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Mori Y, Kudo SE, East JE, Rastogi A, Bretthauer M, Misawa M, Sekiguchi M, Matsuda T, Saito Y, Ikematsu H, Hotta K, Ohtsuka K, Kudo T, Mori K. Cost savings in colonoscopy with artificial intelligence-aided polyp diagnosis: an add-on analysis of a clinical trial (with video). Gastrointest Endosc 2020; 92:905-911.e1. [PMID: 32240683 DOI: 10.1016/j.gie.2020.03.3759] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 03/16/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI) is being implemented in colonoscopy practice, but no study has investigated whether AI is cost saving. We aimed to quantify the cost reduction using AI as an aid in the optical diagnosis of colorectal polyps. METHODS This study is an add-on analysis of a clinical trial that investigated the performance of AI for differentiating colorectal polyps (ie, neoplastic versus non-neoplastic). We included all patients with diminutive (≤5 mm) rectosigmoid polyps in the analyses. The average colonoscopy cost was compared for 2 scenarios: (1) a diagnose-and-leave strategy supported by the AI prediction (ie, diminutive rectosigmoid polyps were not removed when predicted as non-neoplastic), and (2) a resect-all-polyps strategy. Gross annual costs for colonoscopies were also calculated based on the number and reimbursement of colonoscopies conducted under public health insurances in 4 countries. RESULTS Overall, 207 patients with 250 diminutive rectosigmoid polyps (104 neoplastic, 144 non-neoplastic, and 2 indeterminate) were included. AI correctly differentiated neoplastic polyps with 93.3% sensitivity, 95.2% specificity, and 95.2% negative predictive value. Thus, 105 polyps were removed and 145 were left under the diagnose-and-leave strategy, which was estimated to reduce the average colonoscopy cost and the gross annual reimbursement for colonoscopies by 18.9% and US$149.2 million in Japan, 6.9% and US$12.3 million in England, 7.6% and US$1.1 million in Norway, and 10.9% and US$85.2 million in the United States, respectively, compared with the resect-all-polyps strategy. CONCLUSIONS The use of AI to enable the diagnose-and-leave strategy results in substantial cost reductions for colonoscopy.
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Affiliation(s)
- Yuichi Mori
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - James E East
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, United Kingdom; Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - Amit Rastogi
- Division of Gastroenterology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Michael Bretthauer
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway; Department of Transplantation Medicine, Oslo University Hospital, Oslo, Norway
| | - Masashi Misawa
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Masau Sekiguchi
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan; Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan; Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Takahisa Matsuda
- Cancer Screening Center, National Cancer Center Hospital, Tokyo, Japan; Division of Screening Technology, Center for Public Health Sciences, National Cancer Center, Tokyo, Japan; Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Hiroaki Ikematsu
- Department of Gastroenterology and Endoscopy, National Cancer Center Hospital East, Kashiwa, Japan
| | - Kinichi Hotta
- Division of Endoscopy, Shizuoka Cancer Center, Shizuoka, Japan
| | - Kazuo Ohtsuka
- Department of Endoscopy, Tokyo Medical and Dental University, Tokyo, Japan
| | - Toyoki Kudo
- Department of Health Management and Health Economics, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Kensaku Mori
- Graduate School of Informatics, Nagoya University, Nagoya, Japan
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Dong J, Geng Y, Lu D, Li B, Tian L, Lin D, Zhang Y. Clinical Trials for Artificial Intelligence in Cancer Diagnosis: A Cross-Sectional Study of Registered Trials in ClinicalTrials.gov. Front Oncol 2020; 10:1629. [PMID: 33042806 PMCID: PMC7522504 DOI: 10.3389/fonc.2020.01629] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/27/2020] [Indexed: 02/05/2023] Open
Abstract
Objective: Clinical trials are the most effective way to judge the merits of diagnosis and treatment strategies. The in-depth mining of clinical trial data enables us to grasp the application trend of artificial intelligence (AI) for cancer diagnosis. The aim of this study was to analyze the characteristics of registered trials on AI for cancer diagnosis. Methods: Clinical trials on AI for cancer diagnosis registered on the ClinicalTrials.gov database were searched and downloaded. Statistical analysis was performed by using SPSS 20.0 software. Results: A total of 97 registered trials were included. Of them, only 27 (27.8%) were interventional trials and 70 (72.1%) were observational trials. Fifteen (15.4%) trials had been completed. Fifty trials were in recruitment, and another 18 remained unrecruited. The number of cases included in the clinical trials tended to be large, 31 (32.0%) trials including samples ranging from 100 to 499 cases and 17 (17.5%) trials including samples ranging from 500 to 999 cases. Of the 27 interventional trials, only two trials reported trials' phase. Most (85.2%) interventional trials were for diagnosis, and a few (3.7%) were for the purpose of both the diagnosis and therapy of cancers. For the observational clinical trials, 46 (65.7%) were cohort studies, and 11 (15.7%) were case-only studies. Among the observational trials, 46 (65.7%) were prospective studies and 13 (18.6%) were retrospective studies. Among 97 trials, 37 (38.1%) involved colorectal cancer, 11 (11.3%) involved breast cancer, 43 (44.3%) were for imaging diagnosis, 33 (34.0%) were for endoscopic diagnosis, and 11 (11.3%) were for pathological diagnosis. For the interventional trials, 11 trials were parallel assignment (40.7%), and 14 were single group assignment (51.9%). Among the 27 interventional trials, 18 (66.7%) trials were performed without masking, 6 (22.2%) trials were performed with single masking, only 1 (3.7%) was performed with double masking, and 2 (7.4%) was performed with triple masking. Conclusion: It appears that most registered trials on AI for cancer diagnosis are observational design, and more trials are needed in this field.
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Affiliation(s)
- Jingsi Dong
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yingcai Geng
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Lu
- Department of Otorhinolaryngology, Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Bingjie Li
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Long Tian
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Dan Lin
- Department of Lung Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yonggang Zhang
- Department of Periodical Press and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, China
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103
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Sánchez-Peralta LF, Bote-Curiel L, Picón A, Sánchez-Margallo FM, Pagador JB. Deep learning to find colorectal polyps in colonoscopy: A systematic literature review. Artif Intell Med 2020; 108:101923. [PMID: 32972656 DOI: 10.1016/j.artmed.2020.101923] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 03/03/2020] [Accepted: 07/01/2020] [Indexed: 02/07/2023]
Abstract
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.
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Affiliation(s)
| | - Luis Bote-Curiel
- Jesús Usón Minimally Invasive Surgery Centre, Ctra. N-521, km 41.8, 10071 Cáceres, Spain.
| | - Artzai Picón
- Tecnalia, Parque Científico y Tecnológico de Bizkaia, C/ Astondo bidea, Edificio 700, 48160 Derio, Spain.
| | | | - J Blas Pagador
- Jesús Usón Minimally Invasive Surgery Centre, Ctra. N-521, km 41.8, 10071 Cáceres, Spain.
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104
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Yuan C, Yao Y, Cheng B, Cheng Y, Li Y, Li Y, Liu X, Cheng X, Xie X, Wu J, Wang X, Lu W. The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images. Sci Rep 2020; 10:11639. [PMID: 32669565 PMCID: PMC7363819 DOI: 10.1038/s41598-020-68252-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 06/16/2020] [Indexed: 12/12/2022] Open
Abstract
Background Deep learning has presented considerable potential and is gaining more importance in computer assisted diagnosis. As the gold standard for pathologically diagnosing cervical intraepithelial lesions and invasive cervical cancer, colposcopy-guided biopsy faces challenges in improving accuracy and efficiency worldwide, especially in developing countries. To ease the heavy burden of cervical cancer screening, it is urgent to establish a scientific, accurate and efficient method for assisting diagnosis and biopsy. Methods The data were collected to establish three deep-learning-based models. For every case, one saline image, one acetic image, one iodine image and the corresponding clinical information, including age, the results of human papillomavirus testing and cytology, type of transformation zone, and pathologic diagnosis, were collected. The dataset was proportionally divided into three subsets including the training set, the test set and the validation set, at a ratio of 8:1:1. The validation set was used to evaluate model performance. After model establishment, an independent dataset of high-definition images was collected to further evaluate the model performance. In addition, the comparison of diagnostic accuracy between colposcopists and models weas performed. Results The sensitivity, specificity and accuracy of the classification model to differentiate negative cases from positive cases were 85.38%, 82.62% and 84.10% respectively, with an AUC of 0.93. The recall and DICE of the segmentation model to segment suspicious lesions in acetic images were 84.73% and 61.64%, with an average accuracy of 95.59%. Furthermore, 84.67% of high-grade lesions were detected by the acetic detection model. Compared to colposcopists, the diagnostic system performed better in ordinary colposcopy images but slightly unsatisfactory in high-definition images. Implications The deep learning-based diagnostic system could help assist colposcopy diagnosis and biopsy for HSILs.
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Affiliation(s)
- Chunnv Yuan
- Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Yeli Yao
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Bei Cheng
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Yifan Cheng
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Ying Li
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Yang Li
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Xuechen Liu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
| | - Xiaodong Cheng
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Xing Xie
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Jian Wu
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China
| | - Xinyu Wang
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Hangzhou, 310006, China
| | - Weiguo Lu
- Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, China.
- Center for Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Hangzhou, 310006, China.
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105
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Shung DL, Byrne MF. How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening. Gastrointest Endosc Clin N Am 2020; 30:585-595. [PMID: 32439090 DOI: 10.1016/j.giec.2020.02.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance by improving quality and decreasing unnecessary costs. The quality of screening and surveillance as measured by adenoma detection rates can be improved through real-time computer-assisted detection of polyps. Unnecessary costs can be decreased with optical biopsies to identify low-risk polyps using computer-assisted diagnosis that can undergo the resect-and-discard or diagnose-and-leave strategy. Key challenges include the clinical integration of artificial intelligence-based technology into the endoscopists' workflow, the effect of this technology on endoscopy center efficiency, and the interpretability of the underlying deep learning algorithms. The future for image-based artificial intelligence in gastroenterology will include applications to improve the diagnosis and treatment of cancers throughout the gastrointestinal tract.
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Affiliation(s)
- Dennis L Shung
- Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, P.O. Box 208019, New Haven, CT 06520-8019, USA
| | - Michael F Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, 5153 - 2775 Laurel Street, Vancouver, British Columbia, Canada.
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106
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Zhou D, Tian F, Tian X, Sun L, Huang X, Zhao F, Zhou N, Chen Z, Zhang Q, Yang M, Yang Y, Guo X, Li Z, Liu J, Wang J, Wang J, Wang B, Zhang G, Sun B, Zhang W, Kong D, Chen K, Li X. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat Commun 2020; 11:2961. [PMID: 32528084 PMCID: PMC7289893 DOI: 10.1038/s41467-020-16777-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/19/2020] [Indexed: 12/24/2022] Open
Abstract
Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model called CRCNet for optical diagnosis of CRC by training on 464,105 images from 12,179 patients and test its performance on 2263 patients from three independent datasets. At the patient-level, CRCNet achieves an area under the precision-recall curve (AUPRC) of 0.882 (95% CI: 0.828-0.931), 0.874 (0.820-0.926) and 0.867 (0.795-0.923). CRCNet exceeds average endoscopists performance on recall rate across two test sets (91.3% versus 83.8%; two-sided t-test, p < 0.001 and 96.5% versus 90.3%; p = 0.006) and precision for one test set (93.7% versus 83.8%; p = 0.02), while obtains comparable recall rate on one test set and precision on the other two. At the image-level, CRCNet achieves an AUPRC of 0.990 (0.987-0.993), 0.991 (0.987-0.995), and 0.997 (0.995-0.999). Our study warrants further investigation of CRCNet by prospective clinical trials.
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Affiliation(s)
- Dejun Zhou
- Department of Endoscopic Diagnosis and Therapy, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Fei Tian
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xiangdong Tian
- Department of Endoscopic Diagnosis and Therapy, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Lin Sun
- Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xianghui Huang
- General Surgery Department, Ningbo First Hospital, Zhejiang University Ningbo Hospital, Ningbo, China
| | - Feng Zhao
- Anorectal Department, Nanyang Hospital of Traditional Chinese Medicine, Nanyang, China
| | - Nan Zhou
- Anorectal Department, Nanyang Hospital of Traditional Chinese Medicine, Nanyang, China
| | - Zuoyu Chen
- General Surgery Department, General Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, China
| | - Qiang Zhang
- Department of Maxillofacial and Otorhinolaryngology Oncology, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Meng Yang
- Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Yichen Yang
- Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Xuexi Guo
- Department of Pathology, Tianjin First Central Hospital, Tianjin, China
| | - Zhibin Li
- Department of Gastroenterology and Hepatobiliary Surgery, People's First Hospital of Shangqiu, Shangqiu, China
| | - Jia Liu
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jiefu Wang
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Junfeng Wang
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Bangmao Wang
- Department of Gastroenterology, General Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, China
| | - Guoliang Zhang
- Department of Digestion and Endoscopy, Tianjin First Central Hospital, Tianjin, China
| | - Baocun Sun
- Department of Pathology, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Wei Zhang
- Wake Forest Baptist Comprehensive Cancer Center, Wake Forest Baptist Medical Center, Winston-Salem, NC, USA
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Dalu Kong
- Department of Colorectal Cancer, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University, Tianjin, China.
| | - Xiangchun Li
- Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
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107
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Ciuti G, Skonieczna-Żydecka K, Marlicz W, Iacovacci V, Liu H, Stoyanov D, Arezzo A, Chiurazzi M, Toth E, Thorlacius H, Dario P, Koulaouzidis A. Frontiers of Robotic Colonoscopy: A Comprehensive Review of Robotic Colonoscopes and Technologies. J Clin Med 2020; 9:E1648. [PMID: 32486374 PMCID: PMC7356873 DOI: 10.3390/jcm9061648] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/16/2020] [Accepted: 05/19/2020] [Indexed: 12/15/2022] Open
Abstract
Flexible colonoscopy remains the prime mean of screening for colorectal cancer (CRC) and the gold standard of all population-based screening pathways around the world. Almost 60% of CRC deaths could be prevented with screening. However, colonoscopy attendance rates are affected by discomfort, fear of pain and embarrassment or loss of control during the procedure. Moreover, the emergence and global thread of new communicable diseases might seriously affect the functioning of contemporary centres performing gastrointestinal endoscopy. Innovative solutions are needed: artificial intelligence (AI) and physical robotics will drastically contribute for the future of the healthcare services. The translation of robotic technologies from traditional surgery to minimally invasive endoscopic interventions is an emerging field, mainly challenged by the tough requirements for miniaturization. Pioneering approaches for robotic colonoscopy have been reported in the nineties, with the appearance of inchworm-like devices. Since then, robotic colonoscopes with assistive functionalities have become commercially available. Research prototypes promise enhanced accessibility and flexibility for future therapeutic interventions, even via autonomous or robotic-assisted agents, such as robotic capsules. Furthermore, the pairing of such endoscopic systems with AI-enabled image analysis and recognition methods promises enhanced diagnostic yield. By assembling a multidisciplinary team of engineers and endoscopists, the paper aims to provide a contemporary and highly-pictorial critical review for robotic colonoscopes, hence providing clinicians and researchers with a glimpse of the major changes and challenges that lie ahead.
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Affiliation(s)
- Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy; (V.I.); (M.C.); (P.D.)
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Karolina Skonieczna-Żydecka
- Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland;
| | - Wojciech Marlicz
- Department of Gastroenterology, Pomeranian Medical University in Szczecin, 71-252 Szczecin, Poland;
- Endoklinika sp. z o.o., 70-535 Szczecin, Poland
| | - Veronica Iacovacci
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy; (V.I.); (M.C.); (P.D.)
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Hongbin Liu
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London SE1 7EH, UK;
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London W1W 7TY, UK;
| | - Alberto Arezzo
- Department of Surgical Sciences, University of Torino, 10126 Torino, Italy;
| | - Marcello Chiurazzi
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy; (V.I.); (M.C.); (P.D.)
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
| | - Ervin Toth
- Department of Gastroenterology, Skåne University Hospital, Lund University, 20502 Malmö, Sweden;
| | - Henrik Thorlacius
- Department of Clinical Sciences, Section of Surgery, Lund University, 20502 Malmö, Sweden;
| | - Paolo Dario
- The BioRobotics Institute, Scuola Superiore Sant’Anna, 56025 Pisa, Italy; (V.I.); (M.C.); (P.D.)
- Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
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108
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Poon CCY, Jiang Y, Zhang R, Lo WWY, Cheung MSH, Yu R, Zheng Y, Wong JCT, Liu Q, Wong SH, Mak TWC, Lau JYW. AI-doscopist: a real-time deep-learning-based algorithm for localising polyps in colonoscopy videos with edge computing devices. NPJ Digit Med 2020; 3:73. [PMID: 32435701 PMCID: PMC7235017 DOI: 10.1038/s41746-020-0281-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/28/2020] [Indexed: 12/24/2022] Open
Abstract
We have designed a deep-learning model, an "Artificial Intelligent Endoscopist (a.k.a. AI-doscopist)", to localise colonic neoplasia during colonoscopy. This study aims to evaluate the agreement between endoscopists and AI-doscopist for colorectal neoplasm localisation. AI-doscopist was pre-trained by 1.2 million non-medical images and fine-tuned by 291,090 colonoscopy and non-medical images. The colonoscopy images were obtained from six databases, where the colonoscopy images were classified into 13 categories and the polyps' locations were marked image-by-image by the smallest bounding boxes. Seven categories of non-medical images, which were believed to share some common features with colorectal polyps, were downloaded from an online search engine. Written informed consent were obtained from 144 patients who underwent colonoscopy and their full colonoscopy videos were prospectively recorded for evaluation. A total of 128 suspicious lesions were resected or biopsied for histological confirmation. When evaluated image-by-image on the 144 full colonoscopies, the specificity of AI-doscopist was 93.3%. AI-doscopist were able to localise 124 out of 128 polyps (polyp-based sensitivity = 96.9%). Furthermore, after reviewing the suspected regions highlighted by AI-doscopist in a 102-patient cohort, an endoscopist has high confidence in recognizing four missed polyps in three patients who were not diagnosed with any lesion during their original colonoscopies. In summary, AI-doscopist can localise 96.9% of the polyps resected by the endoscopists. If AI-doscopist were to be used in real-time, it can potentially assist endoscopists in detecting one more patient with polyp in every 20-33 colonoscopies.
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Affiliation(s)
- Carmen C. Y. Poon
- Division of Biomedical Engineering Research, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Yuqi Jiang
- Division of Biomedical Engineering Research, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Ruikai Zhang
- Division of Biomedical Engineering Research, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Winnie W. Y. Lo
- Division of Biomedical Engineering Research, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Maggie S. H. Cheung
- Division of Vascular and General Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Ruoxi Yu
- Division of Biomedical Engineering Research, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Yali Zheng
- Division of Biomedical Engineering Research, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
- College of Health Science and Environmental Engineering, Shenzhen Technology University, Shenzhen, People’s Republic of China
| | - John C. T. Wong
- Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Qing Liu
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, People’s Republic of China
| | - Sunny H. Wong
- Division of Gastroenterology and Hepatology, Department of Medicine and Therapeutics, Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - Tony W. C. Mak
- Division of Colorectal Surgery, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
| | - James Y. W. Lau
- Division of Vascular and General Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China
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109
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Ahmad OF, Stoyanov D, Lovat LB. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.tgie.2019.150636] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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110
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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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111
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Ahmad OF. Deep learning for colorectal polyp detection: time for clinical implementation? Lancet Gastroenterol Hepatol 2020; 5:330-331. [PMID: 31981521 DOI: 10.1016/s2468-1253(19)30431-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 12/16/2019] [Indexed: 01/02/2023]
Affiliation(s)
- Omer F Ahmad
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London W1W 7TS, UK.
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112
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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
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113
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Sivananthan A, Glover B, Ayaru L, Patel K, Darzi A, Patel N. The evolution of lower gastrointestinal endoscopy: where are we now? Ther Adv Gastrointest Endosc 2020; 13:2631774520979591. [PMID: 33426522 PMCID: PMC7754801 DOI: 10.1177/2631774520979591] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/06/2020] [Indexed: 12/14/2022] Open
Abstract
Lower gastrointestinal endoscopy has evolved over time, fulfilling a widening diagnostic and therapeutic remit. As our understanding of colorectal cancer and its prevention has improved, endoscopy has progressed with improved diagnostic technologies and advancing endoscopic therapies. Despite this, the fundamental design of the endoscope has remained similar since its inception. This review presents the important role lower gastrointestinal endoscopy serves in the prevention of colorectal cancer and the desirable characteristics of the endoscope that would enhance this. A brief history of the endoscope is presented. Current and future robotic endoscopic platforms, which may fulfil these desirable characteristics, are discussed. The incorporation of new technologies from allied scientific disciplines will help the endoscope fulfil its maximum potential in preventing the increasing global burden of colorectal cancer. There are a number of endoscopic platforms under development, which show significant promise.
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Affiliation(s)
| | | | | | - Kinesh Patel
- Chelsea and Westminster NHS Healthcare Trust, UK
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114
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Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Citation(s) in RCA: 280] [Impact Index Per Article: 70.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 02/07/2023]
Abstract
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
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Affiliation(s)
- Catherine Le Berre
- Institut des Maladies de l'Appareil Digestif, Nantes University Hospital, France; Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France
| | | | - Sabeur Aridhi
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Marie-Dominique Devignes
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Laure Fournier
- Université Paris-Descartes, Institut National de la Santé et de la Recherche Médicale, Unité Mixte De Recherché S970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Malika Smaïl-Tabbone
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Silvio Danese
- Inflammatory Bowel Disease Center and Department of Biomedical Sciences, Humanitas Clinical and Research Center, Humanitas University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France.
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115
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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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116
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Cummins G, Cox BF, Ciuti G, Anbarasan T, Desmulliez MPY, Cochran S, Steele R, Plevris JN, Koulaouzidis A. Gastrointestinal diagnosis using non-white light imaging capsule endoscopy. Nat Rev Gastroenterol Hepatol 2019; 16:429-447. [PMID: 30988520 DOI: 10.1038/s41575-019-0140-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Capsule endoscopy (CE) has proved to be a powerful tool in the diagnosis and management of small bowel disorders since its introduction in 2001. However, white light imaging (WLI) is the principal technology used in clinical CE at present, and therefore, CE is limited to mucosal inspection, with diagnosis remaining reliant on visible manifestations of disease. The introduction of WLI CE has motivated a wide range of research to improve its diagnostic capabilities through integration with other sensing modalities. These developments have the potential to overcome the limitations of WLI through enhanced detection of subtle mucosal microlesions and submucosal and/or transmural pathology, providing novel diagnostic avenues. Other research aims to utilize a range of sensors to measure physiological parameters or to discover new biomarkers to improve the sensitivity, specificity and thus the clinical utility of CE. This multidisciplinary Review summarizes research into non-WLI CE devices by organizing them into a taxonomic structure on the basis of their sensing modality. The potential of these capsules to realize clinically useful virtual biopsy and computer-aided diagnosis (CADx) is also reported.
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Affiliation(s)
- Gerard Cummins
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
| | | | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Marc P Y Desmulliez
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK
| | - Sandy Cochran
- School of Engineering, University of Glasgow, Glasgow, UK
| | - Robert Steele
- School of Medicine, University of Dundee, Dundee, UK
| | - John N Plevris
- Centre for Liver and Digestive Disorders, The Royal Infirmary of Edinburgh, Edinburgh, UK
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117
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Chand M, Ramachandran N, Stoyanov D, Lovat L. Robotics, artificial intelligence and distributed ledgers in surgery: data is key! Tech Coloproctol 2018; 22:645-648. [PMID: 30242534 DOI: 10.1007/s10151-018-1847-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 09/02/2018] [Indexed: 12/17/2022]
Affiliation(s)
- M Chand
- Division of Surgery and Interventional Sciences, Gastrointestinal Services Department, University College London, University College London Hospitals NHS Foundation Trust, London, UK. .,Department of Surgery and Interventional Sciences, University College London Hospitals, NHS Trusts, GENIE Centre, University College London, 235 Euston Rd, London, NW1 2BU, UK.
| | - N Ramachandran
- Radiology Department, University College London Hospitals, NHS Foundation Trust, London, UK
| | - D Stoyanov
- Division of Engineering, University College London, London, UK
| | - L Lovat
- Division of Surgery and Interventional Sciences, Gastrointestinal Services Department, University College London, University College London Hospitals NHS Foundation Trust, London, UK
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