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Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children

Sana Syed, Mohammad Al-Boni, Marium N. Khan, Kamran Sadiq, Najeeha T. Iqbal, Christopher A. Moskaluk, Paul Kelly, Beatrice Amadi, S. Asad Ali, Sean R. Moore, Donald E. Brown
2019 JAMA Network Open  
Duodenal biopsies from children with enteropathies associated with undernutrition, such as environmental enteropathy (EE) and celiac disease (CD), display significant histopathological overlap.  ...  Methods This study is a prospective diagnostic study designed to develop and validate a predictive machine learning model for the interpretation of duodenal biopsy slides and feature detection in diseased  ...  Strengths of our study include a novel machine learning-based histopathological analysis for identifying and differentiating between gastrointestinal diseases and control images and the use of a DNN for  ... 
doi:10.1001/jamanetworkopen.2019.5822 pmid:31199451 pmcid:PMC6575155 fatcat:xcgokqszkrhlfpe2ddnumuddmm

Current Evidence on Computer-Aided Diagnosis of Celiac Disease: Systematic Review

Adriana Molder, Daniel Vasile Balaban, Mariana Jinga, Cristian-Constantin Molder
2020 Frontiers in Pharmacology  
Celiac disease (CD) is a chronic autoimmune disease that occurs in genetically predisposed individuals in whom the ingestion of gluten leads to damage of the small bowel.  ...  computer technology which can be used for computer aided diagnosis of celiac disease.  ...  resources for the machine to learn by itself.  ... 
doi:10.3389/fphar.2020.00341 pmid:32372947 pmcid:PMC7179080 fatcat:otvravgr6necppulfjj4uojzse

Impact of image segmentation techniques on celiac disease classification using scale invariant texture descriptors for standard flexible endoscopic systems

2020 Turkish Journal of Electrical Engineering and Computer Sciences  
For this reason, a hybrid machine learning methods 8 have been applied for the CAD of celiac disease.  ...  detection (CAD) systems in endoscopy are a newly emerging technology to enhance the 7 diagnostic accuracy of the disease and to save time and manpower.  ...  As a result of these investigations, we designed 22 a hybrid system based on machine learning algorithms for automated CAD of CD with endoscopic images. 23 The schema of the recommended CAD algorithm is  ... 
doi:10.3906/elk-2002-171 fatcat:hv3s2qepmffc5aqntar7r37dbq

Clinical Applications of Artificial Intelligence—An Updated Overview

Ștefan Busnatu, Adelina-Gabriela Niculescu, Alexandra Bolocan, George E. D. Petrescu, Dan Nicolae Păduraru, Iulian Năstasă, Mircea Lupușoru, Marius Geantă, Octavian Andronic, Alexandru Mihai Grumezescu, Henrique Martins
2022 Journal of Clinical Medicine  
Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most  ...  Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing  ...  patients with celiac and monitoring for use in detection SVM Serum samples from 90 patients with biopsy-proven celiac disease and 79 healthy individuals for the training dataset controls for the validation  ... 
doi:10.3390/jcm11082265 pmid:35456357 pmcid:PMC9031863 fatcat:mtzdrc7nyzgbjeyou3h4cuso7a

Artificial Intelligence for Understanding Imaging, Text, and Data in Gastroenterology

Ryan W Stidham
2020 Gastroenterology & hepatology  
Ongoing research is studying the use of AI for automated interpretation of text from colonoscopy and clinical documents for improved quality and patient phenotyping as well as enhanced detection and descriptions  ...  AI is powered by computational methods that allow machines to replicate clinical pattern recognition used by gastroenterology specialists to interpret endoscopic or cross-sectional images; understand the  ...  In a prospective study of 102 children from 3 countries, CNN models automatically differentiated celiac disease, environmental enteropathy, and normal cases using duodenal biopsy images with a case detection  ... 
pmid:34035738 pmcid:PMC8132644 fatcat:vlxdtb36anen3pcbhtpxdqmhpq

Artificial Intelligence in Translational Medicine

Simone Brogi, Vincenzo Calderone
2021 International Journal of Translational Medicine  
As a consequence of the mentioned scenario, scientific vocabulary was enriched by novel lexicons such as machine learning (ML)/deep learning (DL) and overall artificial intelligence (AI).  ...  Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijtm1030016 fatcat:c6g6ld26gjg6jbkcddauo44qvu

Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks [article]

Kamran Kowsari, Rasoul Sali, Marium N. Khan, William Adorno, S. Asad Ali, Sean R. Moore, Beatrice C. Amadi, Paul Kelly, Sana Syed, Donald E. Brown
2019 arXiv   pre-print
Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic  ...  We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images.  ...  This model proposes a transfer learning based approach for the classification of stained histology images. They also applied stain normalization before using images for fine tuning the model.  ... 
arXiv:1904.05773v5 fatcat:mch3wgk6uvgndif5soqwlajpum

Application of artificial intelligence in gastroenterology

Young Joo Yang, Chang Seok Bang
2019 World Journal of Gastroenterology  
These researchers used ultrasound images of 63 patients, and the gold standard for labeling for each patient was the pathologic results of a liver biopsy.  ...  Zhou et al [63] established a CNN model for the classification of celiac disease from control with capsule endoscopy clips from six celiac disease patients and five controls.  ... 
doi:10.3748/wjg.v25.i14.1666 fatcat:jlvvdofvljgj7pbkfl5fdht4s4

Precision medicine and machine learning towards the prediction of the outcome of potential celiac disease

Francesco Piccialli, Francesco Calabrò, Danilo Crisci, Salvatore Cuomo, Edoardo Prezioso, Roberta Mandile, Riccardo Troncone, Luigi Greco, Renata Auricchio
2021 Scientific Reports  
Starting from a follow-up dataset available for PCD, we propose the application of Machine Learning (ML) methodologies to extend the analysis on available clinical data and to detect most influent features  ...  AbstractPotential Celiac Patients (PCD) bear the Celiac Disease (CD) genetic predisposition, a significant production of antihuman transglutaminase antibodies, but no morphological changes in the small  ...  Acknowledgements The support of Nastri FD Project is gratefully acknowledged.  ... 
doi:10.1038/s41598-021-84951-x pmid:33707543 pmcid:PMC7952550 fatcat:mgjqzazwqvaqripi5dwgxn3ak4

Celiac Disease Detection from Videocapsule Endoscopy Images Using Strip Principal Component Analysis

Bing Nan Nan Li, Xinle Wang, Rong Wang, Teng Zhou, Rongke Gao, Edward J. Ciaccio, Peter H. Green
2019 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification.  ...  The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition.  ...  However, the use of automated programs would be assistive to detect the subtle presence of villous atrophy not evident by visual inspection.  ... 
doi:10.1109/tcbb.2019.2953701 pmid:31751282 fatcat:m5jnnf3nijeljok6ssiupj2254

Deep Paediatric Gastroenterology with Blockchain

Dr. Yogesh Waikar
2022 Annals of Pediatric Gastroenterology & Hepatology  
The neural network has demonstrated 10, 11, 12 90.5% accuracy rate in identifying celiac disease from endoscopic images . 13 Comparative performance of the machine learning prediction model with pre-endoscopic  ...  Deep learning is the part of machine learning based on artificial neural networks .It uses algorithms to derive conclusions from the given inputs.  ... 
doi:10.5005/jp-journals-11009-0031 fatcat:uat3zp7hbfalrctwzmuggvmdna

Is Computer-Assisted Tissue Image Analysis the Future in Minimally Invasive Surgery? A Review on the Current Status of Its Applications

Vasilios Tanos, Marios Neofytou, Ahmed Samy Abdulhady Soliman, Panayiotis Tanos, Constantinos S. Pattichis
2021 Journal of Clinical Medicine  
We review and evaluate the impact of in vivo optical biopsies performed by tissue image analysis on the surgeon's diagnostic ability and sampling precision and investigate how operation complications could  ...  Our literature review summarizes the available data on CATIA of human tissues and explores the possibilities of computer-assisted early disease diagnoses, including cancer.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jcm10245770 pmid:34945066 pmcid:PMC8706291 fatcat:r755v3e4pvgi3dhjdk2gutb7v4

Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges

Sang Hoon Kim, Yun Jeong Lim
2021 Diagnostics  
Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process.  ...  Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases.  ...  Celiac Disease Computer-aided quantitative analysis of the existence and degree of celiac disease by a CNN-based DL model achieved 100% sensitivity and specificity for the external testing set [32] .  ... 
doi:10.3390/diagnostics11091722 pmid:34574063 fatcat:odopxje6wnaojc4lkwurxwv4ai

Artificial intelligence and capsule endoscopy: unravelling the future

Miguel Mascarenhas, João Afonso, Patrícia Andrade, Hélder Cardoso, Guilherme Macedo
2021 Annals of gastroenterology : quarterly publication of the Hellenic Society of Gastroenterology  
Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting.  ...  The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature.  ...  predicting the occurrence of the disease Ciaccio et al [54] Celiac disease 2014 Improve the image- based detection of villous atrophy and other abnormality in videocapsule endoscopy, using  ... 
doi:10.20524/aog.2021.0606 pmid:33948053 pmcid:PMC8079882 fatcat:xfs2grnewnafhmswrwedevw7fq

Role of Artificial Intelligence in Video Capsule Endoscopy

Ioannis Tziortziotis, Faidon-Marios Laskaratos, Sergio Coda
2021 Diagnostics  
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  ...  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  ...  [52] developed and evaluated a CNN for automated detection of SB ulcers in patients with Crohn's disease.  ... 
doi:10.3390/diagnostics11071192 fatcat:bi25mlkmdvan7gskr5orfruiyu
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