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      The role of deep learning in diagnosing colorectal cancer

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          Abstract

          Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.

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          High-performance medicine: the convergence of human and artificial intelligence

          Eric Topol (2019)
          The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.
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            Colorectal cancer screening for average-risk adults: 2018 guideline update from the American Cancer Society

            Kevin C Oeffinger, Ya-Chen Tina Shih, Louise C Walter … (2018)
            In the United States, colorectal cancer (CRC) is the fourth most common cancer diagnosed among adults and the second leading cause of death from cancer. For this guideline update, the American Cancer Society (ACS) used an existing systematic evidence review of the CRC screening literature and microsimulation modeling analyses, including a new evaluation of the age to begin screening by race and sex and additional modeling that incorporates changes in US CRC incidence. Screening with any one of multiple options is associated with a significant reduction in CRC incidence through the detection and removal of adenomatous polyps and other precancerous lesions and with a reduction in mortality through incidence reduction and early detection of CRC. Results from modeling analyses identified efficient and model-recommendable strategies that started screening at age 45 years. The ACS Guideline Development Group applied the Grades of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria in developing and rating the recommendations. The ACS recommends that adults aged 45 years and older with an average risk of CRC undergo regular screening with either a high-sensitivity stool-based test or a structural (visual) examination, depending on patient preference and test availability. As a part of the screening process, all positive results on noncolonoscopy screening tests should be followed up with timely colonoscopy. The recommendation to begin screening at age 45 years is a qualified recommendation. The recommendation for regular screening in adults aged 50 years and older is a strong recommendation. The ACS recommends (qualified recommendations) that: 1) average-risk adults in good health with a life expectancy of more than 10 years continue CRC screening through the age of 75 years; 2) clinicians individualize CRC screening decisions for individuals aged 76 through 85 years based on patient preferences, life expectancy, health status, and prior screening history; and 3) clinicians discourage individuals older than 85 years from continuing CRC screening. The options for CRC screening are: fecal immunochemical test annually; high-sensitivity, guaiac-based fecal occult blood test annually; multitarget stool DNA test every 3 years; colonoscopy every 10 years; computed tomography colonography every 5 years; and flexible sigmoidoscopy every 5 years. CA Cancer J Clin 2018;68:250-281. © 2018 American Cancer Society.
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              Artificial intelligence in medicine.

              Johanne Tremblay, Pavel Hamet (2017)
              Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Prz Gastroenterol
                Journal ID (iso-abbrev): Prz Gastroenterol
                Journal ID (publisher-id): PG
                Title: Przegla̜d Gastroenterologiczny
                Publisher: Termedia Publishing House
                ISSN (Print): 1895-5770
                ISSN (Electronic): 1897-4317
                Publication date (Electronic): 17 July 2023
                Publication date (Print): 2023
                Volume: 18
                Issue: 3
                Pages: 266-273
                Affiliations
                [1 ]Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
                [2 ]Department of Surgery, General University Hospital of Patras, Patras, Greece
                [3 ]Department of Cardiothoracic Surgery, General University Hospital of Patras, Patras, Greece
                [4 ]Department of Cardiology, Elpis General Hospital, Athens, Greece
                [5 ]Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
                Author notes
                Address for correspondence: Dr. Francesk Mulita, Department of Surgery, General University Hospital of Patras, Patras, Greece, e-mail: oknarfmulita@ 123456hotmail.com
                Article
                Publisher ID: 51103
                DOI: 10.5114/pg.2023.129494
                PMC ID: 10626379
                PubMed ID: 37937113
                SO-VID: 35a20c74-2de5-479e-baed-cce4d0b09a6a
                Copyright © Copyright © 2023 Termedia
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License ( http://creativecommons.org/licenses/by-nc-sa/4.0/)

                History
                Date received : 15 January 2023
                Date accepted : 24 February 2023
                Categories
                Subject: Review Paper

                Keywords: medical imaging,deep learning,endoscopy,artificial intelligence,histologic examination,colorectal cancer
                Data availability:
                Keywords: medical imaging, deep learning, endoscopy, artificial intelligence, histologic examination, colorectal cancer

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