Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach

Koh, Joel En Wei; Michele, Simona De; Sudarshan, Vidya K.; Vicnesh, Jahmunah; Ciaccio, Edward J.; Ooi, Chui Ping; Gururajan, Raj; Gururajan, Rashmi; Oh, Shu Lih; Lewis, Suzanne K.; Green, Peter H.; Bhagat, Govind; Acharya, U. Rajendra

doi:10.1016/j.cmpb.2021.106010

Cited by 23 publications

(10 citation statements)

References 45 publications

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“…The suggested approach for an automatic classification of biopsy pictures can help with the process of evaluating villous atrophy using Marsh score, suggesting that automation of biopsy images is a feasible task. Nevertheless, more amount of data with improved quality (e.g., biopsy images well-orientated) are needed to appropriately train the model, enhancing its predictive power [308]. Remarkably, the reported results have shown great potential for AI/ML in automation of biopsy images for detecting celiac disease as well as other disorders.…”

Section: Ai In Gastroenterologymentioning

confidence: 91%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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The huge advancement of Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques enables the scientific community to gain access to biological datasets, clinical data, and several databases containing billions of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields including the medical one. 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). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, the translational research is benefitting from computer-based approaches, transforming the design and execution of the translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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Section: Ai In Gastroenterologymentioning

confidence: 91%

Artificial Intelligence in Translational Medicine

Brogi¹,

Calderone²

2021

Preprint

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“…The suggested approach for an automatic classification of biopsy pictures can help with the process of evaluating villous atrophy using the Marsh score, suggesting that automation of biopsy images is a feasible task. Nevertheless, more data with improved quality (e.g., well-orientated biopsy images) are needed to appropriately train the model, enhancing its predictive power [319]. Remarkably, the reported results have shown great potential for AI/ML in the automation of biopsy images for detecting celiac disease, as well as other disorders.…”

Section: Ai In Gastroenterologymentioning

confidence: 92%

“…The approach for an automatic classification of biopsy pictures can help with the process of evaluating villous atrophy, suggesting that automation of biopsy images is a feasible task [319] DL decision support method based on DNN algorithm Approach for detecting Helicobacter pylori considering gastric biopsies…”

Section: Ai In Gastroenterologymentioning

confidence: 99%

Artificial Intelligence in Translational Medicine

Brogi

Calderone

2021

IJTM

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The huge advancement in Internet web facilities as well as the progress in computing and algorithm development, along with current innovations regarding high-throughput techniques, enable the scientific community to gain access to biological datasets, clinical data and several databases containing billions of pieces of information concerning scientific knowledge. Consequently, during the last decade the system for managing, analyzing, processing and extrapolating information from scientific data has been considerably modified in several fields, including the medical one. 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). Beyond the terminology, these computational techniques are revolutionizing the scientific research in drug discovery pitch, from the preclinical studies to clinical investigation. Interestingly, between preclinical and clinical research, translational research is benefitting from computer-based approaches, transforming the design and execution of translational research, resulting in breakthroughs for advancing human health. Accordingly, in this review article, we analyze the most advanced applications of AI in translational medicine, providing an up-to-date outlook regarding this emerging field.

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“…Although these data seem quite encouraging, it must be kept in mind that the gastrointestinal tract is a complex environment, and several factors can affect the obtained images, as discussed in depth by Hegenbart et al [ 65 ]. The deep learning approach could be useful also in the evaluation of duodenal biopsies, as demonstrated by different authors that were able to develop artificial intelligence-based methods for the correct classification of duodenal samples[ 66 - 68 ]. Machine learning has also been employed to improve the diagnosis based on B/T cell repertoire[ 69 , 70 ]; although the data obtained in the latter case are quite interesting, this approach is not easily applicable for routine diagnosis.…”

Section: Machine Learningmentioning

confidence: 99%

Celiac disease: From genetics to epigenetics

Gnodi¹,

Meneveri²,

Barisani³

2022

WJG

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Celiac disease (CeD) is a multifactorial autoimmune disorder spread worldwide. The exposure to gluten, a protein found in cereals like wheat, barley and rye, is the main environmental factor involved in its pathogenesis. Even if the genetic predisposition represented by HLA-DQ2 or HLA-DQ8 haplotypes is widely recognised as mandatory for CeD development, it is not enough to explain the total predisposition for the disease. Furthermore, the onset of CeD comprehend a wide spectrum of symptoms, that often leads to a delay in CeD diagnosis. To overcome this deficiency and help detecting people with increased risk for CeD, also clarifying CeD traits linked to disease familiarity, different studies have tried to make light on other predisposing elements. These were in many cases genetic variants shared with other autoimmune diseases. Since inherited traits can be regulated by epigenetic modifications, also induced by environmental factors, the most recent studies focused on the potential involvement of epigenetics in CeD. Epigenetic factors can in fact modulate gene expression with many mechanisms, generating more or less stable changes in gene expression without affecting the DNA sequence. Here we analyze the different epigenetic modifications in CeD, in particular DNA methylation, histone modifications, non-coding RNAs and RNA methylation. Special attention is dedicated to the additional predispositions to CeD, the involvement of epigenetics in developing CeD complications, the pathogenic pathways modulated by epigenetic factors such as microRNAs and the potential use of epigenetic profiling as biomarker to discriminate different classes of patients.

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Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach

Cited by 23 publications

References 45 publications

Artificial Intelligence in Translational Medicine

Artificial Intelligence in Translational Medicine

Artificial Intelligence in Translational Medicine

Celiac disease: From genetics to epigenetics

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