51
|
Xu M, Zhou W, Wu L, Zhang J, Wang J, Mu G, Huang X, Li Y, Yuan J, Zeng Z, Wang Y, Huang L, Liu J, Yu H. Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video). Gastrointest Endosc 2021; 94:540-548.e4. [PMID: 33722576 DOI: 10.1016/j.gie.2021.03.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/06/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Gastric precancerous conditions, including gastric atrophy (GA) and intestinal metaplasia (IM), play an important role in the development of gastric cancer. Image-enhanced endoscopy (IEE) shows great potential in diagnosing gastric precancerous conditions and adenocarcinoma. In this study, a deep convolutional neural network system, named ENDOANGEL, was constructed to detect gastric precancerous conditions by IEE. METHODS Endoscopic images were retrospectively obtained from 5 hospitals in China for the development, validation, and internal and external test of the system. Prospective consecutive patients receiving IEE were enrolled from January 13, 2020 to October 29, 2020 in Renmin Hospital of Wuhan University to assess in real time the applicability of the proposed computer-aided detection (CADe) system in clinical practice, and the performance of CADe was compared with that of endoscopists. RESULTS Six thousand two hundred fifty endoscopic images from 760 patients and 98 video clips from 77 individuals undergoing IEE were enrolled in this study. The diagnostic accuracy of GA was .901 (95% confidence interval [CI], .883-.917) in the internal test set, .864 (95% CI, .842-.884) in the multicenter external test set, and .878 (95% CI, .796-.935) in the prospective video test set. The diagnostic accuracy of IM was .908 (95% CI, .889-.924) in the internal test set, .859 (95% CI, .837-.880) in the multicenter external test set, and .898 (95% CI, .820-.950) in the prospective video test set. CADe achieved similar diagnostic accuracy to that of the experts for detecting GA (.869 [95% CI, .790-.927] vs .846 [95% CI, .808-.879], P = .396) and IM (.888 [95% CI, .812-.941] vs .820 [95% CI, .780-.855], P = .117) and was superior to that of nonexperts for GA (.750 [95% CI, .711-.786], P = .008) and IM (.736 [95% CI, .697-.773], P = .028). CONCLUSIONS CADe achieved high diagnostic accuracy in gastric precancerous conditions, which was similar to that of experts and superior to that of nonexperts. Thus, CADe provides possibilities for a wide application in assisting in the diagnosis of gastric precancerous conditions.
Collapse
Affiliation(s)
- Ming Xu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Wei Zhou
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jing Wang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ganggang Mu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xu Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jingping Yuan
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhi Zeng
- Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yonggui Wang
- School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China; Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China; Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
| |
Collapse
|
52
|
Abstract
PURPOSE OF REVIEW Artificial intelligence is becoming rapidly integrated into modern technology including medicine. Artificial intelligence has a wide range of potential in gastroenterology, particularly with endoscopy, given the required analysis of large datasets of images. The aim of this review is to summarize the advances of artificial intelligence in gastroenterology (GI) endoscopy over the past year. RECENT FINDINGS Computer-aided detection (CADe) systems during real-time colonoscopy have resulted in increased adenoma detection rate with no significant increase in procedure times. Deep learning techniques have been utilized to accurately assess bowel preparation quality, which would impact surveillance colonoscopy recommendations. For the upper GI tract, CADe systems have been developed to aid in improving the diagnosis of Barrett's neoplasia during real-time endoscopy. Artificial intelligence-assisted real-time endoscopy has been shown to reduce blind spots during EGD. SUMMARY The application of artificial intelligence in gastroenterology endoscopy remains promising. Advances over the past year include improved detection of GI neoplasia during endoscopy and characterization of lesions. Further research including randomized, multicenter trials are needed to further evaluate the use of artificial intelligence for real-time endoscopy.
Collapse
|
53
|
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.
Collapse
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.)
| |
Collapse
|
54
|
Yoo BS, D'Souza SM, Houston K, Patel A, Lau J, Elmahdi A, Parekh PJ, Johnson D. Artificial intelligence and colonoscopy − enhancements and improvements. Artif Intell Gastrointest Endosc 2021; 2:157-167. [DOI: 10.37126/aige.v2.i4.157] [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: 06/05/2021] [Revised: 06/21/2021] [Accepted: 07/23/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence is a technology that processes and analyzes information with reproducibility and accuracy. Its application in medicine, especially in the field of gastroenterology, has great potential to facilitate in diagnosis of various disease states. Currently, the role of artificial intelligence as it pertains to colonoscopy revolves around enhanced polyp detection and characterization. The aim of this article is to review the current and potential future applications of artificial intelligence for enhanced quality of detection for colorectal neoplasia.
Collapse
Affiliation(s)
- Byung Soo Yoo
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Steve M D'Souza
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Kevin Houston
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Ankit Patel
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - James Lau
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Alsiddig Elmahdi
- Department of Medicine, Eastern Virginia Medical School, Norfolk, VA 23507, United States
| | - Parth J Parekh
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23505, United States
| | - David Johnson
- Division of Gastroenterology, Department of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA 23505, United States
| |
Collapse
|
55
|
Song YQ, Mao XL, Zhou XB, He SQ, Chen YH, Zhang LH, Xu SW, Yan LL, Tang SP, Ye LP, Li SW. Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy. Front Med (Lausanne) 2021; 8:709347. [PMID: 34368199 PMCID: PMC8339701 DOI: 10.3389/fmed.2021.709347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/29/2021] [Indexed: 12/04/2022] Open
Abstract
With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.
Collapse
Affiliation(s)
- Ya-Qi Song
- Taizhou Hospital, Zhejiang University, Linhai, China
| | - Xin-Li Mao
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Xian-Bin Zhou
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Sai-Qin He
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ya-Hong Chen
- Health Management Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Li-Hui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shi-Wen Xu
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ling-Ling Yan
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Shen-Ping Tang
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Li-Ping Ye
- Taizhou Hospital, Zhejiang University, Linhai, China.,Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.,Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Shao-Wei Li
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.,Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| |
Collapse
|
56
|
Mankoo R, Ali AH, Hammoud GM. Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies. Artif Intell Gastrointest Endosc 2021; 2:89-94. [DOI: 10.37126/aige.v2.i3.89] [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: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
The application of artificial intelligence (AI) using deep learning and machine learning approaches in modern medicine is rapidly expanding. Within the field of Gastroenterology, AI is being evaluated across a breadth of clinical and diagnostic applications including identification of pathology, differentiation of disease processes, and even automated procedure report generation. Many pancreatic pathologies can have overlapping features creating a diagnostic dilemma that provides a window for AI-assisted improvement in current evaluation and diagnosis, particularly using endoscopic ultrasound. This topic highlight will review the basics of AI, history of AI in gastrointestinal endoscopy, and prospects for AI in the evaluation of autoimmune pancreatitis, pancreatic ductal adenocarcinoma, chronic pancreatitis and intraductal papillary mucinous neoplasm.
Collapse
Affiliation(s)
- Ravinder Mankoo
- Division of Gastroenterology and Hepatology, University of Missouri School of Medicine, Columbia, MO 65212, United States
| | - Ahmad H Ali
- Division of Gastroenterology and Hepatology, University of Missouri School of Medicine, Columbia, MO 65212, United States
| | - Ghassan M Hammoud
- Division of Gastroenterology and Hepatology, University of Missouri School of Medicine, Columbia, MO 65212, United States
| |
Collapse
|
57
|
Mankoo R, Ali AH, Hammoud GM. Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies. Artif Intell Gastrointest Endosc 2021. [DOI: 10.37126/aige.v2.i3.88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
|
58
|
Nam JH, Oh DJ, Lee S, Song HJ, Lim YJ. Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality. Diagnostics (Basel) 2021; 11:diagnostics11061127. [PMID: 34203093 PMCID: PMC8234509 DOI: 10.3390/diagnostics11061127] [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: 04/14/2021] [Revised: 06/03/2021] [Accepted: 06/19/2021] [Indexed: 01/31/2023] Open
Abstract
Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.
Collapse
Affiliation(s)
- Ji Hyung Nam
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea; (J.H.N.); (D.J.O.); (S.L.)
| | - Dong Jun Oh
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea; (J.H.N.); (D.J.O.); (S.L.)
| | - Sumin Lee
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea; (J.H.N.); (D.J.O.); (S.L.)
| | - Hyun Joo Song
- Division of Gastroenterology, Department of Internal Medicine, Jeju National University School of Medicine, Jeju 63241, Korea;
| | - Yun Jeong Lim
- Division of Gastroenterology, Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea; (J.H.N.); (D.J.O.); (S.L.)
- Correspondence: ; Tel.: +82-31-961-7133
| |
Collapse
|
59
|
Yao L, Liu J, Wu L, Zhang L, Hu X, Liu J, Lu Z, Gong D, An P, Zhang J, Hu G, Chen D, Luo R, Hu S, Yang Y, Yu H. A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial. Clin Transl Gastroenterol 2021; 12:e00366. [PMID: 34128480 PMCID: PMC8208417 DOI: 10.14309/ctg.0000000000000366] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 04/28/2021] [Indexed: 01/12/2023] Open
Abstract
INTRODUCTION Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy quality, we constructed an endoscopic audit and feedback system named Endo.Adm and evaluated its effect in a form of pretest and posttest trial. METHODS Endo.Adm system was developed using Python and Deep Convolutional Neural Ne2rk models. Sixteen endoscopists were recruited from Renmin Hospital of Wuhan University and were randomly assigned to undergo feedback of Endo.Adm or not (8 for the feedback group and 8 for the control group). The feedback group received weekly quality report cards which were automatically generated by Endo.Adm. We then compared the adenoma detection rate (ADR) and gastric precancerous conditions detection rate between baseline and postintervention phase for endoscopists in each group to evaluate the impact of Endo.Adm feedback. In total, 1,191 colonoscopies and 3,515 gastroscopies were included for analysis. RESULTS ADR was increased after Endo.Adm feedback (10.8%-20.3%, P < 0.01, DISCUSSION Endo.Adm feedback contributed to multifaceted gastrointestinal endoscopic quality improvement. This system is practical to implement and may serve as a standard model for quality improvement in routine work (http://www.chictr.org.cn/, ChiCTR1900024153).
Collapse
Affiliation(s)
- Liwen Yao
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Jun Liu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Lihui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Xiao Hu
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China;
| | - Jinzhu Liu
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China;
| | - Zihua Lu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Dexin Gong
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Ping An
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Jun Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Guiying Hu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Di Chen
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Renquan Luo
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| | - Shan Hu
- Wuhan ENDOANGEL Medical Technology Company, Wuhan, China;
| | - Yanning Yang
- Department of Ophthalmology, Renmin Hospital of Wuhan University, Wuhan, China.
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China;
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China;
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China;
| |
Collapse
|
60
|
Zhang M, Zhu C, Wang Y, Kong Z, Hua Y, Zhang W, Si X, Ye B, Xu X, Li L, Heng D, Liu B, Tian S, Wu J, Dang Y, Zhang G. Differential diagnosis for esophageal protruded lesions using a deep convolution neural network in endoscopic images. Gastrointest Endosc 2021; 93:1261-1272.e2. [PMID: 33065026 DOI: 10.1016/j.gie.2020.10.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 10/01/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Recent advances in deep convolutional neural networks (CNNs) have led to remarkable results in digestive endoscopy. In this study, we aimed to develop CNN-based models for the differential diagnosis of benign esophageal protruded lesions using endoscopic images acquired during real clinical settings. METHODS We retrospectively reviewed the images from 1217 patients who underwent white-light endoscopy (WLE) and EUS between January 2015 and April 2020. Three deep CNN models were developed to accomplish the following tasks: (1) identification of esophageal benign lesions from healthy controls using WLE images; (2) differentiation of 3 subtypes of esophageal protruded lesions (including esophageal leiomyoma [EL], esophageal cyst (EC], and esophageal papilloma [EP]) using WLE images; and (3) discrimination between EL and EC using EUS images. Six endoscopists blinded to the patients' clinical status were enrolled to interpret all images independently. Their diagnostic performances were evaluated and compared with the CNN models using the area under the receiver operating characteristic curve (AUC). RESULTS For task 1, the CNN model achieved an AUC of 0.751 (95% confidence interval [CI], 0.652-0.850) in identifying benign esophageal lesions. For task 2, the proposed model using WLE images for differentiation of esophageal protruded lesions achieved an AUC of 0.907 (95% CI, 0.835-0.979), 0.897 (95% CI, 0.841-0.953), and 0.868 (95% CI, 0.769-0.968) for EP, EL, and EC, respectively. The CNN model achieved equivalent or higher identification accuracy for EL and EC compared with skilled endoscopists. In the task of discriminating EL from EC (task 3), the proposed CNN model had AUC values of 0.739 (EL, 95% CI, 0.600-0.878) and 0.724 (EC, 95% CI, 0.567-0.881), which outperformed seniors and novices. Attempts to combine the CNN and endoscopist predictions led to significantly improved diagnostic accuracy compared with endoscopists interpretations alone. CONCLUSIONS Our team established CNN-based methodologies to recognize benign esophageal protruded lesions using routinely obtained WLE and EUS images. Preliminary results combining the results from the models and the endoscopists underscored the potential of ensemble models for improved differentiation of lesions in real endoscopic settings.
Collapse
Affiliation(s)
- Min Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chang Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yun Wang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zihao Kong
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yifei Hua
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weifeng Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinmin Si
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Bixing Ye
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaobing Xu
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Lurong Li
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ding Heng
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | | | | | | | - Yini Dang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Guoxin Zhang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
61
|
Li JW, Ang TL. Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021; 2:36-49. [DOI: 10.37126/aige.v2.i2.36] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/27/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.
Collapse
Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| |
Collapse
|
62
|
Lee J, Wallace MB. State of the Art: The Impact of Artificial Intelligence in Endoscopy 2020. Curr Gastroenterol Rep 2021; 23:7. [PMID: 33855659 DOI: 10.1007/s11894-021-00810-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/16/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE OF REVIEW Recently numerous researchers have shown remarkable progress using convolutional neural network-based artificial intelligence (AI) for endoscopy. In this manuscript we aim to summarize recent AI impact on endoscopy. RECENT FINDINGS AI for detecting colon polyps has been the most promising area for application of AI in endoscopy. Recent prospective randomized studies showed that AI assisted colonoscopy increased adenoma detection rate and the mean number of adenomas per patient compared to standard colonoscopy alone. AI for optical biopsy of colon polyp showed a negative predictive value of ≥90%. For capsule endoscopy, applying AI to pre-read the video images decreased physician reading time significantly. Recently, researchers are broadening the area of AI to quality assessment of endoscopy such as bowel preparation and automated report generation. AI systems have shown great potential to increase physician performance by enhancing detection, reducing procedure time, and providing real-time feedback of endoscopy quality. To build a generally applicable AI, we need further investigations in real world settings and also integration of AI tools into pragmatic platforms.
Collapse
Affiliation(s)
- Jiyoung Lee
- Division of Gastroenterology and Hepatology, Endoscopy Unit, Mayo Clinic Jacksonville, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.,Health Screening and Promotion Center, Asan Medical Center, Seoul, South Korea
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Endoscopy Unit, Mayo Clinic Jacksonville, 4500 San Pablo Road, Jacksonville, FL, 32224, USA. .,Center of Research in Computer Vision, University of Central Florida, Orlando, FL, USA.
| |
Collapse
|
63
|
Sutton RA, Sharma P. Overcoming barriers to implementation of artificial intelligence in gastroenterology. Best Pract Res Clin Gastroenterol 2021; 52-53:101732. [PMID: 34172254 DOI: 10.1016/j.bpg.2021.101732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 02/08/2021] [Indexed: 01/31/2023]
Abstract
Artificial intelligence is poised to revolutionize the field of medicine, however significant questions must be answered prior to its implementation on a regular basis. Many artificial intelligence algorithms remain limited by isolated datasets which may cause selection bias and truncated learning for the program. While a central database may solve this issue, several barriers such as security, patient consent, and management structure prevent this from being implemented. An additional barrier to daily use is device approval by the Food and Drug Administration. In order for this to occur, clinical studies must address new endpoints, including and beyond the traditional bio- and medical statistics. These must showcase artificial intelligence's benefit and answer key questions, including challenges posed in the field of medical ethics.
Collapse
Affiliation(s)
- Richard A Sutton
- University of Kansas Medical Center 3901 Rainbow Blvd, Kansas City, KS, USA; Kansas City Veteran's Affairs Medical Center 4801 Linwood Blvd, Kansas City, MO, USA.
| | - Prateek Sharma
- University of Kansas Medical Center 3901 Rainbow Blvd, Kansas City, KS, USA; Kansas City Veteran's Affairs Medical Center 4801 Linwood Blvd, Kansas City, MO, USA.
| |
Collapse
|
64
|
Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using a deep convolutional neural network: a multicenter retrospective study (with video). Gastrointest Endosc 2021; 93:422-432.e3. [PMID: 32598959 DOI: 10.1016/j.gie.2020.06.058] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 06/11/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Rupture of gastroesophageal varices is the most common fatal adverse event of cirrhosis. EGD is considered the criterion standard for diagnosis and risk stratification of gastroesophageal variceal bleeding. The aim of this study was to train and validate a real-time deep convolutional neural network (DCNN) system, named ENDOANGEL, for diagnosing gastroesophageal varices and predicting the risk of rupture. METHODS After training with 8566 images of endoscopic gastroesophageal varices from 3021 patients and 6152 images of normal esophagus/stomach from 3168 patients, ENDOANGEL was also tested with independent images and videos. It was also compared with endoscopists in several aspects. RESULTS ENDOANGEL, in contrast with endoscopists, displayed higher accuracy of 97.00% and 92.00% in terms of detecting esophageal varices (EVs) and gastric varices (GVs) in an image contest (97.00% vs 93.94% , P < .01; 92.00% vs 84.43%, P < .05). It also surpassed endoscopists for red color signs of EVs and red spots of GVs (84.21% vs 73.45%, P < .01; 85.26% vs 77.52%, P < .05). Moreover, ENDOANGEL achieved comparable performance in the determination of size, form, color, and bleeding signs. ENDOANGEL also had good performance in making treatment suggestions. With regard to predicting risk factors in multicenter videos, ENDOANGEL showed great stability. CONCLUSIONS Our data suggest that DCNNs were precise in detecting both EVs and GVs and performed excellently in uncovering the endoscopic risk factors of gastroesophageal variceal bleeding. Thus, the application of DCNNs will assist endoscopists in evaluating gastroesophageal varices more objectively and precisely. (Clinical trial registration number: ChiCTR1900023970.).
Collapse
|
65
|
Saraiva S, Rosa I, Dias Pereira A. Use of the Boston Bowel Preparation Scale in the real life setting: what affects it? REVISTA ESPANOLA DE ENFERMEDADES DIGESTIVAS 2021; 113:625. [PMID: 33393339 DOI: 10.17235/reed.2020.7678/2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Adequacy of bowel cleansing is a quality measure for colonoscopy, affecting both its safety and diagnostic accuracy. Among several bowel preparation quality scales referred to in literature, the Boston Bowel Preparation Scale (BBPS) is regarded as one of the most valid and reliable. However, BBPS is conditioned by a partially subjective appraisal. We report the results of a retrospective study that evaluated the quality of bowel preparation using BBPS and the factors associated with cleansing in routine clinical practice, in a series of consecutive examinations performed in a tertiary care hospital.
Collapse
Affiliation(s)
- Sofia Saraiva
- Instituto Português de Oncologia de Lisboa Francisco Gentil E.P.E, Portugal
| | - Isadora Rosa
- Instituto Português de Oncologia de Lisboa Francisco Gentil E.P.E
| | | |
Collapse
|
66
|
The potential of deep learning for gastrointestinal endoscopy—a disruptive new technology. Artif Intell Med 2021. [DOI: 10.1016/b978-0-12-821259-2.00012-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
67
|
Struyvenberg MR, de Groof AJ, Bergman JJ, van der Sommen F, de With PHN, Konda VJA, Curvers WL. Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue? Gastrointest Endosc Clin N Am 2021; 31:91-103. [PMID: 33213802 DOI: 10.1016/j.giec.2020.08.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations.
Collapse
Affiliation(s)
- Maarten R Struyvenberg
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Albert J de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Jacques J Bergman
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, the Netherlands
| | - Vani J A Konda
- Department of Gastroenterology and Hepatology, Baylor University Medical Center, 3500 Gaston Ave, Dallas, TX 75246, USA
| | - Wouter L Curvers
- Department of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands.
| |
Collapse
|
68
|
Abstract
Colonoscopy is an important diagnostic and therapeutic tool in evaluating and treating gastrointestinal tract pathologies. Adequate visualization of the intestinal lumen is necessary for detection of lesions, and thus bowel preparation is a key component of the process. It is estimated that over 25% percent of pediatric patients have sub-optimal bowel preparations, which can lead to longer procedure times, missed pathology, unsuccessful ileal intubation, and possibly repeat procedure/anesthesia. There is no universal protocol for bowel preparation in pediatrics and there is a wide variability of practices around the world. The purpose of this paper is to review the recent published literature regarding bowel preparations for pediatric colonoscopy with focus on published work in the last decade exploring a number of factors involved in bowel preparation including the role of patient education, types of bowel preparation, and their efficacy and safety.
Collapse
Affiliation(s)
- Petar Mamula
- Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Noor Nema
- Children's Hospital of Philadelphia, Philadelphia, PA, United States
| |
Collapse
|
69
|
Sinonquel P, Bisschops R. Striving for quality improvement: can artificial intelligence help? Best Pract Res Clin Gastroenterol 2020; 52-53:101722. [PMID: 34172249 DOI: 10.1016/j.bpg.2020.101722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 12/22/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) is of keen interest for global health development as potential support for current human shortcomings. Gastrointestinal (GI) endoscopy is an excellent substrate for AI, since it holds the genuine potential to improve quality in GI endoscopy and overall patient care by improving detection and diagnosis guiding the endoscopists in performing endoscopy to the highest quality standards. The possibility of large data acquisitioning to refine algorithms makes implementation of AI into daily practice a potential reality. With the start of a new era adopting deep learning, large amounts of data can easily be processed, resulting in better diagnostic performances. In the upper gastrointestinal tract, research currently focusses on the detection and characterization of neoplasia, including Barrett's, squamous cell and gastric carcinoma, with an increasing amount of AI studies demonstrating the potential and benefit of AI-augmented endoscopy. Deep learning applied to small bowel video capsule endoscopy also appears to enhance pathology detection and reduce capsule reading time. In the colon, multiple prospective trials including five randomized trials, showed a consistent improvement in polyp and adenoma detection rates, one of the main quality indicators in endoscopy. There are however potential additional roles for AI to assist in quality improvement of endoscopic procedures, training and therapeutic decision making. Further large-scale, multicenter validation trials are required before AI-augmented diagnostic gastrointestinal endoscopy can be integrated into our routine clinical practice.
Collapse
Affiliation(s)
- P Sinonquel
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
| | - R Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium; Department of Translational Research in Gastrointestinal Diseases (TARGID), Catholic University Leuven, Herestraat 49, 3000, Leuven, Belgium.
| |
Collapse
|
70
|
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.
Collapse
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
Collapse
|
71
|
Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, Chen Q, Huang S, Yang M, Yang X, Hu S, Wang Y, Hu X, Zheng B, Zhang K, Wu H, Dong Z, Xu Y, Zhu Y, Chen X, Zhang M, Yu L, Cheng F, Yu H. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep 2020; 10:19196. [PMID: 33154542 DOI: 10.1101/2020.02.25.20021568] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 08/03/2020] [Indexed: 05/19/2023] Open
Abstract
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
Collapse
Affiliation(s)
- Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liang Zhang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yilin Zhao
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | | | | | - Ming Yang
- Qianjiang Central Hospital, Qianjiang, China
| | - Xiao Yang
- Qianjiang Central Hospital, Qianjiang, China
| | - Shan Hu
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Yonggui Wang
- Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Xiao Hu
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Biqing Zheng
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Kuo Zhang
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Huiling Wu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zehua Dong
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Xu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xi Chen
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjiao Zhang
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
| | - Lilei Yu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
| | - Fan Cheng
- Department of Urinary Surgery, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
| | - Honggang Yu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
| |
Collapse
|
72
|
Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, Chen Q, Huang S, Yang M, Yang X, Hu S, Wang Y, Hu X, Zheng B, Zhang K, Wu H, Dong Z, Xu Y, Zhu Y, Chen X, Zhang M, Yu L, Cheng F, Yu H. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep 2020; 10:19196. [PMID: 33154542 PMCID: PMC7645624 DOI: 10.1038/s41598-020-76282-0] [Citation(s) in RCA: 186] [Impact Index Per Article: 46.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 08/03/2020] [Indexed: 12/16/2022] Open
Abstract
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
Collapse
Affiliation(s)
- Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Lianlian Wu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jun Zhang
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liang Zhang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dexin Gong
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yilin Zhao
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan, China
| | | | | | - Ming Yang
- Qianjiang Central Hospital, Qianjiang, China
| | - Xiao Yang
- Qianjiang Central Hospital, Qianjiang, China
| | - Shan Hu
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Yonggui Wang
- Hubei Key Laboratory of Critical Zone Evolution, School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
| | - Xiao Hu
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Biqing Zheng
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Kuo Zhang
- Wuhan EndoAngel Medical Technology Company, Wuhan, China
| | - Huiling Wu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zehua Dong
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Youming Xu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xi Chen
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjiao Zhang
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China
| | - Lilei Yu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
| | - Fan Cheng
- Department of Urinary Surgery, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
| | - Honggang Yu
- Department of Internal Medicine, Renmin Hospital of Wuhan University, 99 Zhangzhidong Road, Wuhan, 430060, Hubei Province, China.
- Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China.
- Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
| |
Collapse
|
73
|
Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force. Gastrointest Endosc 2020; 92:951-959. [PMID: 32565188 DOI: 10.1016/j.gie.2020.06.035] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 06/14/2020] [Indexed: 12/11/2022]
Abstract
Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes.
Collapse
|
74
|
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest Endosc 2020; 92:807-812. [PMID: 32565184 DOI: 10.1016/j.gie.2020.06.040] [Citation(s) in RCA: 194] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 06/11/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now that AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which are reviewed in further detail by several other articles in this issue of Gastrointestinal Endoscopy.
Collapse
Affiliation(s)
- Vivek Kaul
- Division of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, New York, USA
| | - Sarah Enslin
- Division of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, New York, USA
| | - Seth A Gross
- Division of Gastroenterology & Hepatology, NYU Langone Health System, New York, New York, USA
| |
Collapse
|
75
|
Hwang JH, Jamidar P, Kyanam Kabir Baig KR, Leung FW, Lightdale JR, Maranki JL, Okolo PI, Swanstrom LL, Chak A. GIE Editorial Board top 10 topics: advances in GI endoscopy in 2019. Gastrointest Endosc 2020; 92:241-251. [PMID: 32470427 DOI: 10.1016/j.gie.2020.05.021] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 05/14/2020] [Indexed: 02/06/2023]
Abstract
The American Society for Gastrointestinal Endoscopy's GIE Editorial Board reviewed original endoscopy-related articles published during 2019 in Gastrointestinal Endoscopy and 10 other leading medical and gastroenterology journals. Votes from each individual member were tallied to identify a consensus list of 10 topic areas of major advances in GI endoscopy. Individual board members summarized important findings published in these 10 areas of disinfection, artificial intelligence, bariatric endoscopy, adenoma detection, polypectomy, novel imaging, Barrett's esophagus, third space endoscopy, interventional EUS, and training. This document summarizes these "top 10" endoscopic advances of 2019.
Collapse
Affiliation(s)
- Joo Ha Hwang
- Department of Medicine, Division of Gastroenterology and Hepatology, Stanford University, Palo Alto, California
| | - Priya Jamidar
- Professor of Medicine, Yale University, New Haven, Connecticut
| | | | - Felix W Leung
- Veterans Affairs Greater Los Angeles Healthcare System and David Geffen School of Medicine at UCLA
| | - Jennifer R Lightdale
- University of Massachusetts Medical School, Umass Memorial Childrens Medical Center, Worcester, Massachusetts
| | | | - Patrick I Okolo
- Executive Medical Director, Rochester Regional Health Systems, Rochester, NY
| | - Lee L Swanstrom
- Professor of Surgery, Oregon Health and Sciences University: Scientific Director and Chief Innovations Officer, Institutes Hospitalos Universitaires (IHU-Strasbourg) University of Strasbourg
| | - Amitabh Chak
- University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| |
Collapse
|
76
|
Lui TKL, Guo CG, Leung WK. Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92:11-22.e6. [PMID: 32119938 DOI: 10.1016/j.gie.2020.02.033] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/17/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS We performed a meta-analysis of all published studies to determine the diagnostic accuracy of artificial intelligence (AI) on histology prediction and detection of colorectal polyps. METHOD We searched Embase, PubMed, Medline, Web of Science, and Cochrane library databases to identify studies using AI for colorectal polyp histology prediction and detection. The quality of included studies was measured by the Quality Assessment of Diagnostic Accuracy Studies tool. We used a bivariate meta-analysis following a random-effects model to summarize the data and plotted hierarchical summary receiver operating characteristic curves. The area under the hierarchical summary receiver operating characteristic curve (AUC) served as an indicator of the diagnostic accuracy and during head-to-head comparisons. RESULTS A total of 7680 images of colorectal polyps from 18 studies were included in the analysis of histology prediction. The accuracy of the AI (AUC) was .96 (95% confidence interval [CI], .95-.98), with a corresponding pooled sensitivity of 92.3% (95% CI, 88.8%-94.9%) and specificity of 89.8% (95% CI, 85.3%-93.0%). The AUC of AI using narrow-band imaging (NBI) was significantly higher than the AUC using non-NBI (.98 vs .84, P < .01). The performance of AI was superior to nonexpert endoscopists (.97 vs .90, P < .01). For characterization of diminutive polyps using a deep learning model with nonmagnifying NBI, the pooled negative predictive value was 95.1% (95% CI, 87.7%-98.1%). For polyp detection, the pooled AUC was .90 (95% CI, .67-1.00) with a sensitivity of 95.0% (95% CI, 91.0%-97.0%) and a specificity of 88.0% (95% CI, 58.0%-99.0%). CONCLUSIONS AI was accurate in histology prediction and detection of colorectal polyps, including diminutive polyps. The performance of AI was better under NBI and was superior to nonexpert endoscopists. Despite the difference in AI models and study designs, AI performances are rather consistent, which could serve as a reference for future AI studies.
Collapse
Affiliation(s)
- Thomas K L Lui
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| | - Chuan-Guo Guo
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| | - Wai K Leung
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong.
| |
Collapse
|
77
|
Millien VO, Mansour NM. Bowel Preparation for Colonoscopy in 2020: A Look at the Past, Present, and Future. Curr Gastroenterol Rep 2020; 22:28. [PMID: 32377915 DOI: 10.1007/s11894-020-00764-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF THIS REVIEW Colorectal cancer is the third most common cancer in the USA. Colonoscopy is considered the gold standard for colorectal cancer screening and can offer both diagnosis and therapy. The bowel preparation remains a significant barrier for patients who need to undergo colonoscopy and is often cited as the most dreaded aspect of the colonoscopy process. Inadequate bowel preparations still occur in 10-25% of colonoscopies, and this in turn can lead to increased procedural times, lower cecal intubation rates, and shorter interval between colonoscopies. From a quality standpoint, it is imperative that we do what we can to decrease the rate of inadequate bowel preparations. This review will focus on recent data regarding bowel preparation and offers a glimpse into what may be coming in the future. RECENT FINDINGS Recent advances in the field have been made to improve tolerability of bowel preparations and allow for more adequate colonoscopies. Newer, lower volume, flavored preparations, the use of adjuncts, and using split-dose preparations all can help with tolerability, compliance, and, in turn, preparation quality. Edible bowel preparations may become available in the near future. Early data on the use of artificial intelligence for assessment of preparation quality has been promising. Additionally, utilization of smartphone technology for education prior to the bowel preparation has also been shown to improve the adequacy of bowel preparations. CONCLUSIONS Ongoing efforts to improve the tolerability and palatability of colonoscopy bowel preparations are important from a quality improvement standpoint to ensure the adequacy of colonoscopy. Incorporating patient-specific factors and comorbidities is also an essential aspect of improving the quality of bowel preparation. Leveraging technology to better communicate with and educate patients on the bowel preparation process is likely to play a larger role in the coming years.
Collapse
Affiliation(s)
- Valentine Ongeri Millien
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA
| | - Nabil M Mansour
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA.
| |
Collapse
|
78
|
Almadi MA, Ho KY. Artificial inelegance in endoscopy: An updated auricle of Delphi! Saudi J Gastroenterol 2020; 26:1-3. [PMID: 32098934 PMCID: PMC7045776 DOI: 10.4103/sjg.sjg_636_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Affiliation(s)
- Majid A. Almadi
- Division of Gastroenterology, Department of Medicine, King Khalid University Hospital, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Division of Gastroenterology, The McGill University Health Center, Montreal General Hospital, McGill University, Montreal, Canada
| | - Khek Yu Ho
- Department of Medicine, National University Hospital, Singapore
| |
Collapse
|