Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

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Abstract

Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.

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Deep learning in neural networks: An overview

Jürgen Schmidhuber (2015)

In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

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Quality indicators for colonoscopy and the risk of interval cancer.

Michal F Kaminski, Jaroslaw Regula, Ewa Kraszewska … (2010)

Although rates of detection of adenomatous lesions (tumors or polyps) and cecal intubation are recommended for use as quality indicators for screening colonoscopy, these measurements have not been validated, and their importance remains uncertain. We used a multivariate Cox proportional-hazards regression model to evaluate the influence of quality indicators for colonoscopy on the risk of interval cancer. Data were collected from 186 endoscopists who were involved in a colonoscopy-based colorectal-cancer screening program involving 45,026 subjects. Interval cancer was defined as colorectal adenocarcinoma that was diagnosed between the time of screening colonoscopy and the scheduled time of surveillance colonoscopy. We derived data on quality indicators for colonoscopy from the screening program's database and data on interval cancers from cancer registries. The primary aim of the study was to assess the association between quality indicators for colonoscopy and the risk of interval cancer. A total of 42 interval colorectal cancers were identified during a period of 188,788 person-years. The endoscopist's rate of detection of adenomas was significantly associated with the risk of interval colorectal cancer (P=0.008), whereas the rate of cecal intubation was not significantly associated with this risk (P=0.50). The hazard ratios for adenoma detection rates of less than 11.0%, 11.0 to 14.9%, and 15.0 to 19.9%, as compared with a rate of 20.0% or higher, were 10.94 (95% confidence interval [CI], 1.37 to 87.01), 10.75 (95% CI, 1.36 to 85.06), and 12.50 (95% CI, 1.51 to 103.43), respectively (P=0.02 for all comparisons). The adenoma detection rate is an independent predictor of the risk of interval colorectal cancer after screening colonoscopy. 2010 Massachusetts Medical 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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Journal

Journal ID (nlm-ta): J Clin Med

Journal ID (iso-abbrev): J Clin Med

Journal ID (publisher-id): jcm

Title: Journal of Clinical Medicine

Publisher: MDPI

ISSN (Electronic): 2077-0383

Publication date (Electronic): 15 October 2020

Publication date Collection: October 2020

Volume: 9

Issue: 10

Electronic Location Identifier: 3313

Affiliations

[1 ]Department of Internal Medicine, The Wright Center for Graduate Medical Education, Scranton, PA 18505, USA

[2 ]Saint Agnes Medical Center, Fresno, CA 93720, USA; rupindrmann@ 123456yahoo.com

[3 ]Department of Medicine, Geisinger Community Medical Center, Scranton, PA 18510, USA; drzainabgandhi@ 123456gmail.com

[4 ]Department of Gastroenterology and Hepatology, The University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; abhilash.perisetti@ 123456gmail.com

[5 ]Division of Gastroenterology, The Commonwealth Medical College, Wilkes Barre General Hospital, Wilkes-Barre, PA 18764, USA; amanali786@ 123456hotmail.com

[6 ]Digestive Care Associates, Kingston, PA 18704, USA; aminali92403@ 123456gmail.com

[7 ]Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, Fort Wayne, IN 46845, USA; neil.sharma@ 123456parkview.com

[8 ]Division of Interventional Oncology & Surgical Endoscopy, Indiana University School of Medicine, Fort Wayne, IN 46805, USA

[9 ]Department of Medicine, University of Texas Health San Antonio, San Antonio, TX 78229, USA; drsaligram@ 123456yahoo.com

[10 ]General and Advanced Endoscopy, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; Btharian@ 123456uams.edu

[11 ]Advanced Endoscopy Fellowship, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; Sinamdar@ 123456uams.edu

Author notes

[* ]Correspondence: doc.hemant@ 123456yahoo.com or goyalh@ 123456thewrightcenter.org ; Tel.: +1-570-914-8897

Author information

Zainab Gandhi https://orcid.org/0000-0002-7214-4981

Abhilash Perisetti https://orcid.org/0000-0003-4074-6395

Article

Publisher ID: jcm-09-03313

DOI: 10.3390/jcm9103313

PMC ID: 7602532

PubMed ID: 33076511

SO-VID: d1c38088-3117-462e-a534-158cfa4ef323

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer

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Artificial Intelligence in Medicine

Most cited references 69

Deep learning in neural networks: An overview

Quality indicators for colonoscopy and the risk of interval cancer.

Artificial intelligence in medicine.

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