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      Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

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          Abstract

          The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization ( i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

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          Radiomics: Images Are More than Pictures, They Are Data

          Robert Gillies, Paul E Kinahan, Hedvig Hricak (2015)
          This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
          Article 0 comments Cited 1968 times – based on 0 reviews     Review now
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            SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

            Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla (2017)
            We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1] . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3] , DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet.
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              Radiomics: extracting more information from medical images using advanced feature analysis.

              Philippe Lambin, Emmanuel Rios-Velazquez, Ralph Leijenaar … (2012)
              Solid cancers are spatially and temporally heterogeneous. This limits the use of invasive biopsy based molecular assays but gives huge potential for medical imaging, which has the ability to capture intra-tumoural heterogeneity in a non-invasive way. During the past decades, medical imaging innovations with new hardware, new imaging agents and standardised protocols, allows the field to move towards quantitative imaging. Therefore, also the development of automated and reproducible analysis methodologies to extract more information from image-based features is a requirement. Radiomics--the high-throughput extraction of large amounts of image features from radiographic images--addresses this problem and is one of the approaches that hold great promises but need further validation in multi-centric settings and in the laboratory. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                M Alvaro Berbís
                José Aneiros-Fernández
                F Javier Mendoza Olivares
                Enrique Nava
                Antonio Luna
                Journal
                Journal ID (nlm-ta): World J Gastroenterol
                Journal ID (iso-abbrev): World J Gastroenterol
                Journal ID (publisher-id): WJG
                Title: World Journal of Gastroenterology
                Publisher: Baishideng Publishing Group Inc
                ISSN (Print): 1007-9327
                ISSN (Electronic): 2219-2840
                Publication date (Print): 21 July 2021
                Publication date (Electronic): 21 July 2021
                Volume: 27
                Issue: 27
                Pages: 4395-4412
                Affiliations
                Department of R&D, HT Médica, Madrid 28046, Madrid, Spain
                Department of Pathology, Hospital Universitario Clínico San Cecilio, Granada 18012, Spain
                Department of Gastroenterology, Fatima Clinic, Sevilla 41012, Spain
                Department of Communications Engineering, University of Málaga, Malaga 29016, Spain
                MRI Unit, Department of Radiology, HT Médica, Jaén 23007, Spain. aluna70@ 123456htime.org
                Author notes

                Author contributions: All authors contributed to this paper with literature review and analysis and approval of the final version.

                Corresponding author: Antonio Luna, MD, PhD, Doctor, MRI Unit, Department of Radiology, HT Médica, C/ Carmelo Torres 2, Jaén 23007, Spain. aluna70@ 123456htime.org

                Article
                Other ID: jWJG.v27.i27.pg4395
                DOI: 10.3748/wjg.v27.i27.4395
                PMC ID: 8316909
                PubMed ID: 34366612
                SO-VID: 00307255-79b4-40bd-8a5f-11705e24b337
                Copyright © ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
                License:

                This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/

                History
                Date received : 28 January 2021
                Date revision received : 14 April 2021
                Date accepted : 7 June 2021
                Categories
                Subject: Minireviews

                Keywords: artificial intelligence,machine learning,radiology,endoscopy,pathology,gastroenterology
                Data availability:
                Keywords: artificial intelligence, machine learning, radiology, endoscopy, pathology, gastroenterology

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