checkpoint inhibitors have generated durable responses in many cancer patients, representing a revolutionary milestone in cancer immunotherapy. However, therapeutic efficacy is limited to a small portion of patients, and immune-related adverse events are noteworthy, especially for monoclonal antibodies directed against CTLA-4. Previously, small molecules have been developed to impair the CTLA-4: CD80 interaction; however, they directly targeted CD80 and not CTLA-4. In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to target CTLA-4. We validated primary hits with biochemical, biophysical, immunological, and experimental animal assays. We then optimized lead compounds and obtained inhibitors with an inhibitory concentration of 1 micromole in disrupting the interaction between CTLA-4 and CD80. Unlike ipilimumab, these small molecules did not degrade CTLA-4. Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. This project supports an AI-based framework in designing small molecules targeting immune checkpoints for cancer therapy.
2024-02-15 | GSE228560 | GEO
Project description:During the COVID-19 pandemic, Multisystem Inflammatory Syndrome in Children (MIS-C) emerged as a disease with unknown causes and no specific diagnostic test. We used artificial intelligence in a nationwide study from April 2020 to March 2022 to identify a 4-protein diagnostic signature for MIS-C. This signature had an AUC of 1.0 for distinguishing MIS-C from other febrile diseases. Our analysis also revealed intricate protein patterns, indicating that MIS-C is associated with immune dysregulation, lipid metabolism changes, increased coagulation activity, and tissue remodeling.
2024-05-14 | PXD045661 | Pride
Project description:Background and aimsArtificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis.MethodsThe MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board.ResultsDeep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images.ConclusionsThe implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
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Project description:The goal of this substudy is to investigate the accuracy of a computer-aided polyp characterization (CADx) system. The main question[s] it aims to answer are:
• How high is the specificity of the AI system when characterizing colorectal polyps
Participants will receive a standard colonoscopy, assisted by the artificial intelligence (AI) assisted system GI Genius.
Researchers will compare the AI system´s characterization with the histopathology to see how accurate the system is.
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Project description:Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is the need for biomarkers of residual disease, and recurrence. We analysed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from 6 children bearing various subtypes of MB and 6 controls needing EVD insertion for unrelated causes. Samples included total CSF, Microvesicles, Exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid Chromatography-Coupled Tandem Mass Spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4), and laminin B-type (LMNB1) as proteins that maximize the discrimination between control and MB samples, respectively. Artificial intelligence was able to distinguish between MB vs non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients quality of life.
2022-10-14 | PXD035292 | Pride
Project description:Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.
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