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Zeng S, Qing Q, Xu W, Yu S, Zheng M, Tan H, Peng J, Huang J. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Front Med (Lausanne) 2024; 11:1365524. [PMID: 38784235 PMCID: PMC11111965 DOI: 10.3389/fmed.2024.1365524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Abstract
Precision medicine, characterized by the personalized integration of a patient's genetic blueprint and clinical history, represents a dynamic paradigm in healthcare evolution. The emerging field of personalized anesthesia is at the intersection of genetics and anesthesiology, where anesthetic care will be tailored to an individual's genetic make-up, comorbidities and patient-specific factors. Genomics and biomarkers can provide more accurate anesthetic protocols, while artificial intelligence can simplify anesthetic procedures and reduce anesthetic risks, and real-time monitoring tools can improve perioperative safety and efficacy. The aim of this paper is to present and summarize the applications of these related fields in anesthesiology by reviewing them, exploring the potential of advanced technologies in the implementation and development of personalized anesthesia, realizing the future integration of new technologies into clinical practice, and promoting multidisciplinary collaboration between anesthesiology and disciplines such as genomics and artificial intelligence.
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Affiliation(s)
- Shiyue Zeng
- Zhuzhou Clinical College, Jishou University, Jishou, China
| | - Qi Qing
- Zhuzhou Clinical College, Jishou University, Jishou, China
| | - Wei Xu
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Simeng Yu
- Zhuzhou Clinical College, Jishou University, Jishou, China
| | - Mingzhi Zheng
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Hongpei Tan
- Department of Radiology, Third Xiangya Hospital, Central South University, Changsha, China
| | - Junmin Peng
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Jing Huang
- Department of Anesthesiology, Zhuzhou Central Hospital, Zhuzhou, China
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2
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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3
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Gan P, Li P, Xia H, Zhou X, Tang X. The application of artificial intelligence in improving colonoscopic adenoma detection rate: Where are we and where are we going. GASTROENTEROLOGIA Y HEPATOLOGIA 2023; 46:203-213. [PMID: 35489584 DOI: 10.1016/j.gastrohep.2022.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 03/08/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023]
Abstract
Colorectal cancer (CRC) is one of the common malignant tumors in the world. Colonoscopy is the crucial examination technique in CRC screening programs for the early detection of precursor lesions, and treatment of early colorectal cancer, which can reduce the morbidity and mortality of CRC significantly. However, pooled polyp miss rates during colonoscopic examination are as high as 22%. Artificial intelligence (AI) provides a promising way to improve the colonoscopic adenoma detection rate (ADR). It might assist endoscopists in avoiding missing polyps and offer an accurate optical diagnosis of suspected lesions. Herein, we described some of the milestone studies in using AI for colonoscopy, and the future application directions of AI in improving colonoscopic ADR.
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Affiliation(s)
- Peiling Gan
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Peiling Li
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Huifang Xia
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xian Zhou
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Xiaowei Tang
- Department of Gastroenterology, Affiliated Hospital of Southwest Medical University, Luzhou, China; Department of Gastroenterology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
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4
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Eysenbach G, Liu SHK, Leung K, Wu JT, Zauber AG, Leung WK. The Impacts of Computer-Aided Detection of Colorectal Polyps on Subsequent Colonoscopy Surveillance Intervals: Simulation Study. J Med Internet Res 2023; 25:e42665. [PMID: 36763451 PMCID: PMC9960036 DOI: 10.2196/42665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 01/10/2023] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Computer-aided detection (CADe) of colorectal polyps has been shown to increase adenoma detection rates, which would potentially shorten subsequent surveillance intervals. OBJECTIVE The purpose of this study is to simulate the potential changes in subsequent colonoscopy surveillance intervals after the application of CADe in a large cohort of patients. METHODS We simulated the projected increase in polyp and adenoma detection by universal CADe application in our patients who had undergone colonoscopy with complete endoscopic and histological findings between 2016 and 2020. The simulation was based on bootstrapping the published performance of CADe. The corresponding changes in surveillance intervals for each patient, as recommended by the US Multi-Society Task Force on Colorectal Cancer (USMSTF) or the European Society of Gastrointestinal Endoscopy (ESGE), were determined after the CADe was determined. RESULTS A total of 3735 patients who had undergone colonoscopy were included. Based on the simulated CADe effect, the application of CADe would result in 19.1% (n=714) and 1.9% (n=71) of patients having shorter surveillance intervals, according to the USMSTF and ESGE guidelines, respectively. In particular, all (or 2.7% (n=101) of the total) patients who were originally scheduled to have 3-5 years of surveillance would have their surveillance intervals shortened to 3 years, following the USMSTF guidelines. The changes in this group of patients were largely attributed to an increase in the number of adenomas (n=75, 74%) rather than serrated lesions being detected. CONCLUSIONS Widespread adoption of CADe would inevitably increase the demand for surveillance colonoscopies with the shortening of original surveillance intervals, particularly following the current USMSTF guideline.
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Affiliation(s)
| | - Sze Hang Kevin Liu
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Kathy Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Joseph T Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
| | - Ann G Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Wai Keung Leung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong
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5
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Rao B H, Trieu JA, Nair P, Gressel G, Venu M, Venu RP. Artificial intelligence in endoscopy: More than what meets the eye in screening colonoscopy and endosonographic evaluation of pancreatic lesions. Artif Intell Gastrointest Endosc 2022; 3:16-30. [DOI: 10.37126/aige.v3.i3.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 03/07/2022] [Accepted: 05/07/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI)-based tools have ushered in a new era of innovation in the field of gastrointestinal (GI) endoscopy. Despite vast improvements in endoscopic techniques and equipment, diagnostic endoscopy remains heavily operator-dependent, in particular, colonoscopy and endoscopic ultrasound (EUS). Recent reports have shown that as much as 25% of colonic adenomas may be missed at colonoscopy. This can result in an increased incidence of interval colon cancer. Similarly, EUS has been shown to have high inter-observer variability, overlap in diagnoses with a relatively low specificity for pancreatic lesions. Our understanding of Machine-learning (ML) techniques in AI have evolved over the last decade and its application in AI–based tools for endoscopic detection and diagnosis is being actively investigated at several centers. ML is an aspect of AI that is based on neural networks, and is widely used for image classification, object detection, and semantic segmentation which are key functional aspects of AI-related computer aided diagnostic systems. In this review, current status and limitations of ML, specifically for adenoma detection and endosonographic diagnosis of pancreatic lesions, will be summarized from existing literature. This will help to better understand its role as viewed through the prism of real world application in the field of GI endoscopy.
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Affiliation(s)
- Harshavardhan Rao B
- Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
| | - Judy A Trieu
- Internal Medicine - Gastroenterology, Loyola University Medical Center, Maywood, IL 60153, United States
| | - Priya Nair
- Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
| | - Gilad Gressel
- Center for Cyber Security Systems and Networks, Amrita Vishwavidyapeetham, Kollam 690546, Kerala, India
| | - Mukund Venu
- Internal Medicine - Gastroenterology, Loyola University Medical Center, Maywood, IL 60153, United States
| | - Rama P Venu
- Department of Gastroenterology, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
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6
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Awidi M, Bagga A. Artificial intelligence and machine learning in colorectal cancer. Artif Intell Gastrointest Endosc 2022; 3:31-43. [DOI: 10.37126/aige.v3.i3.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/24/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US. Artificial intelligence (AI) with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types. This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them. Furthermore, we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions. Considering the evolving transition to more personalized and tailored treatment strategies for CRC, the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes. We also discuss the constraints and limitations of the use of AI systems. While the medical profession remains enthusiastic about the future of AI and machine learning, large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.
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Affiliation(s)
- Muhammad Awidi
- Internal Medicine, Beth Israel Lahey Health, Burlington, MA 01805, United States
| | - Arindam Bagga
- Internal Medicine, Tufts Medical Center, Boston, MA 02111, United States
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7
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Bejan V, Pîslaru M, Scripcariu V. Diagnosis of Peritoneal Carcinomatosis of Colorectal Origin Based on an Innovative Fuzzy Logic Approach. Diagnostics (Basel) 2022; 12:1285. [PMID: 35626439 PMCID: PMC9140813 DOI: 10.3390/diagnostics12051285] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/08/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023] Open
Abstract
Colorectal cancer represents one of the most important causes worldwide of cancer related morbidity and mortality. One of the complications which can occur during cancer progression, is peritoneal carcinomatosis. In the majority of cases, it is diagnosed in late stages due to the lack of diagnostic tools capable of revealing the early-stage peritoneal burden. Therefore, still associates with poor prognosis and quality of life, despite recent therapeutic advances. The aim of the study was to develop a fuzzy logic approach to assess the probability of peritoneal carcinomatosis presence using routine blood test parameters as input data. The patient data was acquired retrospective from patients diagnosed between 2010-2021. The developed model focuses on the specific quantitative alteration of these parameters in the presence of peritoneal carcinomatosis, which is an innovative approach as regards the literature in the field and validates the feasibility of using a fuzzy logic approach in the noninvasive diagnosis of peritoneal carcinomatosis.
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Affiliation(s)
- Valentin Bejan
- Department of Surgery, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Farmacy of Iași, 700115 Iasi, Romania;
| | - Marius Pîslaru
- Department of Engineering and Management, Faculty of Industrial Design and Business Management, “Gheorghe Asachi” Technical University of Iași, 700050 Iasi, Romania;
| | - Viorel Scripcariu
- Department of Surgery, Faculty of Medicine, “Gr. T. Popa” University of Medicine and Farmacy of Iași, 700115 Iasi, Romania;
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8
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Epperlein JP, Zayats M, Tirupathi S, Zhuk S, Tchrakian T, Mac Aonghusa P, O'Shea DF, Hardy NP, Dalli J, Cahill RA. Practical Perfusion Quantification in Multispectral Endoscopic Video: Using the Minutes after ICG Administration to Assess Tissue Pathology. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:428-437. [PMID: 35308965 PMCID: PMC8861725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The wide availability of near infrared light sources in interventional medical imaging stacks enables non-invasive quantification of perfusion by using fluorescent dyes, typically Indocyanine Green (ICG). Due to their often leaky and chaotic vasculatures, intravenously administered ICG perfuses through cancerous tissues differently. We investigate here how a few characteristic values derived from the time series of fluorescence can be used in simple machine learning algorithms to distinguish benign lesions from cancers. These features capture the initial uptake of ICG in the colon, its peak fluorescence, and its early wash-out. By using simple, explainable algorithms we demonstrate, in clinical cases, that sensitivity (specificity) rates of over 95% (95%) for cancer classification can be achieved.
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Affiliation(s)
| | | | | | | | | | | | | | - Niall P Hardy
- University College Dublin
- Mater Misericordiae University Hospital, Dublin, Ireland
| | - Jeffrey Dalli
- University College Dublin
- Mater Misericordiae University Hospital, Dublin, Ireland
| | - Ronan A Cahill
- University College Dublin
- Mater Misericordiae University Hospital, Dublin, Ireland
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9
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Abstract
Artificial intelligence (AI) is a fascinating new technology that incorporates machine learning and neural networks to improve existing technology or create new ones. Potential applications of AI are introduced to aid in the fight against colorectal cancer (CRC). This includes how AI will affect the epidemiology of colorectal cancer and the new methods of mass information gathering like GeoAI, digital epidemiology and real-time information collection. Meanwhile, this review also examines existing tools for diagnosing disease like CT/MRI, endoscopes, genetics, and pathological assessments also benefitted greatly from implementation of deep learning. Finally, how treatment and treatment approaches to CRC can be enhanced when applying AI is under discussion. The power of AI regarding the therapeutic recommendation in colorectal cancer demonstrates much promise in clinical and translational field of oncology, which means better and personalized treatments for those in need.
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Affiliation(s)
- Chaoran Yu
- Department of General Surgery, Shanghai Ninth People’ Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025 People’s Republic of China
| | - Ernest Johann Helwig
- Tongji Medical College of Huazhong University of Science and Technology, Wuhan, 430030 People’s Republic of China
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10
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Hardy NP, Mac Aonghusa P, Neary PM, Cahill RA. Intraprocedural Artificial Intelligence for Colorectal Cancer Detection and Characterisation in Endoscopy and Laparoscopy. Surg Innov 2021; 28:768-775. [PMID: 33634722 PMCID: PMC8647474 DOI: 10.1177/1553350621997761] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In this article, we provide an evidence-based primer of current tools and evolving concepts in the area of intraprocedural artificial intelligence (AI) methods in colonoscopy and laparoscopy as a 'procedure companion', with specific focus on colorectal cancer recognition and characterisation. These interventions are both likely beneficiaries from an impending rapid phase in technical and technological evolution. The domains where AI is most likely to impact are explored as well as the methodological pitfalls pertaining to AI methods. Such issues include the need for large volumes of data to train AI systems, questions surrounding false positive rates, explainability and interpretability as well as recent concerns surrounding instabilities in current deep learning (DL) models. The area of biophysics-inspired models, a potential remedy to some of these pitfalls, is explored as it could allow our understanding of the fundamental physiological differences between tissue types to be exploited in real time with the help of computer-assisted interpretation. Right now, such models can include data collected from dynamic fluorescence imaging in surgery to characterise lesions by their biology reducing the number of cases needed to build a reliable and interpretable classification system. Furthermore, instead of focussing on image-by-image analysis, such systems could analyse in a continuous fashion, more akin to how we view procedures in real life and make decisions in a manner more comparable to human decision-making. Synergistical approaches can ensure AI methods usefully embed within practice thus safeguarding against collapse of this exciting field of investigation as another 'boom and bust' cycle of AI endeavour.
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Affiliation(s)
- Niall P Hardy
- UCD Centre for Precision Surgery, School of
Medicine, University College Dublin, Dublin, Ireland
| | | | - Peter M Neary
- Department of Surgery, University College
Cork, University Hospital Waterford, Waterford, Ireland
| | - Ronan A Cahill
- UCD Centre for Precision Surgery, School of
Medicine, University College Dublin, Dublin, Ireland
- Department of Surgery, Mater Misericordiae University
Hospital, Dublin, Ireland
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11
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Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021; 31:743-758. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA
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12
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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13
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Ahmad OF, Mori Y, Misawa M, Kudo SE, Anderson JT, Bernal J, Berzin TM, Bisschops R, Byrne MF, Chen PJ, East JE, Eelbode T, Elson DS, Gurudu SR, Histace A, Karnes WE, Repici A, Singh R, Valdastri P, Wallace MB, Wang P, Stoyanov D, Lovat LB. Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method. Endoscopy 2021; 53:893-901. [PMID: 33167043 PMCID: PMC8390295 DOI: 10.1055/a-1306-7590] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
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Affiliation(s)
- Omer F. Ahmad
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - John T. Anderson
- Department of Gastroenterology, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | - Jorge Bernal
- Computer Science Department, Universitat Autonoma de Barcelona and Computer Vision Center, Barcelona, Spain
| | - Tyler M. Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID KU Leuven, Leuven, Belgium
| | - Michael F. Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Peng-Jen Chen
- Division of Gastroenterology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - James E. East
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, UK,Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tom Eelbode
- Medical Imaging Research Center, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Daniel S. Elson
- Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London, UK,Department of Surgery and Cancer, Imperial College London, London, UK
| | - Suryakanth R. Gurudu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Aymeric Histace
- ETIS, Universite de Cergy-Pointoise, ENSEA, CNRS, Cergy-Pointoise Cedex, France
| | - William E. Karnes
- H. H. Chao Comprehensive Digestive Disease Center, Division of Gastroenterology & Hepatology, Department of Medicine, University of California, Irvine, California, USA
| | - Alessandro Repici
- Department of Gastroenterology, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy,Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Rajvinder Singh
- Department of Gastroenterology and Hepatology, Lyell McEwan Hospital, Adelaide, South Australia, Australia
| | - Pietro Valdastri
- School of Electronics and Electrical Engineering, University of Leeds, Leeds, UK
| | - Michael B. Wallace
- Division of Gastroenterology & Hepatology, Mayo Clinic, Jacksonville, Florida, USA
| | - Pu Wang
- Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
| | - Laurence B. Lovat
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK,Gastrointestinal Services, University College London Hospital, London, UK
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14
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Pecere S, Milluzzo SM, Esposito G, Dilaghi E, Telese A, Eusebi LH. Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases. Diagnostics (Basel) 2021; 11:diagnostics11091575. [PMID: 34573917 PMCID: PMC8469485 DOI: 10.3390/diagnostics11091575] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 12/16/2022] Open
Abstract
The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development.
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Affiliation(s)
- Silvia Pecere
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Center for Endoscopic Research Therapeutics and Training (CERTT), Catholic University, 00168 Rome, Italy
- Correspondence: (S.P.); (L.H.E.)
| | - Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00135 Rome, Italy;
- Fondazione Poliambulanza Istituto Ospedaliero, 25121 Brescia, Italy
| | - Gianluca Esposito
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Emanuele Dilaghi
- Department of Medical-Surgical Sciences and Translational Medicine, Sant’Andrea Hospital, Sapienza University of Rome, 00168 Rome, Italy; (G.E.); (E.D.)
| | - Andrea Telese
- Department of Gastroenterology, University College London Hospital (UCLH), London NW1 2AF, UK;
| | - Leonardo Henry Eusebi
- Division of Gastroenterology and Endoscopy, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40121 Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, 40121 Bologna, Italy
- Correspondence: (S.P.); (L.H.E.)
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15
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Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27:4802-4817. [PMID: 34447227 PMCID: PMC8371500 DOI: 10.3748/wjg.v27.i29.4802] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/12/2021] [Accepted: 07/16/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer remains a leading cause of morbidity and mortality in the United States. Advances in artificial intelligence (AI), specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps. Conversely, these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place, decreasing the risks that come with unnecessary polypectomies. This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.
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Affiliation(s)
- Joel Joseph
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
| | - Ella Marie LePage
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
| | - Catherine Phillips Cheney
- Department of Internal Medicine, Wake Forest School of Medicine, Winston Salem, NC 27157, United States
| | - Rishi Pawa
- Department of Internal Medicine, Section of Gastroenterology and Hepatology, Wake Forest Baptist Medical Center, Winston-Salem, NC 27157, United States
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16
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Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2021; 33:1041-1048. [PMID: 32804846 DOI: 10.1097/meg.0000000000001906] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Colonoscopy is an important method to diagnose polyps, especially adenomatous polyps. However, the rate of missed diagnoses is relatively high. In this study, we aimed to determine whether artificial intelligence (AI) improves the polyp detection rate (PDR) and adenoma detection rate (ADR) with colonoscopy. We performed a systematic search in PubMed, Cochrane Library, Embase, and Web of Science databases; the search included entries in the databases up to and including 29 February 2020. Five articles that involved a total of 4311 patients fulfilled the selection criteria. The results of these studies showed that both PDR and ADR increased with the assistance of AI compared with those in control groups {pooled odds ratio (OR) = 1.91 [95% confidence interval (CI) 1.68-2.16] and 1.75 (95% CI 1.52-2.01), respectively}. Good bowel preparation reduced the impact of AI, but significant differences were still apparent in PDR and ADR [pooled OR = 1.69 (95% CI 1.32-2.16) and 1.36 (95% CI 1.04-1.78), respectively]. The characteristics of polyps and adenomas also influenced the results. The average number of polyps and adenomas detected varied significantly by location, and small polyps and adenomas were more likely to be missed. However, the effect of the morphology of polyps and AI-assisted detection needs further studies. In conclusion, AI increases the detection rates of polyps and adenomas in colonoscopy. Without AI assistance, detection rates can be improved with better bowel preparation and training for small polyp and adenoma detection.
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Nazarian S, Glover B, Ashrafian H, Darzi A, Teare J. Diagnostic Accuracy of Artificial Intelligence and Computer-Aided Diagnosis for the Detection and Characterization of Colorectal Polyps: Systematic Review and Meta-analysis. J Med Internet Res 2021; 23:e27370. [PMID: 34259645 PMCID: PMC8319784 DOI: 10.2196/27370] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/09/2021] [Accepted: 05/06/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Colonoscopy reduces the incidence of colorectal cancer (CRC) by allowing detection and resection of neoplastic polyps. Evidence shows that many small polyps are missed on a single colonoscopy. There has been a successful adoption of artificial intelligence (AI) technologies to tackle the issues around missed polyps and as tools to increase the adenoma detection rate (ADR). OBJECTIVE The aim of this review was to examine the diagnostic accuracy of AI-based technologies in assessing colorectal polyps. METHODS A comprehensive literature search was undertaken using the databases of Embase, MEDLINE, and the Cochrane Library. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. Studies reporting the use of computer-aided diagnosis for polyp detection or characterization during colonoscopy were included. Independent proportions and their differences were calculated and pooled through DerSimonian and Laird random-effects modeling. RESULTS A total of 48 studies were included. The meta-analysis showed a significant increase in pooled polyp detection rate in patients with the use of AI for polyp detection during colonoscopy compared with patients who had standard colonoscopy (odds ratio [OR] 1.75, 95% CI 1.56-1.96; P<.001). When comparing patients undergoing colonoscopy with the use of AI to those without, there was also a significant increase in ADR (OR 1.53, 95% CI 1.32-1.77; P<.001). CONCLUSIONS With the aid of machine learning, there is potential to improve ADR and, consequently, reduce the incidence of CRC. The current generation of AI-based systems demonstrate impressive accuracy for the detection and characterization of colorectal polyps. However, this is an evolving field and before its adoption into a clinical setting, AI systems must prove worthy to patients and clinicians. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42020169786; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020169786.
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Affiliation(s)
- Scarlet Nazarian
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Ben Glover
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Hutan Ashrafian
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Ara Darzi
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
| | - Julian Teare
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom
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18
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Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc 2021; 14:26317745211014698. [PMID: 34263163 PMCID: PMC8252334 DOI: 10.1177/26317745211014698] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/07/2021] [Indexed: 12/27/2022] Open
Abstract
Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Missouri, Columbia, MO 65211, USA
| | - Michael F Byrne
- Division of Gastroenterology, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada; Satisfai Health, Vancouver, BC, Canada
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Hardy NP, Dalli J, Mac Aonghusa P, Neary PM, Cahill RA. Biophysics inspired artificial intelligence for colorectal cancer characterization. Artif Intell Gastroenterol 2021; 2:77-84. [DOI: 10.35712/aig.v2.i3.77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/21/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
Over the last ten years artificial intelligence (AI) methods have begun to pervade even the most common everyday tasks such as email filtering and mobile banking. While the necessary quality and safety standards may have understandably slowed the introduction of AI to healthcare when compared with other industries, we are now beginning to see AI methods becoming more available to the clinician in select settings. In this paper we discuss current AI methods as they pertain to gastrointestinal procedures including both gastroenterology and gastrointestinal surgery. The current state of the art for polyp detection in gastroenterology is explored with a particular focus on deep leaning, its strengths, as well as some of the factors that may limit its application to the field of surgery. The use of biophysics (utilizing physics to study and explain biological phenomena) in combination with more traditional machine learning is also discussed and proposed as an alternative approach that may solve some of the challenges associated with deep learning. Past and present uses of biophysics inspired AI methods, such as the use of fluorescence guided surgery to aid in the characterization of colorectal lesions, are used to illustrate the role biophysics-inspired AI can play in the exciting future of the gastrointestinal proceduralist.
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Affiliation(s)
- Niall P Hardy
- UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
| | - Jeffrey Dalli
- UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
| | | | - Peter M Neary
- Department of Surgery, University Hospital Waterford, University College Cork, Waterford X91 ER8E, Ireland
| | - Ronan A Cahill
- UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
- Department of Surgery, Mater Misericordiae University Hospital (MMUH), Dublin 7, Ireland
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20
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Computer-Aided Detection False Positives in Colonoscopy. Diagnostics (Basel) 2021; 11:diagnostics11061113. [PMID: 34207226 PMCID: PMC8235696 DOI: 10.3390/diagnostics11061113] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/08/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022] Open
Abstract
Randomized control trials and meta-analyses comparing colonoscopies with and without computer-aided detection (CADe) assistance showed significant increases in adenoma detection rates (ADRs) with CADe. A major limitation of CADe is its false positives (FPs), ranked 3rd in importance among 59 research questions in a modified Delphi consensus review. The definition of FPs varies. One commonly used definition defines an FP as an activation of the CADe system, irrespective of the number of frames or duration of time, not due to any polypoid or nonpolypoid lesions. Although only 0.07 to 0.2 FPs were observed per colonoscopy, video analysis studies using FPs as the primary outcome showed much higher numbers of 26 to 27 per colonoscopy. Most FPs were of short duration (91% < 0.5 s). A higher number of FPs was also associated with suboptimal bowel preparation. The appearance of FPs can lead to user fatigue. The polypectomy of FPs results in increased procedure time and added use of resources. Re-training the CADe algorithms is one way to reduce FPs but is not practical in the clinical setting during colonoscopy. Water exchange (WE) is an emerging method that the colonoscopist can use to provide salvage cleaning during insertion. We discuss the potential of WE for reducing FPs as well as the augmentation of ADRs through CADe.
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21
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Kim KO, Kim EY. Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm. Gut Liver 2021; 15:346-353. [PMID: 32773386 PMCID: PMC8129657 DOI: 10.5009/gnl20186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 06/28/2020] [Indexed: 12/19/2022] Open
Abstract
Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)
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Affiliation(s)
- Kyeong Ok Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Eun Young Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea
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22
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Milluzzo SM, Cesaro P, Grazioli LM, Olivari N, Spada C. Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective. Clin Endosc 2021; 54:329-339. [PMID: 33434961 PMCID: PMC8182250 DOI: 10.5946/ce.2020.082] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 12/24/2022] Open
Abstract
The present manuscript aims to review the history, recent advances, evidence, and challenges of artificial intelligence (AI) in colonoscopy. Although it is mainly focused on polyp detection and characterization, it also considers other potential applications (i.e., inflammatory bowel disease) and future perspectives. Some of the most recent algorithms show promising results that are similar to human expert performance. The integration of AI in routine clinical practice will be challenging, with significant issues to overcome (i.e., regulatory, reimbursement). Medico-legal issues will also need to be addressed. With the exception of an AI system that is already available in selected countries (GI Genius; Medtronic, Minneapolis, MN, USA), the majority of the technology is still in its infancy and has not yet been proven to reach a sufficient diagnostic performance to be adopted in the clinical practice. However, larger players will enter the arena of AI in the next few months.
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Affiliation(s)
- Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Department of Gastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS -Università Cattolica del Sacro Cuore, Roma, Italy
| | - Paola Cesaro
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | | | - Nicola Olivari
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Department of Gastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS -Università Cattolica del Sacro Cuore, Roma, Italy
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23
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Misawa M, Kudo SE, Mori Y, Maeda Y, Ogawa Y, Ichimasa K, Kudo T, Wakamura K, Hayashi T, Miyachi H, Baba T, Ishida F, Itoh H, Oda M, Mori K. Current status and future perspective on artificial intelligence for lower endoscopy. Dig Endosc 2021; 33:273-284. [PMID: 32969051 DOI: 10.1111/den.13847] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 09/03/2020] [Accepted: 09/16/2020] [Indexed: 12/23/2022]
Abstract
The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.
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Affiliation(s)
- Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Heath and Society, University of Oslo, Oslo, Norway
| | - Yasuharu Maeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yushi Ogawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kunihiko Wakamura
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Takemasa Hayashi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toshiyuki Baba
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Fumio Ishida
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Aichi, Japan
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Parasher G, Wong M, Rawat M. Evolving role of artificial intelligence in gastrointestinal endoscopy. World J Gastroenterol 2020; 26:7287-7298. [PMID: 33362384 PMCID: PMC7739161 DOI: 10.3748/wjg.v26.i46.7287] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/02/2020] [Accepted: 11/29/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.
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Affiliation(s)
- Gulshan Parasher
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Morgan Wong
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
| | - Manmeet Rawat
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM 87131, United States
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Abstract
Background and Aims Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. Methods The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Results Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images. Conclusions The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
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Key Words
- ADR, adenoma detection rate
- AI, artificial intelligence
- AMR, adenoma miss rate
- ANN, artificial neural network
- BE, Barrett’s esophagus
- CAD, computer-aided diagnosis
- CADe, CAD studies for colon polyp detection
- CADx, CAD studies for colon polyp classification
- CI, confidence interval
- CNN, convolutional neural network
- CRC, colorectal cancer
- DL, deep learning
- GI, gastroenterology
- HD-WLE, high-definition white light endoscopy
- HDWL, high-definition white light
- ML, machine learning
- NBI, narrow-band imaging
- NPV, negative predictive value
- PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
- SVM, support vector machine
- VLE, volumetric laser endomicroscopy
- WCE, wireless capsule endoscopy
- WL, white light
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Johnson KB, Wei W, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci 2020; 14:86-93. [PMID: 32961010 PMCID: PMC7877825 DOI: 10.1111/cts.12884] [Citation(s) in RCA: 244] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 08/11/2020] [Indexed: 12/16/2022] Open
Abstract
The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with less‐common responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.
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Affiliation(s)
- Kevin B. Johnson
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of PediatricsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Wei‐Qi Wei
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | | | - Mark E. Frisse
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Karl Misulis
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Clinical NeurologyVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kyu Rhee
- IBM Watson HealthCambridgeMassachusettsUSA
| | - Juan Zhao
- Department of Biomedical InformaticsVanderbilt University Medical CenterNashvilleTennesseeUSA
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Chahal D, Byrne MF. A primer on artificial intelligence and its application to endoscopy. Gastrointest Endosc 2020; 92:813-820.e4. [PMID: 32387497 DOI: 10.1016/j.gie.2020.04.074] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 04/28/2020] [Indexed: 12/11/2022]
Abstract
Artificial intelligence (AI) has emerged as a powerful and exciting new technology poised to impact many aspects of health care. In endoscopy, AI is now being used to detect and characterize benign and malignant GI lesions and assess malignant lesion depth of invasion. It will undoubtedly also find use in capsule endoscopy and inflammatory bowel disease. Herein, we provide the general endoscopist with a brief overview of AI and its emerging uses in our field. We also touch on the challenges of incorporating AI into clinical practice, such as workflow integration, data storage, and data privacy.
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Affiliation(s)
- Daljeet Chahal
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Michael F Byrne
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; Satisfai Health and AI4GI joint venture, Vancouver, British Columbia, Canada
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28
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Hoogenboom SA, Bagci U, Wallace MB. Artificial intelligence in gastroenterology. The current state of play and the potential. How will it affect our practice and when? ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.tgie.2019.150634] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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29
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Rex DK. Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.tgie.2019.150638] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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30
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Sánchez-Montes C, Bernal J, García-Rodríguez A, Córdova H, Fernández-Esparrach G. Review of computational methods for the detection and classification of polyps in colonoscopy imaging. GASTROENTEROLOGIA Y HEPATOLOGIA 2020; 43:222-232. [PMID: 32143918 DOI: 10.1016/j.gastrohep.2019.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 11/24/2019] [Indexed: 02/06/2023]
Abstract
Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.
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Affiliation(s)
- Cristina Sánchez-Montes
- Unidad de Endoscopia Digestiva, Hospital Universitari i Politècnic La Fe, Grupo de Investigación de Endoscopia Digestiva, IIS La Fe, Valencia, España
| | - Jorge Bernal
- Centro de Visión por Computador, Departamento de Ciencias de la Computación, Universidad Autónoma de Barcelona, Barcelona, España
| | - Ana García-Rodríguez
- Unidad de Endoscopia, Servicio de Gastroenterología, Hospital Clínic, IDIBAPS, CIBEREHD, Universidad de Barcelona, Barcelona, España
| | - Henry Córdova
- Unidad de Endoscopia, Servicio de Gastroenterología, Hospital Clínic, IDIBAPS, CIBEREHD, Universidad de Barcelona, Barcelona, España; IDIBAPS, CIBEREHD, Barcelona, España
| | - Gloria Fernández-Esparrach
- Unidad de Endoscopia, Servicio de Gastroenterología, Hospital Clínic, IDIBAPS, CIBEREHD, Universidad de Barcelona, Barcelona, España; IDIBAPS, CIBEREHD, Barcelona, España.
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31
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He YS, Su JR, Li Z, Zuo XL, Li YQ. Application of artificial intelligence in gastrointestinal endoscopy. J Dig Dis 2019; 20:623-630. [PMID: 31639272 DOI: 10.1111/1751-2980.12827] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Revised: 10/07/2019] [Accepted: 10/20/2019] [Indexed: 02/06/2023]
Abstract
With recent significant improvements in artificial intelligence (AI), especially in the field of deep learning, an increasing number of studies have evaluated the use of AI in endoscopy to detect and diagnose gastrointestinal (GI) lesions. The present review summarizes current publications on the use of AI in GI endoscopy and focuses on the challenges and future of AI-aided systems. We expect AI to provide an effective and practical method for endoscopists in lesion detection and characterization as well as in quality control in endoscopy. However, so far, most studies have remained at the preclinical stage. More attention should be paid in the future to the use of AI in real-life clinical applications.
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Affiliation(s)
- Yi Shan He
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, Shandong Province, China
| | - Jing Ran Su
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, Shandong Province, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, Shandong Province, China
| | - Xiu Li Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, Shandong Province, China
| | - Yan Qing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.,Robot Engineering Laboratory for Precise Diagnosis and Therapy of GI Tumor, Qilu Hospital of Shandong University, Jinan, Shandong Province, China
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32
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Hsieh YH, Leung FW. An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection. Expert Rev Gastroenterol Hepatol 2019; 13:1153-1160. [PMID: 31755802 DOI: 10.1080/17474124.2019.1694903] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
Introduction: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy in 2018, water exchange colonoscopy and artificial intelligence were both considered important advances. Artificial intelligence holds the potential to increase and water exchange significantly increases adenoma detection.Areas covered: The authors searched MEDLINE (1998-2019) using the following medical subject terms: water-aided, water-assisted and water exchange colonoscopy, adenoma, artificial intelligence, deep learning, computer-assisted detection, and neural networks. Additional related studies were manually searched from the reference lists of publications. Only fully published journal articles in English were reviewed. The latest date of the search was Aug10, 2019. Artificial intelligence, machine learning, and deep learning contribute to the promise of real-time computer-aided detection diagnosis. By emphasizing near-complete suction of infused water during insertion, water exchange provides salvage cleaning and decreases cleaning-related multi-tasking distractions during withdrawal, increasing adenoma detection. The review will address how artificial intelligence and water exchange can complement each other in improving adenoma detection during colonoscopy.Expert opinion: In 5 years, research on artificial intelligence will likely achieve real-time application and evaluation of factors contributing to quality colonoscopy. Better understanding and more widespread use of water exchange will be possible.
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Affiliation(s)
- Yu-Hsi Hsieh
- Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan.,School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Felix W Leung
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, CA, USA.,David Geffen School of Medicine, at University of California at Los Angeles, Los Angeles, CA, USA
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33
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Chao WL, Manickavasagan H, Krishna SG. Application of Artificial Intelligence in the Detection and Differentiation of Colon Polyps: A Technical Review for Physicians. Diagnostics (Basel) 2019; 9:diagnostics9030099. [PMID: 31434208 PMCID: PMC6787748 DOI: 10.3390/diagnostics9030099] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 08/13/2019] [Accepted: 08/19/2019] [Indexed: 12/14/2022] Open
Abstract
Research in computer-aided diagnosis (CAD) and the application of artificial intelligence (AI) in the endoscopic evaluation of the gastrointestinal tract is novel. Since colonoscopy and detection of polyps can decrease the risk of colon cancer, it is recommended by multiple national and international societies. However, the procedure of colonoscopy is performed by humans where there are significant interoperator and interpatient variations, and hence, the risk of missing detection of adenomatous polyps. Early studies involving CAD and AI for the detection and differentiation of polyps show great promise. In this appraisal, we review existing scientific aspects of AI in CAD of colon polyps and discuss the pitfalls and future directions for advancing the science. This review addresses the technical intricacies in a manner that physicians can comprehend to promote a better understanding of this novel application.
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Affiliation(s)
- Wei-Lun Chao
- Department of Computer Science, Cornell University, New York, NY 14853, USA
- Department of Computer Science and Engineering, Ohio State University, Columbus, OH 43210, USA
| | - Hanisha Manickavasagan
- Division of Gastroenterology, Hepatology and Nutrition, the Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
| | - Somashekar G Krishna
- Division of Gastroenterology, Hepatology and Nutrition, the Ohio State University Wexner Medical Center, Columbus, OH 43210, USA.
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34
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Vinsard DG, Mori Y, Misawa M, Kudo SE, Rastogi A, Bagci U, Rex DK, Wallace MB. Quality assurance of computer-aided detection and diagnosis in colonoscopy. Gastrointest Endosc 2019; 90:55-63. [PMID: 30926431 DOI: 10.1016/j.gie.2019.03.019] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 03/18/2019] [Indexed: 02/05/2023]
Abstract
Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-aided detection and diagnosis (CADe and/or CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice-polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease the polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect-and-discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas, with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion by using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine-learning-based CADe and/or CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.
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Affiliation(s)
- Daniela Guerrero Vinsard
- Showa University International Center for Endoscopy, Showa University Northern Yokohama Hospital, Yokohama, Japan; Division of Internal Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Amit Rastogi
- Division of Gastroenterology, University of Kansas Medical Center, Kansas City, Kansas
| | - Ulas Bagci
- Center for Research in Computer Vision, University of Central Florida, Orlando, Florida
| | - Douglas K Rex
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida, USA
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35
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Kudo SE, Mori Y, Misawa M, Takeda K, Kudo T, Itoh H, Oda M, Mori K. Artificial intelligence and colonoscopy: Current status and future perspectives. Dig Endosc 2019; 31:363-371. [PMID: 30624835 DOI: 10.1111/den.13340] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 12/04/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIM Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer-aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. METHODS AND RESULTS Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. CONCLUSION We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence-assisted medical devices.
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Affiliation(s)
- Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Kenichi Takeda
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Toyoki Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan
| | - Hayato Itoh
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Masahiro Oda
- Graduate School of Informatics, Nagoya University, Aichi, Japan
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Aichi, Japan
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36
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Liu WN, Zhang YY, Bian XQ, Wang LJ, Yang Q, Zhang XD, Huang J. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol 2019; 26:13-19. [PMID: 31898644 PMCID: PMC7045775 DOI: 10.4103/sjg.sjg_377_19] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND/AIM To study the impact of computer-aided detection (CADe) system on the detection rate of polyps and adenomas in colonoscopy. MATERIALS AND METHODS A total of 1026 patients were prospectively randomly scheduled for colonoscopy with (the CADe group, CADe) or without (the control group, CON) the aid of the CADe system, together with visual notification and voice alarm, so as to compare the detection rate of polyp. RESULTS Compared with group CON, the detection rate of adenomas increased in group CADe, the average number of adenomas increased, the number of small adenomas increased, the number of proliferative polyps increased, and the differences were statistically significant (P < 0.001), but the comparison for the number of larger adenomas showed no significant difference between the groups (P> 0.05). CONCLUSIONS The CADe system is feasible for increasing the detection of polyps and adenomas in colonoscopy.
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Affiliation(s)
- Wen-Na Liu
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Yang-Yang Zhang
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Xu-Qiang Bian
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Li-Juan Wang
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Qiang Yang
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Xi-Dou Zhang
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China
| | - Jin Huang
- Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou, China,Address for correspondence: Dr. Jin Huang, Department of Digestive Endoscopy, No. 988 Hospital of Joint Logistic Support Force of PLA, Zhengzhou - 450000, China. E-mail:
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