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Kusters CHJ, Jaspers TJM, Boers TGW, Jong MR, Jukema JB, Fockens KN, de Groof AJ, Bergman JJ, van der Sommen F, De With PHN. Will Transformers change gastrointestinal endoscopic image analysis? A comparative analysis between CNNs and Transformers, in terms of performance, robustness and generalization. Med Image Anal 2024; 99:103348. [PMID: 39298861 DOI: 10.1016/j.media.2024.103348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 07/10/2024] [Accepted: 09/10/2024] [Indexed: 09/22/2024]
Abstract
Gastrointestinal endoscopic image analysis presents significant challenges, such as considerable variations in quality due to the challenging in-body imaging environment, the often-subtle nature of abnormalities with low interobserver agreement, and the need for real-time processing. These challenges pose strong requirements on the performance, generalization, robustness and complexity of deep learning-based techniques in such safety-critical applications. While Convolutional Neural Networks (CNNs) have been the go-to architecture for endoscopic image analysis, recent successes of the Transformer architecture in computer vision raise the possibility to update this conclusion. To this end, we evaluate and compare clinically relevant performance, generalization and robustness of state-of-the-art CNNs and Transformers for neoplasia detection in Barrett's esophagus. We have trained and validated several top-performing CNNs and Transformers on a total of 10,208 images (2,079 patients), and tested on a total of 7,118 images (998 patients) across multiple test sets, including a high-quality test set, two internal and two external generalization test sets, and a robustness test set. Furthermore, to expand the scope of the study, we have conducted the performance and robustness comparisons for colonic polyp segmentation (Kvasir-SEG) and angiodysplasia detection (Giana). The results obtained for featured models across a wide range of training set sizes demonstrate that Transformers achieve comparable performance as CNNs on various applications, show comparable or slightly improved generalization capabilities and offer equally strong resilience and robustness against common image corruptions and perturbations. These findings confirm the viability of the Transformer architecture, particularly suited to the dynamic nature of endoscopic video analysis, characterized by fluctuating image quality, appearance and equipment configurations in transition from hospital to hospital. The code is made publicly available at: https://github.com/BONS-AI-VCA-AMC/Endoscopy-CNNs-vs-Transformers.
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Affiliation(s)
- Carolus H J Kusters
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands.
| | - Tim J M Jaspers
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Tim G W Boers
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Martijn R Jong
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jelmer B Jukema
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Kiki N Fockens
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Albert J de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Jacques J Bergman
- Department of Gastroenterology and Hepatology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Peter H N De With
- Department of Electrical Engineering, Video Coding & Architectures, Eindhoven University of Technology, Eindhoven, The Netherlands
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Clarke JA, Benning J, Isaacs J, Angell-Clarke S. A balance of clinical assessment and use of diagnostic imaging: A CT colonography comparative case report. Radiol Case Rep 2024; 19:2751-2755. [PMID: 38680738 PMCID: PMC11047173 DOI: 10.1016/j.radcr.2024.03.066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/09/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
Computer tomography colonography (CTC) is a non-invasive procedure which has replaced barium enema. CTC uses helical images of a cleansed and gas-distended colon for the diagnosis and treatment of colonic neoplasms. This case study compares 2 patients: one with positive pathology (patient A) and another as comparator (patient B) with a similar pathology to discuss and debate possible treatment pathways. Patient (A) CTC showed 2 polyps: 6 mm and 10 mm, which the colorectal surgeons thought only needed follow-up. Our comparator (patient B) displayed a similar pathology which measured 9 mm. In this case (patient B), there was mutual agreement with the surgeons for polypectomy but without haematology involvement which was atypical of the usual pathway. The surgeons did not see the 9 mm polyp at polypectomy which could be due to observer error or radiology reporter error. Given that conventional colonoscopy is more sensitive in detecting polyps; a repeat of both tests could confirm the presence of polyp, however, the surgeons gave patient (B) a virtual appointment and requested a repeat CTC in 12 months. In colorectal medicine there can be variations in the treatment of patients with polyps. While a repeat of both tests could confirm the presence of polyp in patient (B), the surgeons' decisions regarding the patient's treatment reflected a balance of confidence in clinical assessment and use of diagnostic imaging which can reduce unnecessary requests and use of diagnostic tests.
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Affiliation(s)
- Justin A. Clarke
- Ashford and St. Peter's Hospitals Radiology Department, Guilford Road, Chertsey, Surrey, UK
| | - Jeevon Benning
- Ashford and St. Peter's Hospitals Radiology Department, Guilford Road, Chertsey, Surrey, UK
| | - John Isaacs
- Ashford and St. Peter's Hospitals Research and Development Department, Guilford Road, Chertsey, Surrey, UK
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3
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Spadaccini M, Troya J, Khalaf K, Facciorusso A, Maselli R, Hann A, Repici A. Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going? Dig Liver Dis 2024; 56:1148-1155. [PMID: 38458884 DOI: 10.1016/j.dld.2024.01.203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/22/2024] [Accepted: 01/23/2024] [Indexed: 03/10/2024]
Abstract
Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy.
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Kareem Khalaf
- Division of Gastroenterology, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Surgical and Medical Sciences, University of Foggia, Foggia, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital Würzburg, Würzburg, Germany
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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Ichimasa K, Kudo SE, Misawa M, Takashina Y, Yeoh KG, Miyachi H. Role of the artificial intelligence in the management of T1 colorectal cancer. Dig Liver Dis 2024; 56:1144-1147. [PMID: 38311532 DOI: 10.1016/j.dld.2024.01.202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 01/24/2024] [Indexed: 02/06/2024]
Abstract
Approximately 10% of submucosal invasive (T1) colorectal cancers demonstrate extraintestinal lymph node metastasis, necessitating surgical intervention with lymph node dissection. The ability to identify T1b (submucosal invasion depth ≥ 1000 µm) as a risk factor for lymph node metastasis via pre-treatment endoscopy is crucial in guiding treatment strategies. Accurately distinguishing T1b from T1a (submucosal invasion depth < 1000 µm) or dysplasia remains a significant challenge for artificial intelligence (AI) systems, which require high and consistent diagnostic capabilities. Moreover, as endoscopic therapies like endoscopic full-thickness resection and endoscopic intermuscular dissection evolve, and the focus on reducing unnecessary surgeries intensifies, the initial management of T1 colorectal cancers via endoscopic treatment is anticipated to increase. Consequently, the development of highly accurate and reliable AI systems is essential, not only for pre-treatment depth assessment but also for post-treatment risk stratification of lymph node metastasis. While such AI diagnostic systems are still under development, significant advancements are expected in the near future to improve decision-making in T1 colorectal cancer management.
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Affiliation(s)
- Katsuro Ichimasa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki Chuo, Tsuzuki-ku, Yokohama, Kanagawa 224-8503, Japan; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
| | - Shin-Ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki Chuo, Tsuzuki-ku, Yokohama, Kanagawa 224-8503, Japan
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki Chuo, Tsuzuki-ku, Yokohama, Kanagawa 224-8503, Japan
| | - Yuki Takashina
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki Chuo, Tsuzuki-ku, Yokohama, Kanagawa 224-8503, Japan
| | - Khay Guan Yeoh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Hideyuki Miyachi
- Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki Chuo, Tsuzuki-ku, Yokohama, Kanagawa 224-8503, Japan
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Wang X, Guan X, Tong Y, Liang Y, Huang Z, Wen M, Luo J, Chen H, Yang S, She Z, Wei Z, Zhou Y, Qi Y, Zhu P, Nong Y, Zhang Q. UHPLC-HRMS-based Multiomics to Explore the Potential Mechanisms and Biomarkers for Colorectal Cancer. BMC Cancer 2024; 24:644. [PMID: 38802800 PMCID: PMC11129395 DOI: 10.1186/s12885-024-12321-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum. METHODS Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards. RESULTS Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00). CONCLUSIONS To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention.
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Affiliation(s)
- Xuancheng Wang
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Xuan Guan
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Ying Tong
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Yunxiao Liang
- Department of Gastroenterology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, 530021, PR China
| | - Zongsheng Huang
- Department of Gastroenterology, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, 530021, PR China
| | - Mingsen Wen
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Jichu Luo
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Hongwei Chen
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Shanyi Yang
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Zhiyong She
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Zhijuan Wei
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Yun Zhou
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Yali Qi
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Pingchuan Zhu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, Guangxi, 530004, PR China
| | - Yanying Nong
- Department of Academic Affairs, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, 530021, PR China.
| | - Qisong Zhang
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, 530004, PR China.
- Center for Instrumental Analysis, Guangxi University, Nanning, Guangxi, 530004, PR China.
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Guo H, Somayajula SA, Hosseini R, Xie P. Improving image classification of gastrointestinal endoscopy using curriculum self-supervised learning. Sci Rep 2024; 14:6100. [PMID: 38480815 PMCID: PMC10937990 DOI: 10.1038/s41598-024-53955-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 03/17/2024] Open
Abstract
Endoscopy, a widely used medical procedure for examining the gastrointestinal (GI) tract to detect potential disorders, poses challenges in manual diagnosis due to non-specific symptoms and difficulties in accessing affected areas. While supervised machine learning models have proven effective in assisting clinical diagnosis of GI disorders, the scarcity of image-label pairs created by medical experts limits their availability. To address these limitations, we propose a curriculum self-supervised learning framework inspired by human curriculum learning. Our approach leverages the HyperKvasir dataset, which comprises 100k unlabeled GI images for pre-training and 10k labeled GI images for fine-tuning. By adopting our proposed method, we achieved an impressive top-1 accuracy of 88.92% and an F1 score of 73.39%. This represents a 2.1% increase over vanilla SimSiam for the top-1 accuracy and a 1.9% increase for the F1 score. The combination of self-supervised learning and a curriculum-based approach demonstrates the efficacy of our framework in advancing the diagnosis of GI disorders. Our study highlights the potential of curriculum self-supervised learning in utilizing unlabeled GI tract images to improve the diagnosis of GI disorders, paving the way for more accurate and efficient diagnosis in GI endoscopy.
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Affiliation(s)
- Han Guo
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, 92093, USA
| | - Sai Ashish Somayajula
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, 92093, USA
| | - Ramtin Hosseini
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, 92093, USA
| | - Pengtao Xie
- Department of Electrical and Computer Engineering, University of California, San Diego, San Diego, 92093, USA.
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7
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Uchikov P, Khalid U, Kraev K, Hristov B, Kraeva M, Tenchev T, Chakarov D, Sandeva M, Dragusheva S, Taneva D, Batashki A. Artificial Intelligence in the Diagnosis of Colorectal Cancer: A Literature Review. Diagnostics (Basel) 2024; 14:528. [PMID: 38472999 DOI: 10.3390/diagnostics14050528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND The aim of this review is to explore the role of artificial intelligence in the diagnosis of colorectal cancer, how it impacts CRC morbidity and mortality, and why its role in clinical medicine is limited. METHODS A targeted, non-systematic review of the published literature relating to colorectal cancer diagnosis was performed with PubMed databases that were scouted to help provide a more defined understanding of the recent advances regarding artificial intelligence and their impact on colorectal-related morbidity and mortality. Articles were included if deemed relevant and including information associated with the keywords. RESULTS The advancements in artificial intelligence have been significant in facilitating an earlier diagnosis of CRC. In this review, we focused on evaluating genomic biomarkers, the integration of instruments with artificial intelligence, MR and hyperspectral imaging, and the architecture of neural networks. We found that these neural networks seem practical and yield positive results in initial testing. Furthermore, we explored the use of deep-learning-based majority voting methods, such as bag of words and PAHLI, in improving diagnostic accuracy in colorectal cancer detection. Alongside this, the autonomous and expansive learning ability of artificial intelligence, coupled with its ability to extract increasingly complex features from images or videos without human reliance, highlight its impact in the diagnostic sector. Despite this, as most of the research involves a small sample of patients, a diversification of patient data is needed to enhance cohort stratification for a more sensitive and specific neural model. We also examined the successful application of artificial intelligence in predicting microsatellite instability, showcasing its potential in stratifying patients for targeted therapies. CONCLUSIONS Since its commencement in colorectal cancer, artificial intelligence has revealed a multitude of functionalities and augmentations in the diagnostic sector of CRC. Given its early implementation, its clinical application remains a fair way away, but with steady research dedicated to improving neural architecture and expanding its applicational range, there is hope that these advanced neural software could directly impact the early diagnosis of CRC. The true promise of artificial intelligence, extending beyond the medical sector, lies in its potential to significantly influence the future landscape of CRC's morbidity and mortality.
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Affiliation(s)
- Petar Uchikov
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Usman Khalid
- Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Krasimir Kraev
- Department of Propaedeutics of Internal Diseases "Prof. Dr. Anton Mitov", Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Bozhidar Hristov
- Section "Gastroenterology", Second Department of Internal Diseases, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Maria Kraeva
- Department of Otorhinolaryngology, Medical Faculty, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Tihomir Tenchev
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Dzhevdet Chakarov
- Department of Propaedeutics of Surgical Diseases, Section of General Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
| | - Milena Sandeva
- Department of Midwifery, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Snezhanka Dragusheva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Daniela Taneva
- Department of Nursing Care, Faculty of Public Health, Medical University of Plovdiv, 4000 Plovdiv, Bulgaria
| | - Atanas Batashki
- Department of Special Surgery, Faculty of Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria
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Khalaf K, Fujiyoshi MRA, Spadaccini M, Rizkala T, Ramai D, Colombo M, Fugazza A, Facciorusso A, Carrara S, Hassan C, Repici A. From Staining Techniques to Artificial Intelligence: A Review of Colorectal Polyps Characterization. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:89. [PMID: 38256350 PMCID: PMC10818333 DOI: 10.3390/medicina60010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/24/2023] [Accepted: 12/26/2023] [Indexed: 01/24/2024]
Abstract
This review article provides a comprehensive overview of the evolving techniques in image-enhanced endoscopy (IEE) for the characterization of colorectal polyps, and the potential of artificial intelligence (AI) in revolutionizing the diagnostic accuracy of endoscopy. We discuss the historical use of dye-spray and virtual chromoendoscopy for the characterization of colorectal polyps, which are now being replaced with more advanced technologies. Specifically, we focus on the application of AI to create a "virtual biopsy" for the detection and characterization of colorectal polyps, with potential for replacing histopathological diagnosis. The incorporation of AI has the potential to provide an evolutionary learning system that aids in the diagnosis and management of patients with the best possible outcomes. A detailed analysis of the literature supporting AI-assisted diagnostic techniques for the detection and characterization of colorectal polyps, with a particular emphasis on AI's characterization mechanism, is provided. The benefits of AI over traditional IEE techniques, including the reduction in human error in diagnosis, and its potential to provide an accurate diagnosis with similar accuracy to the gold standard are presented. However, the need for large-scale testing of AI in clinical practice and the importance of integrating patient data into the diagnostic process are acknowledged. In conclusion, the constant evolution of IEE technology and the potential for AI to revolutionize the field of endoscopy in the future are presented.
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Affiliation(s)
- Kareem Khalaf
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5B 1T8, Canada; (K.K.); (M.R.A.F.)
| | - Mary Raina Angeli Fujiyoshi
- Division of Gastroenterology, St. Michael’s Hospital, University of Toronto, Toronto, ON M5B 1T8, Canada; (K.K.); (M.R.A.F.)
- Digestive Diseases Center, Showa University Koto Toyosu Hospital, Tokyo 135-8577, Japan
| | - Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Tommy Rizkala
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Daryl Ramai
- Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT 84132, USA;
| | - Matteo Colombo
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
| | - Antonio Facciorusso
- Department of Endoscopy, Section of Gastroenterology, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy;
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
| | - Cesare Hassan
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, 20089 Rozzano, Italy; (T.R.); (M.C.); (A.F.); (S.C.); (C.H.); (A.R.)
- Department of Biomedical Sciences, Humanitas University, 20089 Rozzano, Italy
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9
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Nduma BN, Nkeonye S, Uwawah TD, Kaur D, Ekhator C, Ambe S. Use of Artificial Intelligence in the Diagnosis of Colorectal Cancer. Cureus 2024; 16:e53024. [PMID: 38410294 PMCID: PMC10895204 DOI: 10.7759/cureus.53024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2024] [Indexed: 02/28/2024] Open
Abstract
Colorectal cancer (CRC) is one of the most common forms of cancer. Therefore, diagnosing the condition early and accurately is critical for improved patient outcomes and effective treatment. Recently, artificial intelligence (AI) algorithms such as support vector machine (SVM) and convolutional neural network (CNN) have demonstrated promise in medical image analysis. This paper, conducted from a systematic review perspective, aimed to determine the effectiveness of AI integration in CRC diagnosis, emphasizing accuracy, sensitivity, and specificity. From a methodological perspective, articles that were included were those that had been conducted in the past decade. Also, the articles needed to have been documented in English, with databases such as Embase, PubMed, and Google Scholar used to obtain relevant research studies. Similarly, keywords were used to arrive at relevant articles. These keywords included AI, CRC, specificity, sensitivity, accuracy, efficacy, effectiveness, disease diagnosis, screening, machine learning, area under the curve (AUC), and deep learning. From the results, most scholarly studies contend that AI is superior in medical image analysis, the development of subtle patterns, and decision support. However, while deploying these algorithms, a key theme is that the collaboration between medical experts and AI systems needs to be seamless. In addition, the AI algorithms ought to be refined continuously in the current world of big data and ensure that they undergo rigorous validation to provide more informed decision-making for or against adopting those AI tools in clinical settings. In conclusion, therefore, balancing between human expertise and technological innovation is likely to pave the way for the realization of AI's full potential concerning its promising role in improving CRC diagnosis, upon which there might be significant patient outcome improvements, disease detection, and the achievement of a more effective healthcare system.
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Affiliation(s)
| | - Stephen Nkeonye
- Oncology, University of Texas MD Anderson Cancer Center, Houston, USA
| | | | - Davinder Kaur
- Internal Medicine, Medical City, North Richland Hills, USA
| | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology College of Osteopathic Medicine, Old Westbury, USA
| | - Solomon Ambe
- Neurology, Baylor Scott & White Health, McKinney, USA
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Young E, Edwards L, Singh R. The Role of Artificial Intelligence in Colorectal Cancer Screening: Lesion Detection and Lesion Characterization. Cancers (Basel) 2023; 15:5126. [PMID: 37958301 PMCID: PMC10647850 DOI: 10.3390/cancers15215126] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/14/2023] [Accepted: 10/14/2023] [Indexed: 11/15/2023] Open
Abstract
Colorectal cancer remains a leading cause of cancer-related morbidity and mortality worldwide, despite the widespread uptake of population surveillance strategies. This is in part due to the persistent development of 'interval colorectal cancers', where patients develop colorectal cancer despite appropriate surveillance intervals, implying pre-malignant polyps were not resected at a prior colonoscopy. Multiple techniques have been developed to improve the sensitivity and accuracy of lesion detection and characterisation in an effort to improve the efficacy of colorectal cancer screening, thereby reducing the incidence of interval colorectal cancers. This article presents a comprehensive review of the transformative role of artificial intelligence (AI), which has recently emerged as one such solution for improving the quality of screening and surveillance colonoscopy. Firstly, AI-driven algorithms demonstrate remarkable potential in addressing the challenge of overlooked polyps, particularly polyp subtypes infamous for escaping human detection because of their inconspicuous appearance. Secondly, AI empowers gastroenterologists without exhaustive training in advanced mucosal imaging to characterise polyps with accuracy similar to that of expert interventionalists, reducing the dependence on pathologic evaluation and guiding appropriate resection techniques or referrals for more complex resections. AI in colonoscopy holds the potential to advance the detection and characterisation of polyps, addressing current limitations and improving patient outcomes. The integration of AI technologies into routine colonoscopy represents a promising step towards more effective colorectal cancer screening and prevention.
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Affiliation(s)
- Edward Young
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
| | - Louisa Edwards
- Faculty of Health and Medical Sciences, University of Adelaide, Queen Elizabeth Hospital, Port Rd, Woodville South, SA 5011, Australia
| | - Rajvinder Singh
- Faculty of Health and Medical Sciences, University of Adelaide, Lyell McEwin Hospital, Haydown Rd, Elizabeth Vale, SA 5112, Australia
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Stojic V, Zdravkovic N, Nikolic-Turnic T, Zdravkovic N, Dimitrijevic J, Misic A, Jovanovic K, Milojevic S, Zivic J. Using of endoscopic polypectomy in patients with diagnosed malignant colorectal polyp - The cross-sectional clinical study. Open Med (Wars) 2023; 18:20230811. [PMID: 37873541 PMCID: PMC10590616 DOI: 10.1515/med-2023-0811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 09/02/2023] [Accepted: 09/05/2023] [Indexed: 10/25/2023] Open
Abstract
The aim of this study was to evaluate the efficacy of endoscopic polypectomy as a therapeutic treatment for malignant alteration of colorectal polyps. In a 5-year research, 89 patients were included, who were tested and treated at the University Clinical Center Kragujevac, Kragujevac, Serbia, with the confirmed presence of malignant alteration polyps of the colon by colonoscopy, which were removed using the method of endoscopic polypectomy and confirmed by the histopathological examination of the entire polyp. After that, the same group of patients was monitored endoscopically within a certain period, controlling polypectomy locations and the occurrence of a possible remnant of the polyp, in the period of up to 2 years of polypectomy. We observed that, with an increasing size of polyps, there is also an increase in the percentage of the complexity of endoscopic resection and the appearance of remnant with histological characteristics of the invasive cancer. The highest percentage of incomplete endoscopic resection and the appearance of remnant with histological characteristics of the invasive cancer were shown at malignant altered polyps in the field of tubulovillous adenoma. Eighteen patients in total underwent the surgical intervention. In conclusion, our data support the high efficacy of endoscopic polypectomy for the removal of the altered malignant polyp.
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Affiliation(s)
- Vladislava Stojic
- Department of Medical Statistics and Informatics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Natasa Zdravkovic
- Department of Internal Medicine, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Tamara Nikolic-Turnic
- Department of Pharmacy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Nebojsa Zdravkovic
- Department of Medical Statistics and Informatics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Jelena Dimitrijevic
- Department of Medical Statistics and Informatics, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Aleksandra Misic
- Department of Dentistry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Kristijan Jovanovic
- Department of Anatomy, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Stefan Milojevic
- Faculty of Business Economics, EDUCONS University, Sremska Kamenica, Serbia
| | - Jelena Zivic
- Department of Internal Medicine, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
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Bai J, Liu K, Gao L, Zhao X, Zhu S, Han Y, Liu Z. Computer-aided diagnosis in predicting the invasion depth of early colorectal cancer: a systematic review and meta-analysis of diagnostic test accuracy. Surg Endosc 2023; 37:6627-6639. [PMID: 37430125 DOI: 10.1007/s00464-023-10223-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/16/2023] [Indexed: 07/12/2023]
Abstract
BACKGROUND Endoscopic resection (ER) is widely applied to treat early colorectal cancer (CRC). Predicting the invasion depth of early CRC is critical in determining treatment strategies. The use of computer-aided diagnosis (CAD) algorithms could theoretically make accurate and objective predictions regarding the suitability of lesions for ER indication based on invasion depth. This study aimed to assess diagnostic test accuracy of CAD algorithms in predicting the invasion depth of early CRC and to compare the performance between the CAD algorithms and endoscopists. METHODS Multiple databases were searched until June 30, 2022 for studies that evaluated the diagnostic performance of CAD algorithms for invasion depth of CRC. Meta-analysis of diagnostic test accuracy using a bivariate mixed-effects model was performed. RESULTS Ten studies consisting of 13 arms (13,918 images from 1472 lesions) were included. Due to significant heterogeneity, studies were stratified into Japan/Korea-based or China-based studies. For the former, the area under the curve (AUC), sensitivity, and specificity of the CAD algorithms were 0.89 (95% CI 0.86-0.91), 62% (95% CI 50-72%), and 96% (95% CI 93-98%), respectively. For the latter, AUC, sensitivity, and specificity were 0.94 (95% CI 0.92-0.96), 88% (95% CI 78-94%), and 88% (95% CI 80-93%), respectively. The performance of the CAD algorithms in Japan/Korea-based studies was not significantly different from that of all endoscopists (0.88 vs. 0.91, P = 0.10) but was inferior to that of expert endoscopists (0.88 vs. 0.92, P = 0.03). The performance of the CAD algorithms in China-based studies was better than that of all endoscopists (0.94 vs. 0.90, P = 0.01). CONCLUSION The CAD algorithms showed comparable accuracy for prediction of invasion depth of early CRC compared to all endoscopists, which was still lower than expert endoscopists in diagnostic accuracy; more improvements should be achieved before it can be extensively applied to clinical practice.
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Affiliation(s)
- Jiawei Bai
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China
- School of Medicine, Yan'an University, Yan'an, China
| | - Kai Liu
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Li Gao
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Xin Zhao
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Shaohua Zhu
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China
| | - Ying Han
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China.
| | - Zhiguo Liu
- Xijing Hospital of Digestive Diseases, Air Force Medical University (Fourth Military Medical University), 127 Changle West Road, Xi'an, 710032, Shaanxi, China.
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Cho SH, Kim YS. Prediction of Retear After Arthroscopic Rotator Cuff Repair Based on Intraoperative Arthroscopic Images Using Deep Learning. Am J Sports Med 2023; 51:2824-2830. [PMID: 37565449 DOI: 10.1177/03635465231189201] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
BACKGROUND It is challenging to predict retear after arthroscopic rotator cuff repair (ARCR). The usefulness of arthroscopic intraoperative images as predictors of the ARCR prognosis has not been analyzed. PURPOSE To evaluate the usefulness of arthroscopic images for the prediction of retear after ARCR using deep learning (DL) algorithms. STUDY DESIGN Cohort study (Diagnosis); Level of evidence, 2. METHODS In total, 1394 arthroscopic intraoperative images were retrospectively obtained from 580 patients. Repaired tendon integrity was evaluated using magnetic resonance imaging performed within 2 years after surgery. Images obtained immediately after ARCR were included. We used 3 DL architectures to predict retear based on arthroscopic images. Three pretrained DL algorithms (VGG16, DenseNet, and Xception) were used for transfer learning. Training and test sets were split into 8:2. Threefold stratified validation was used to fine-tune the hyperparameters using the training data set. The validation results of each fold were evaluated. The performance of each model in the test set was evaluated in terms of accuracy, area under the receiver operating characteristic curve (AUC), F1-score, sensitivity, and specificity. RESULTS In total, 1138 and 256 arthroscopic images were obtained from 514 patients and 66 patients in the nonretear and retear groups, respectively. The mean validation accuracy of each model was 83% for VGG16, 89% for Xception, and 91% for DenseNet. The accuracy for the test set was 76% for VGG16, 87% for Xception, and 91% for DenseNet. The AUC was highest for DenseNet (0.92); it was 0.83 for VGG16 and 0.91 for Xception. For the test set, the specificity and sensitivity were 0.93 and 0.84 for DenseNet, 0.89 and 0.84 for Xception, and 0.70 and 0.80 for VGG16, respectively. CONCLUSION The application of DL algorithms to intraoperative arthroscopic images has demonstrated a high level of accuracy in predicting retear occurrences.
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Affiliation(s)
- Sung-Hyun Cho
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yang-Soo Kim
- Department of Orthopedic Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
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Chlorogiannis DD, Verras GI, Tzelepi V, Chlorogiannis A, Apostolos A, Kotis K, Anagnostopoulos CN, Antzoulas A, Davakis S, Vailas M, Schizas D, Mulita F. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation? PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:353-367. [PMID: 38572457 PMCID: PMC10985751 DOI: 10.5114/pg.2023.130337] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/20/2023] [Indexed: 04/05/2024]
Abstract
Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.
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Affiliation(s)
| | | | - Vasiliki Tzelepi
- Department of Pathology, School of Medicine, University of Patras, Patras, Greece
| | | | - Anastasios Apostolos
- First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
| | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | - Spyridon Davakis
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Michail Vailas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Dimitrios Schizas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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15
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Shin K, Lee JS, Lee JY, Lee H, Kim J, Byeon JS, Jung HY, Kim DH, Kim N. An Image Turing Test on Realistic Gastroscopy Images Generated by Using the Progressive Growing of Generative Adversarial Networks. J Digit Imaging 2023; 36:1760-1769. [PMID: 36914855 PMCID: PMC10406771 DOI: 10.1007/s10278-023-00803-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023] Open
Abstract
Generative adversarial networks (GAN) in medicine are valuable techniques for augmenting unbalanced rare data, anomaly detection, and avoiding patient privacy issues. However, there were limits to generating high-quality endoscopic images with various characteristics, such as peristalsis, viewpoints, light sources, and mucous patterns. This study used the progressive growing of GAN (PGGAN) within the normal distribution dataset to confirm the ability to generate high-quality gastrointestinal images and investigated what barriers PGGAN has to generate endoscopic images. We trained the PGGAN with 107,060 gastroscopy images from 4165 normal patients to generate highly realistic 5122 pixel-sized images. For the evaluation, visual Turing tests were conducted on 100 real and 100 synthetic images to distinguish the authenticity of images by 19 endoscopists. The endoscopists were divided into three groups based on their years of clinical experience for subgroup analysis. The overall accuracy, sensitivity, and specificity of the 19 endoscopist groups were 61.3%, 70.3%, and 52.4%, respectively. The mean accuracy of the three endoscopist groups was 62.4 [Group I], 59.8 [Group II], and 59.1% [Group III], which was not considered a significant difference. There were no statistically significant differences in the location of the stomach. However, the real images with the anatomical landmark pylorus had higher detection sensitivity. The images generated by PGGAN showed highly realistic depictions that were difficult to distinguish, regardless of their expertise as endoscopists. However, it was necessary to establish GANs that could better represent the rugal folds and mucous membrane texture.
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Affiliation(s)
- Keewon Shin
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea
| | - Jung Su Lee
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
- Seoul Samsung Internal Medicine Clinic, Seoul, Republic of Korea
| | - Ji Young Lee
- Department of Health Screening and Promotion Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Hyunsu Lee
- Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jeongseok Kim
- Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Hwoon-Yong Jung
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Do Hoon Kim
- Department of Gastroenterology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
| | - Namkug Kim
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea.
- Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Medical Center, Seoul, Republic of Korea.
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Rodrigues JA, Correia JH. Photodynamic Therapy for Colorectal Cancer: An Update and a Look to the Future. Int J Mol Sci 2023; 24:12204. [PMID: 37569580 PMCID: PMC10418644 DOI: 10.3390/ijms241512204] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/24/2023] [Accepted: 07/27/2023] [Indexed: 08/13/2023] Open
Abstract
This review provides an update on the current state of photodynamic therapy (PDT) for colorectal cancer (CRC) and explores potential future directions in this field. PDT has emerged as a promising minimally invasive treatment modality that utilizes photosensitizers and specific light wavelengths to induce cell death in targeted tumor tissues. In recent years, significant progress has been made in understanding the underlying mechanisms, optimizing treatment protocols, and improving the efficacy of PDT for CRC. This article highlights key advancements in PDT techniques, including novel photosensitizers, light sources, and delivery methods. Furthermore, it discusses ongoing research efforts and potential future directions, such as combination therapies and nanotechnology-based approaches. By elucidating the current landscape and providing insights into future directions, this review aims to guide researchers and clinicians in harnessing the full potential of PDT for the effective management of CRC.
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Affiliation(s)
- José A. Rodrigues
- CMEMS-UMinho, University of Minho, 4800-058 Guimarães, Portugal;
- LABBELS—Associate Laboratory, 4800-122 Braga, Portugal
| | - José H. Correia
- CMEMS-UMinho, University of Minho, 4800-058 Guimarães, Portugal;
- LABBELS—Associate Laboratory, 4800-122 Braga, Portugal
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Galati JS, Lin K, Gross SA. Recent advances in devices and technologies that might prove revolutionary for colonoscopy procedures. Expert Rev Med Devices 2023; 20:1087-1103. [PMID: 37934873 DOI: 10.1080/17434440.2023.2280773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/09/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is the third most common malignancy and second leading cause of cancer-related mortality in the world. Adenoma detection rate (ADR), a quality indicator for colonoscopy, has gained prominence as it is inversely related to CRC incidence and mortality. As such, recent efforts have focused on developing novel colonoscopy devices and technologies to improve ADR. AREAS COVERED The main objective of this paper is to provide an overview of advancements in the fields of colonoscopy mechanical attachments, artificial intelligence-assisted colonoscopy, and colonoscopy optical enhancements with respect to ADR. We accomplished this by performing a comprehensive search of multiple electronic databases from inception to September 2023. This review is intended to be an introduction to colonoscopy devices and technologies. EXPERT OPINION Numerous mechanical attachments and optical enhancements have been developed that have the potential to improve ADR and AI has gone from being an inaccessible concept to a feasible means for improving ADR. While these advances are exciting and portend a change in what will be considered standard colonoscopy, they continue to require refinement. Future studies should focus on combining modalities to further improve ADR and exploring the use of these technologies in other facets of colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Kevin Lin
- Department of Internal Medicine, NYU Langone Health, New York, NY, USA
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY, USA
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Doğan Y, Bor S. Computer-Based Intelligent Solutions for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes and Chicago Classification 3.0. Healthcare (Basel) 2023; 11:1790. [PMID: 37372907 DOI: 10.3390/healthcare11121790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 05/30/2023] [Accepted: 06/14/2023] [Indexed: 06/29/2023] Open
Abstract
Gastroesophageal reflux disease (GERD) is a multidisciplinary disease; therefore, when treating GERD, a large amount of data needs to be monitored and managed.The aim of our study was to develop a novel automation and decision support system for GERD, primarily to automatically determine GERD and its Chicago Classification 3.0 (CC 3.0) phenotypes. However, phenotyping is prone to errors and is not a strategy widely known by physicians, yet it is very important in patient treatment. In our study, the GERD phenotype algorithm was tested on a dataset with 2052 patients and the CC 3.0 algorithm was tested on a dataset with 133 patients. Based on these two algorithms, a system was developed with an artificial intelligence model for distinguishing four phenotypes per patient. When a physician makes a wrong phenotyping decision, the system warns them and provides the correct phenotype. An accuracy of 100% was obtained for both GERD phenotyping and CC 3.0 in these tests. Finally, since the transition to using this developed system in 2017, the annual number of cured patients, around 400 before, has increased to 800. Automatic phenotyping provides convenience in patient care, diagnosis, and treatment management. Thus, the developed system can substantially improve the performance of physicians.
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Affiliation(s)
- Yunus Doğan
- Department of Computer Engineering, Dokuz Eylül University, Izmir 35390, Türkiye
| | - Serhat Bor
- Department of Gastroenterology, Ege University Faculty of Medicine, Bornova, Izmir 35100, Türkiye
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Pecere S, Ciuffini C, Chiappetta MF, Petruzziello L, Papparella LG, Spada C, Gasbarrini A, Barbaro F. Increasing the accuracy of colorectal cancer screening. Expert Rev Anticancer Ther 2023; 23:583-591. [PMID: 37099725 DOI: 10.1080/14737140.2023.2207828] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
INTRODUCTION Colorectal cancer (CRC) is a major health issue, being responsible for nearly 10% of all cancer-related deaths. Since CRC is often an asymptomatic or paucisymptomatic disease until it reaches advanced stages, screening is crucial for the diagnosis of preneoplastic lesions or early CRC. AREAS COVERED The aim of this review is to summarize the literature evidence on currently available CRC screening tools, with their pros and cons, focusing on the level of accuracy reached by each test over time. We also provide an overview of novel technologies and scientific advances that are currently being investigated and that in the future may represent real game-changers in the field of CRC screening. EXPERT OPINION We suggest that best screening modalities are annual or biennial FIT and colonoscopy every 10 years. We believe that the introduction of artificial intelligence (AI)-tools in the CRC screening field could lead to a significant improvement of the screening efficacy in reducing CRC incidence and mortality in the future. More resources should be put into implementing CRC programmes and support research project to further increase accuracy of CRC screening tests and strategies.
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Affiliation(s)
- Silvia Pecere
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Cristina Ciuffini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Michele Francesco Chiappetta
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Lucio Petruzziello
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Luigi Giovanni Papparella
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Cristiano Spada
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Antonio Gasbarrini
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
| | - Federico Barbaro
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome
- Università Cattolica Del Sacro Cuore di Roma, Rome
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Gimeno-García AZ, Hernández-Pérez A, Nicolás-Pérez D, Hernández-Guerra M. Artificial Intelligence Applied to Colonoscopy: Is It Time to Take a Step Forward? Cancers (Basel) 2023; 15:cancers15082193. [PMID: 37190122 DOI: 10.3390/cancers15082193] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/17/2023] Open
Abstract
Growing evidence indicates that artificial intelligence (AI) applied to medicine is here to stay. In gastroenterology, AI computer vision applications have been stated as a research priority. The two main AI system categories are computer-aided polyp detection (CADe) and computer-assisted diagnosis (CADx). However, other fields of expansion are those related to colonoscopy quality, such as methods to objectively assess colon cleansing during the colonoscopy, as well as devices to automatically predict and improve bowel cleansing before the examination, predict deep submucosal invasion, obtain a reliable measurement of colorectal polyps and accurately locate colorectal lesions in the colon. Although growing evidence indicates that AI systems could improve some of these quality metrics, there are concerns regarding cost-effectiveness, and large and multicentric randomized studies with strong outcomes, such as post-colonoscopy colorectal cancer incidence and mortality, are lacking. The integration of all these tasks into one quality-improvement device could facilitate the incorporation of AI systems in clinical practice. In this manuscript, the current status of the role of AI in colonoscopy is reviewed, as well as its current applications, drawbacks and areas for improvement.
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Affiliation(s)
- Antonio Z Gimeno-García
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Anjara Hernández-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - David Nicolás-Pérez
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
| | - Manuel Hernández-Guerra
- Gastroenterology Department, Hospital Universitario de Canarias, 38200 San Cristóbal de La Laguna, Tenerife, Spain
- Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Tenerife, Spain
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Guerrero Vinsard D, Bruining DH, East JE, Ebner D, Kane SV, Kisiel JB, Leighton JA, Lennon RJ, Loftus EV, Malik T, Picco M, Raffals L, Ramos GP, Santiago P, Coelho-Prabhu N. Interobserver agreement of the modified Paris classification and histology prediction of colorectal lesions in patients with inflammatory bowel disease. Gastrointest Endosc 2023; 97:790-798.e2. [PMID: 36402202 DOI: 10.1016/j.gie.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 10/10/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND AIMS SCENIC (International Consensus Statement on Surveillance and Management of Dysplasia in IBD) guidelines recommend that visible dysplasia in patients with longstanding inflammatory bowel disease (IBD) should be endoscopically characterized using a modified Paris classification. This study aimed to determine the interobserver agreement (IOA) of the modified Paris classification and endoscopists' accuracy for pathology prediction of IBD visible lesions. METHODS One hundred deidentified endoscopic still images and 30 videos of IBD visible colorectal lesions were graded by 10 senior and 4 trainee endoscopists from 5 tertiary care centers. Endoscopists were asked to assign 4 classifications for each image: the standard Paris classification, modified Paris classification, pathology prediction, and lesion border. Agreement was measured using Light's kappa coefficient. Consensus of ratings was assessed according to strict majority. RESULTS The overall Light's kappa for all study endpoints was between .32 and .49. In a subgroup analysis between junior and senior endoscopists, Light's kappa continued to be less than .6 with a slightly higher agreement among juniors. Lesions with the lowest agreement and no consensus were mostly classified as Is, IIa, and mixed Paris classification and sessile and superficial elevated for modified Paris classification. Endoscopist accuracy for prediction of dysplastic, nondysplastic, and serrated pathology was 77%, 56%, and 30%, respectively. There was a strong association (P < .001) between the given morphology classification and the predicted pathology with Ip lesions carrying a much lower expectation of dysplasia than Is/IIc/III and mixed lesions. The agreement for border prediction was .5 for junior and .3 for senior endoscopists. CONCLUSIONS This study demonstrates very low IOA for Paris and modified Paris classifications and low accuracy and IOA for lesion histopathology prediction. Revisions of these classifications are required to create a clinically useful risk stratification tool and enable eventual application of augmented intelligence tools.
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Affiliation(s)
| | - David H Bruining
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - James E East
- Division of Gastroenterology and Hepatology, Mayo Clinic Healthcare, London, UK
| | - Derek Ebner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunanda V Kane
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - John B Kisiel
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Ryan J Lennon
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Edward V Loftus
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Talha Malik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Sheikh Shakhbout Medical City, Abu Dhabi, United Arab Emirates
| | - Michael Picco
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, Florida, USA
| | - Laura Raffals
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Guilherme P Ramos
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Priscila Santiago
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening. Diagnostics (Basel) 2023; 13:diagnostics13061102. [PMID: 36980409 PMCID: PMC10047293 DOI: 10.3390/diagnostics13061102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 02/19/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.
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23
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A Deep-Learning Approach for Identifying and Classifying Digestive Diseases. Symmetry (Basel) 2023. [DOI: 10.3390/sym15020379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
The digestive tract, often known as the gastrointestinal (GI) tract or the gastrointestinal system, is affected by digestive ailments. The stomach, large and small intestines, liver, pancreas and gallbladder are all components of the digestive tract. A digestive disease is any illness that affects the digestive system. Serious to moderate conditions can exist. Heartburn, cancer, irritable bowel syndrome (IBS) and lactose intolerance are only a few of the frequent issues. The digestive system may be treated with many different surgical treatments. Laparoscopy, open surgery and endoscopy are a few examples of these techniques. This paper proposes transfer-learning models with different pre-trained models to identify and classify digestive diseases. The proposed systems showed an increase in metrics, such as the accuracy, precision and recall, when compared with other state-of-the-art methods, and EfficientNetB0 achieved the best performance results of 98.01% accuracy, 98% precision and 98% recall.
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24
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Ali S. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. NPJ Digit Med 2022; 5:184. [PMID: 36539473 PMCID: PMC9767933 DOI: 10.1038/s41746-022-00733-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
Recent developments in deep learning have enabled data-driven algorithms that can reach human-level performance and beyond. The development and deployment of medical image analysis methods have several challenges, including data heterogeneity due to population diversity and different device manufacturers. In addition, more input from experts is required for a reliable method development process. While the exponential growth in clinical imaging data has enabled deep learning to flourish, data heterogeneity, multi-modality, and rare or inconspicuous disease cases still need to be explored. Endoscopy being highly operator-dependent with grim clinical outcomes in some disease cases, reliable and accurate automated system guidance can improve patient care. Most designed methods must be more generalisable to the unseen target data, patient population variability, and variable disease appearances. The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes.
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Affiliation(s)
- Sharib Ali
- School of Computing, University of Leeds, LS2 9JT, Leeds, UK.
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25
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Teramoto A, Hamada S, Ogino B, Yasuda I, Sano Y. Updates in narrow-band imaging for colorectal polyps: Narrow-band imaging generations, detection, diagnosis, and artificial intelligence. Dig Endosc 2022; 35:453-470. [PMID: 36480465 DOI: 10.1111/den.14489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/01/2022] [Indexed: 01/20/2023]
Abstract
Narrow-band imaging (NBI) is an optical digital enhancement method that allows the observation of vascular and surface structures of colorectal lesions. Its usefulness in the detection and diagnosis of colorectal polyps has been demonstrated in several clinical trials and the diagnostic algorithms have been simplified after the establishment of endoscopic classifications such as the Japan NBI Expert Team classification. However, there were issues including lack of brightness in the earlier models, poor visibility under insufficient bowel preparation, and the incompatibility of magnifying endoscopes in certain endoscopic platforms, which had impeded NBI from becoming standardized globally. Nonetheless, NBI continued its evolution and the newest endoscopic platform launched in 2020 offers significantly brighter and detailed images. Enhanced visualization is expected to improve the detection of polyps while universal compatibility across all scopes including magnifying endoscopy will promote the global standardization of magnifying diagnosis. Therefore, knowledge related to magnifying colonoscopy will become essential as magnification becomes standardly equipped in future models, although the advent of computer-aided diagnosis and detection may greatly assist endoscopists to ensure quality of practice. Given that most endoscopic departments will be using both old and new models, it is important to understand how each generation of endoscopic platforms differ from each other. We reviewed the advances in the endoscopic platforms, artificial intelligence, and evidence related to NBI essential for the next generation of endoscopic practice.
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Affiliation(s)
- Akira Teramoto
- Third Department of Internal Medicine, Toyama University Hospital, Toyama, Japan
| | - Seiji Hamada
- Gastrointestinal Center, Urasoe General Hospital, Okinawa, Japan
| | - Banri Ogino
- Third Department of Internal Medicine, Toyama University Hospital, Toyama, Japan
| | - Ichiro Yasuda
- Third Department of Internal Medicine, Toyama University Hospital, Toyama, Japan
| | - Yasushi Sano
- Gastrointestinal Center, Sano Hospital, Hyogo, Japan
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26
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Parkash O, Siddiqui ATS, Jiwani U, Rind F, Padhani ZA, Rizvi A, Hoodbhoy Z, Das JK. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Front Med (Lausanne) 2022; 9:1018937. [PMID: 36405592 PMCID: PMC9672666 DOI: 10.3389/fmed.2022.1018937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/03/2022] [Indexed: 11/06/2022] Open
Abstract
Background Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease. Methods We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360). Findings We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0–94.1) and specificity was 91.7% (95% CI: 87.4–94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias. Interpretation The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].
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Affiliation(s)
- Om Parkash
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Uswa Jiwani
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fahad Rind
- Head and Neck Oncology, The Ohio State University, Columbus, OH, United States
| | - Zahra Ali Padhani
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
| | - Arjumand Rizvi
- Center of Excellence in Women and Child Health, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Jai K. Das
- Institute for Global Health and Development, Aga Khan University, Karachi, Pakistan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
- *Correspondence: Jai K. Das,
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27
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Chen H, Zhang J, Zhou H, Zhu Y, Liang Y, Zhu P, Zhang Q. UHPLC-HRMS–based serum lipisdomics reveals novel biomarkers to assist in the discrimination between colorectal adenoma and cancer. Front Oncol 2022; 12:934145. [PMID: 35965551 PMCID: PMC9366052 DOI: 10.3389/fonc.2022.934145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/30/2022] [Indexed: 11/13/2022] Open
Abstract
The development of a colorectal adenoma (CA) into carcinoma (CRC) is a long and stealthy process. There remains a lack of reliable biomarkers to distinguish CA from CRC. To effectively explore underlying molecular mechanisms and identify novel lipid biomarkers promising for early diagnosis of CRC, an ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS) method was employed to comprehensively measure lipid species in human serum samples of patients with CA and CRC. Results showed significant differences in serum lipid profiles between CA and CRC groups, and 85 differential lipid species (P < 0.05 and fold change > 1.50 or < 0.67) were discovered. These significantly altered lipid species were mainly involved in fatty acid (FA), phosphatidylcholine (PC), and triacylglycerol (TAG) metabolism with the constituent ratio > 63.50%. After performance evaluation by the receiver operating characteristic (ROC) curve analysis, seven lipid species were ultimately proposed as potential biomarkers with the area under the curve (AUC) > 0.800. Of particular value, a lipid panel containing docosanamide, SM d36:0, PC 36:1e, and triheptanoin was selected as a composite candidate biomarker with excellent performance (AUC = 0.971), and the highest selected frequency to distinguish patients with CA from patients with CRC based on the support vector machine (SVM) classification model. To our knowledge, this study was the first to undertake a lipidomics profile using serum intended to identify screening lipid biomarkers to discriminate between CA and CRC. The lipid panel could potentially serve as a composite biomarker aiding the early diagnosis of CRC. Metabolic dysregulation of FAs, PCs, and TAGs seems likely involved in malignant transformation of CA, which hopefully will provide new clues to understand its underlying mechanism.
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Affiliation(s)
- Hongwei Chen
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Jiahao Zhang
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Hailin Zhou
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Yifan Zhu
- Medical College of Guangxi University, Guangxi University, Nanning, China
| | - Yunxiao Liang
- Department of Gastroenterology, People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Pingchuan Zhu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, China
| | - Qisong Zhang
- Medical College of Guangxi University, Guangxi University, Nanning, China
- *Correspondence: Qisong Zhang,
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28
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Braoudaki M, Ahmad MS, Mustafov D, Seriah S, Siddiqui MN, Siddiqui SS. Chemokines and chemokine receptors in colorectal cancer; multifarious roles and clinical impact. Semin Cancer Biol 2022; 86:436-449. [PMID: 35700938 DOI: 10.1016/j.semcancer.2022.06.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 11/19/2022]
Abstract
Colorectal cancer (CRC) is considered the second cause of cancer death worldwide. The early diagnosis plays a key role in patient prognosis and subsequently overall survival. Similar to several types of cancer, colorectal cancer is also characterised by drug resistance and heterogeneity that contribute to its complexity -especially at advanced stages. However, despite the extensive research related to the identification of biomarkers associated to early diagnosis, accurate prognosis and the management of CRC patients, little progress has been made thus far. Therefore, the mortality rates, especially at advanced stages, remain high. A large family of chemoattractant cytokines called chemokines are known for their significant role in inflammation and immunity. Chemokines released by the different tumorous cells play a key role in increasing the complexity of the tumour's microenvironment. The current review investigates the role of chemokines and chemokine receptors in colorectal cancer and their potential as clinical molecular signatures that could be effectively used as a personalised therapeutic approach. We discussed how chemokine and chemokine receptors regulate the microenvironment and lead to heterogeneity in CRC. An important aspect of chemokines is their role in drug resistance which has been extensively discussed. This review also provides an overview of the current advances in the search for chemokines and chemokine receptors in CRC.
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Affiliation(s)
- Maria Braoudaki
- Dept of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, UK
| | - Mohammed Saqif Ahmad
- Dept of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, UK
| | - Denis Mustafov
- Dept of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, UK
| | - Sara Seriah
- Dept of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, UK
| | - Mohammad Naseem Siddiqui
- Department of Biosciences, Faculty of Natural Sciences, Jamia Millia Islamia, Jamia Nagar, New Delhi 110025, India
| | - Shoib Sarwar Siddiqui
- Dept of Clinical, Pharmaceutical and Biological Sciences, School of Life and Medical Sciences, University of Hertfordshire, UK.
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29
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Fati SM, Senan EM, Azar AT. Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases. SENSORS (BASEL, SWITZERLAND) 2022; 22:4079. [PMID: 35684696 PMCID: PMC9185306 DOI: 10.3390/s22114079] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/21/2022] [Accepted: 05/24/2022] [Indexed: 05/27/2023]
Abstract
Every year, nearly two million people die as a result of gastrointestinal (GI) disorders. Lower gastrointestinal tract tumors are one of the leading causes of death worldwide. Thus, early detection of the type of tumor is of great importance in the survival of patients. Additionally, removing benign tumors in their early stages has more risks than benefits. Video endoscopy technology is essential for imaging the GI tract and identifying disorders such as bleeding, ulcers, polyps, and malignant tumors. Videography generates 5000 frames, which require extensive analysis and take a long time to follow all frames. Thus, artificial intelligence techniques, which have a higher ability to diagnose and assist physicians in making accurate diagnostic decisions, solve these challenges. In this study, many multi-methodologies were developed, where the work was divided into four proposed systems; each system has more than one diagnostic method. The first proposed system utilizes artificial neural networks (ANN) and feed-forward neural networks (FFNN) algorithms based on extracting hybrid features by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and fuzzy color histogram (FCH) algorithms. The second proposed system uses pre-trained CNN models which are the GoogLeNet and AlexNet based on the extraction of deep feature maps and their classification with high accuracy. The third proposed method uses hybrid techniques consisting of two blocks: the first block of CNN models (GoogLeNet and AlexNet) to extract feature maps; the second block is the support vector machine (SVM) algorithm for classifying deep feature maps. The fourth proposed system uses ANN and FFNN based on the hybrid features between CNN models (GoogLeNet and AlexNet) and LBP, GLCM and FCH algorithms. All the proposed systems achieved superior results in diagnosing endoscopic images for the early detection of lower gastrointestinal diseases. All systems produced promising results; the FFNN classifier based on the hybrid features extracted by GoogLeNet, LBP, GLCM and FCH achieved an accuracy of 99.3%, precision of 99.2%, sensitivity of 99%, specificity of 100%, and AUC of 99.87%.
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Affiliation(s)
- Suliman Mohamed Fati
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
| | - Ebrahim Mohammed Senan
- Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India;
| | - Ahmad Taher Azar
- College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia;
- Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
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30
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Caires Silveira E, Santos Corrêa CF, Madureira Silva L, Almeida Santos B, Mattos Pretti S, Freire de Melo F. Recognition of esophagitis in endoscopic images using transfer learning. World J Gastrointest Endosc 2022; 14:311-319. [PMID: 35719896 PMCID: PMC9157692 DOI: 10.4253/wjge.v14.i5.311] [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: 05/13/2021] [Revised: 07/15/2021] [Accepted: 04/26/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Esophagitis is an inflammatory and damaging process of the esophageal mucosa, which is confirmed by endoscopic visualization and may, in extreme cases, result in stenosis, fistulization and esophageal perforation. The use of deep learning (a field of artificial intelligence) techniques can be considered to determine the presence of esophageal lesions compatible with esophagitis.
AIM To develop, using transfer learning, a deep neural network model to recognize the presence of esophagitis in endoscopic images.
METHODS Endoscopic images of 1932 patients with a diagnosis of esophagitis and 1663 patients without any pathological diagnosis provenient from the KSAVIR and HyperKSAVIR datasets were splitted in training (80%) and test (20%) and used to develop and evaluate a binary deep learning classifier built using the DenseNet-201 architecture, a densely connected convolutional network, with weights pretrained on the ImageNet image set and fine-tuned during training. The classifier model performance was evaluated in the test set according to accuracy, sensitivity, specificity and area under the receiver operating characteristic curve (AUC).
RESULTS The model was trained using Adam optimizer with a learning rate of 0.0001 and applying binary cross entropy loss function. In the test set (n = 719), the classifier achieved 93.32% accuracy, 93.18% sensitivity, 93.46% specificity and a 0.96 AUC. Heatmaps for spatial predictive relevance in esophagitis endoscopic images from the test set were also plotted. In face of the obtained results, the use of dense convolutional neural networks with pretrained and fine-tuned weights proves to be a good strategy for predictive modeling for esophagitis recognition in endoscopic images. In addition, adopting the classification approach combined with the subsequent plotting of heat maps associated with the classificatory decision gives greater explainability to the model.
CONCLUSION It is opportune to raise new studies involving transfer learning for the analysis of endoscopic images, aiming to improve, validate and disseminate its use for clinical practice.
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Affiliation(s)
- Elena Caires Silveira
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
| | - Caio Fellipe Santos Corrêa
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
| | - Leonardo Madureira Silva
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
| | - Bruna Almeida Santos
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
| | - Soraya Mattos Pretti
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
| | - Fabrício Freire de Melo
- Multidisciplinary Institute of Health, Federal University of Bahia, Vitória da Conquista 45029-094, Bahia, Brazil
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31
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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32
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Vulpoi RA, Luca M, Ciobanu A, Olteanu A, Barboi OB, Drug VL. Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going? Diagnostics (Basel) 2022; 12:diagnostics12040927. [PMID: 35453975 PMCID: PMC9029251 DOI: 10.3390/diagnostics12040927] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/30/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
Artificial intelligence, a computer-based concept that tries to mimic human thinking, is slowly becoming part of the endoscopy lab. It has developed considerably since the first attempt at developing an automated medical diagnostic tool, today being adopted in almost all medical fields, digestive endoscopy included. The detection rate of preneoplastic lesions (i.e., polyps) during colonoscopy may be increased with artificial intelligence assistance. It has also proven useful in detecting signs of ulcerative colitis activity. In upper digestive endoscopy, deep learning models may prove to be useful in the diagnosis and management of upper digestive tract diseases, such as gastroesophageal reflux disease, Barrett’s esophagus, and gastric cancer. As is the case with all new medical devices, there are challenges in the implementation in daily medical practice. The regulatory, economic, organizational culture, and language barriers between humans and machines are a few of them. Even so, many devices have been approved for use by their respective regulators. Future studies are currently striving to develop deep learning models that can replicate a growing amount of human brain activity. In conclusion, artificial intelligence may become an indispensable tool in digestive endoscopy.
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Affiliation(s)
- Radu-Alexandru Vulpoi
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
| | - Mihaela Luca
- Institute of Computer Science, Romanian Academy—Iași Branch, 700481 Iași, Romania; (M.L.); (A.C.)
| | - Adrian Ciobanu
- Institute of Computer Science, Romanian Academy—Iași Branch, 700481 Iași, Romania; (M.L.); (A.C.)
| | - Andrei Olteanu
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
| | - Oana-Bogdana Barboi
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
- Correspondence: ; Tel.: +40-74-345-5012
| | - Vasile Liviu Drug
- Institute of Gastroenterology and Hepatology, Saint Spiridon Hospital, “Grigore T. Popa” University of Medicine and Pharmacy, 700111 Iași, Romania; (R.-A.V.); (A.O.); (V.L.D.)
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Alrumaihi F, Khan MA, Babiker AY, Alsaweed M, Azam F, Allemailem KS, Almatroudi AA, Ahamad SR, Alsugoor MH, Alharbi KN, Almansour NM, Khan A. Lipid-Based Nanoparticle Formulation of Diallyl Trisulfide Chemosensitizes the Growth Inhibitory Activity of Doxorubicin in Colorectal Cancer Model: A Novel In Vitro, In Vivo and In Silico Analysis. Molecules 2022; 27:molecules27072192. [PMID: 35408590 PMCID: PMC9000458 DOI: 10.3390/molecules27072192] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 03/19/2022] [Accepted: 03/25/2022] [Indexed: 02/07/2023] Open
Abstract
Garlic’s main bioactive organosulfur component, diallyl trisulfide (DATS), has been widely investigated in cancer models. However, DATS is not suitable for clinical use due to its low solubility. The current study seeks to improve DATS bioavailability and assess its chemopreventive and chemosensitizing properties in an AOM-induced colorectal cancer model. The polyethylene glycol coated Distearoylphosphatidylcholine/Cholesterol (DSPC/Chol) comprising DATS-loaded DATSL and doxorubicin (DOXO)-encapsulated DOXL liposomes was prepared and characterized. The changes in the sensitivity of DATS and DOXO by DATSL and DOXL were evaluated in RKO and HT-29 colon cancer cells. The synergistic effect of DATSL and DOXL was studied by cell proliferation assay in the combinations of IC10, IC25, and IC35 of DATSL with the IC10 of DOXL. AOM, DATSL, and DOXL were administered to different groups of mice for a period of 21 weeks. The data exhibited ~93% and ~46% entrapment efficiency of DATSL and DOXL, respectively. The size of sham liposomes was 110.5 nm, whereas DATSL and DOXL were 135.5 nm and 169 nm, respectively. DATSL and DOXL exhibited significant sensitivity in the cell proliferation experiment, lowering their IC50 doses by more than 8- and 14-fold, respectively. However, the DATSL IC10, IC25, and IC35 showed escalating chemosensitivity, and treated the cells in combination with DOXL IC10. Analysis of histopathological, cancer marker enzymes, and antioxidant enzymes revealed that the high dose of DATSL pretreatment and DOXL chemotherapy is highly effective in inhibiting AOM-induced colon cancer promotion. The combination of DATSL and DOXL indicated promise as a colorectal cancer treatment in this study. Intermolecular interactions of DATS and DOXO against numerous cancer targets by molecular docking indicated MMP-9 as the most favourable target for DATS exhibiting binding energy of −4.6 kcal/mol. So far, this is the first research to demonstrate the chemopreventive as well as chemosensitizing potential of DATSL in an animal model of colorectal cancer.
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Affiliation(s)
- Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (A.Y.B.); (K.S.A.); (A.A.A.); (K.N.A.)
| | - Masood Alam Khan
- Department of Basic Health Sciences, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia;
| | - Ali Yousif Babiker
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (A.Y.B.); (K.S.A.); (A.A.A.); (K.N.A.)
| | - Mohammed Alsaweed
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia;
| | - Faizul Azam
- Department of Pharmaceutical Chemistry and Pharmacognosy, Unaizah College of Pharmacy, Qassim University, Unaizah 51911, Saudi Arabia;
| | - Khaled S. Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (A.Y.B.); (K.S.A.); (A.A.A.); (K.N.A.)
| | - Ahmad A. Almatroudi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (A.Y.B.); (K.S.A.); (A.A.A.); (K.N.A.)
| | - Syed Rizwan Ahamad
- Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia;
| | - Mahdi H. Alsugoor
- Department of Emergency Medical Services, Faculty of Health Sciences, AlQunfudah, Umm Al-Qura University, Makkah 21912, Saudi Arabia;
| | - Khloud Nawaf Alharbi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia; (F.A.); (A.Y.B.); (K.S.A.); (A.A.A.); (K.N.A.)
| | - Nahlah Makki Almansour
- Department of Biology, College of Science, University of Hafr Al Batin, Hafr Al Batin 31991, Saudi Arabia;
| | - Arif Khan
- Department of Basic Health Sciences, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia;
- Correspondence: ; Tel.: +966-590038460; Fax: +966-63801628
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Li JW, Wang LM, Ang TL. Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications. Singapore Med J 2022; 63:118-124. [PMID: 35509251 PMCID: PMC9251247 DOI: 10.11622/smedj.2022044] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
| | - Lai Mun Wang
- Pathology Section, Department of Laboratory Medicine, Changi General Hospital, Singapore
- SingHealth Duke-NUS Pathology Academic Clinical Programme, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
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The Effect of Liposomal Diallyl Disulfide and Oxaliplatin on Proliferation of Colorectal Cancer Cells: In Vitro and In Silico Analysis. Pharmaceutics 2022; 14:pharmaceutics14020236. [PMID: 35213970 PMCID: PMC8877238 DOI: 10.3390/pharmaceutics14020236] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/24/2021] [Accepted: 01/13/2022] [Indexed: 01/27/2023] Open
Abstract
Diallyl disulfide (DADS) is one of the main bioactive organosulfur compounds of garlic, and its potential against various cancer models has been demonstrated. The poor solubility of DADS in aqueous solutions limits its uses in clinical application. The present study aimed to develop a novel formulation of DADS to increase its bioavailability and therapeutic potential and evaluate its role in combination with oxaliplatin (OXA) in the colorectal cancer system. We prepared and characterized PEGylated, DADS (DCPDD), and OXA (DCPDO) liposomes. The anticancer potential of these formulations was then evaluated in HCT116 and RKO colon cancer cells by different cellular assays. Further, a molecular docking-based computational analysis was conducted to determine the probable binding interactions of DADS and OXA. The results revealed the size of the DCPDD and DCPDO to be 114.46 nm (95% EE) and 149.45 nm (54% EE), respectively. They increased the sensitivity of the cells and reduced the IC50 several folds, while the combinations of them showed a synergistic effect and induced apoptosis by 55% in the cells. The molecular docking data projected several possible targets of DADS and OXA that could be evaluated more precisely by these novel formulations in detail. This study will direct the usage of DCPDD to augment the therapeutic potential of DCPDO against colon cancer in clinical settings.
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Zhuang H, Bao A, Tan Y, Wang H, Xie Q, Qiu M, Xiong W, Liao F. Application and prospect of artificial intelligence in digestive endoscopy. Expert Rev Gastroenterol Hepatol 2022; 16:21-31. [PMID: 34937459 DOI: 10.1080/17474124.2022.2020646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION With the progress of science and technology, artificial intelligence represented by deep learning has gradually begun to be applied in the medical field. Artificial intelligence has been applied to benign gastrointestinal lesions, tumors, early cancer, inflammatory bowel disease, gallbladder, pancreas, and other diseases. This review summarizes the latest research results on artificial intelligence in digestive endoscopy and discusses the prospect of artificial intelligence in digestive system diseases. AREAS COVERED We retrieved relevant documents on artificial intelligence in digestive tract diseases from PubMed and Medline. This review elaborates on the knowledge of computer-aided diagnosis in digestive endoscopy. EXPERT OPINION Artificial intelligence significantly improves diagnostic accuracy, reduces physicians' workload, and provides a shred of evidence for clinical diagnosis and treatment. Shortly, artificial intelligence will have high application value in the field of medicine.
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Affiliation(s)
- Huangming Zhuang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Anyu Bao
- Clinical Laboratory, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yulin Tan
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hanyu Wang
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qingfang Xie
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Meiqi Qiu
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Wanli Xiong
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Fei Liao
- Gastroenterology Department, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021; 27:8103-8122. [PMID: 35068857 PMCID: PMC8704267 DOI: 10.3748/wjg.v27.i47.8103] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/22/2021] [Accepted: 12/08/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.
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Affiliation(s)
- Mahsa Taghiakbari
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo 0450, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama 224-8503, Japan
| | - Daniel von Renteln
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
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Zhu Y, Wang L, Nong Y, Liang Y, Huang Z, Zhu P, Zhang Q. Serum Untargeted UHPLC-HRMS-Based Lipidomics to Discover the Potential Biomarker of Colorectal Advanced Adenoma. Cancer Manag Res 2021; 13:8865-8878. [PMID: 34858060 PMCID: PMC8632617 DOI: 10.2147/cmar.s336322] [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: 08/27/2021] [Accepted: 10/29/2021] [Indexed: 12/12/2022] Open
Abstract
Background As a key precancerous lesion, colorectal advanced adenoma (CAA) is closely related to the occurrence and development of colorectal cancer (CRC). Effective identification of CAA-related biomarkers can prevent CRC morbidity and mortality. Lipids, as an important endogenous substance, have been proved to be involved in the occurrence and development of CRC. Lipidomics is an advanced technique that studies lipid metabolism and biomarkers of diseases. However, there are no lipidomics studies based on large serum samples to explore diagnostic biomarkers for CAA. Methods An integrated serum lipid profile from 50 normal (NR) and 46 CAA subjects was performed using ultra-high performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS). Lipidomic data were acquired for negative and positive ionization modes, respectively. Differential lipids were selected by univariate and multivariate statistics analyses. A receiver operator characteristic curve (ROC) analysis was conducted to evaluate the diagnostic performance of differential lipids. Results A total of 53 differential lipids were obtained by combining univariate and multivariate statistical analyses (P < 0.05 and VIP > 1). In addition, 12 differential lipids showed good diagnostic performance (AUC > 0.90) for the discrimination of NR and CAA by receiver operating characteristic curve (ROC) analysis. Of them, the performance of PC 44:5 and PC 35:6e presented the outstanding performance (AUC = 1.00, (95% CI, 1.00–1.00)). Moreover, triglyceride (TAG) had the highest proportion (37.74%) as the major dysregulated lipids in the CAA. Conclusion This is the first study that profiled serum lipidomics and explored lipid biomarkers with good diagnostic ability of CAA to contribute to the early prevention of CRC. Twelve differential lipids that effectively discriminate between NR and CAA serve as the potential diagnostic markers of CAA. An obvious perturbation of TAG metabolism could be involved in the CAA formation.
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Affiliation(s)
- Yifan Zhu
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
| | - Lisheng Wang
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
| | - Yanying Nong
- Department of Gastroenterology, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, 530011, People's Republic of China
| | - Yunxiao Liang
- Department of Gastroenterology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, 530021, People's Republic of China
| | - Zongsheng Huang
- Department of Gastroenterology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, 530021, People's Republic of China
| | - Pingchuan Zhu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
| | - Qisong Zhang
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, 530004, People's Republic of China
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van der Zander QEW, Schreuder RM, Fonollà R, Scheeve T, van der Sommen F, Winkens B, Aepli P, Hayee B, Pischel AB, Stefanovic M, Subramaniam S, Bhandari P, de With PHN, Masclee AAM, Schoon EJ. Optical diagnosis of colorectal polyp images using a newly developed computer-aided diagnosis system (CADx) compared with intuitive optical diagnosis. Endoscopy 2021; 53:1219-1226. [PMID: 33368056 DOI: 10.1055/a-1343-1597] [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] [Indexed: 12/12/2022]
Abstract
BACKGROUND Optical diagnosis of colorectal polyps remains challenging. Image-enhancement techniques such as narrow-band imaging and blue-light imaging (BLI) can improve optical diagnosis. We developed and prospectively validated a computer-aided diagnosis system (CADx) using high-definition white-light (HDWL) and BLI images, and compared the system with the optical diagnosis of expert and novice endoscopists. METHODS CADx characterized colorectal polyps by exploiting artificial neural networks. Six experts and 13 novices optically diagnosed 60 colorectal polyps based on intuition. After 4 weeks, the same set of images was permuted and optically diagnosed using the BLI Adenoma Serrated International Classification (BASIC). RESULTS CADx had a diagnostic accuracy of 88.3 % using HDWL images and 86.7 % using BLI images. The overall diagnostic accuracy combining HDWL and BLI (multimodal imaging) was 95.0 %, which was significantly higher than that of experts (81.7 %, P = 0.03) and novices (66.7 %, P < 0.001). Sensitivity was also higher for CADx (95.6 % vs. 61.1 % and 55.4 %), whereas specificity was higher for experts compared with CADx and novices (95.6 % vs. 93.3 % and 93.2 %). For endoscopists, diagnostic accuracy did not increase when using BASIC, either for experts (intuition 79.5 % vs. BASIC 81.7 %, P = 0.14) or for novices (intuition 66.7 % vs. BASIC 66.5 %, P = 0.95). CONCLUSION CADx had a significantly higher diagnostic accuracy than experts and novices for the optical diagnosis of colorectal polyps. Multimodal imaging, incorporating both HDWL and BLI, improved the diagnostic accuracy of CADx. BASIC did not increase the diagnostic accuracy of endoscopists compared with intuitive optical diagnosis.
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Affiliation(s)
- Quirine E W van der Zander
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center + Maastricht, the Netherlands.,GROW, School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Ramon M Schreuder
- Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands
| | - Roger Fonollà
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Thom Scheeve
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Bjorn Winkens
- Department of Methodology and Statistics, CAPHRI, Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands
| | - Patrick Aepli
- Division of Gastroenterology and Hepatology, Luzerner Kantonsspital, Lucerne, Switzerland
| | - Bu'Hussain Hayee
- Division of Gastroenterology and Hepatology, King's College Hospital, London, United Kingdom
| | - Andreas B Pischel
- Division of Gastroenterology and Hepatology, University Hospital Gothenburg, Gothenburg, Sweden
| | - Milan Stefanovic
- Division of Gastroenterology and Hepatology, Diagnostični Center Bled, Ljubljana, Slovenia
| | - Sharmila Subramaniam
- Division of Gastroenterology and Hepatology, Queen Alexandra Hospital, Portsmouth, United Kingdom
| | - Pradeep Bhandari
- Division of Gastroenterology and Hepatology, Queen Alexandra Hospital, Portsmouth, United Kingdom
| | - Peter H N de With
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Ad A M Masclee
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center + Maastricht, the Netherlands
| | - Erik J Schoon
- GROW, School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands.,Division of Gastroenterology and Hepatology, Catharina Hospital Eindhoven, Eindhoven, the Netherlands
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Zhu XW, Yan J, He YL, Liu G, Li X. Application of deep learning based artificial intelligence technology in identification of colorectal polyps. Shijie Huaren Xiaohua Zazhi 2021; 29:1201-1206. [DOI: 10.11569/wcjd.v29.i20.1201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is a cancer type that is most suitable for screening since subjects at risk of this malignancy can clearly benefit from colonoscopy screening. In 2017, there were about 431951 new cases of colorectal cancer in China, with an increase of 203.5% in 28 years. Early detection and early removal of adenomatous polyps and other precancerous lesions during colonoscopy can prevent the occurrence of colorectal cancer. However, various factors lead to missed diagnosis of polyps during colonoscopy, which increases the risk of colorectal cancer. In recent years, with the rapid development of artificial intelligence technology in the medical field, colonoscopy assisted by artificial intelligence can increase the detection rate of polyps and improve the quality of colonoscopy. This paper mainly reviews the quality control, bowel preparation, diagnosis and classification of colorectal polyps, and the future opportunities and challenges faced by convolutional neural network based artificial intelligence technology in the field of colonoscopy, hoping to provide some reference for clinical work.
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Affiliation(s)
- Xing-Wang Zhu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun Yan
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Ying-Li He
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Gang Liu
- Lanzhou University School of Information Science & Engineering, Lanzhou 730000, Gansu Province, China
| | - Xun Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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El-Nakeep S, El-Nakeep M. Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations. Artif Intell Gastroenterol 2021; 2:124-132. [DOI: 10.35712/aig.v2.i5.124] [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: 06/06/2021] [Revised: 06/26/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
Abstract
This minireview discusses the benefits and pitfalls of machine learning, and artificial intelligence in upper gastrointestinal endoscopy for the detection and characterization of neoplasms. We have reviewed the literature for relevant publications on the topic using PubMed, IEEE, Science Direct, and Google Scholar databases. We discussed the phases of machine learning and the importance of advanced imaging techniques in upper gastrointestinal endoscopy and its association with artificial intelligence.
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Affiliation(s)
- Sarah El-Nakeep
- Gastroenterology and Hepatology Unit, Internal Medicine Department, Faculty of Medicine, AinShams University, Cairo 11591, Egypt
| | - Mohamed El-Nakeep
- Master of Science in Electrical Engineering "Electronics and Communications", Electronics and Electrical Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11736, Egypt
- Bachelor of Science in Electronics and Electrical Communications, Electronics and Communications and Computers Department, Faculty of Engineering, Helwan University, Cairo 11736, Egypt
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Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021; 27:5908-5918. [PMID: 34629808 PMCID: PMC8475008 DOI: 10.3748/wjg.v27.i35.5908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/29/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a “resect and discard“ or ”leave in“ strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.
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Affiliation(s)
- Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
| | - Andreas V Hadjinicolaou
- MRC Cancer Unit, Department of Gastroenterology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fanourios Georgiades
- Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Department of Computer Science, University College London, London W1W 7TY, United Kingdom
| | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
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Correia FP, Lourenço LC. Artificial intelligence application in diagnostic gastrointestinal endoscopy - Deus ex machina? World J Gastroenterol 2021; 27:5351-5361. [PMID: 34539137 PMCID: PMC8409168 DOI: 10.3748/wjg.v27.i32.5351] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/15/2021] [Accepted: 07/19/2021] [Indexed: 02/06/2023] Open
Abstract
The close relationship of medicine with technology and the particular interest in this symbiosis in recent years has led to the development of several computed artificial intelligence (AI) systems aimed at various areas of medicine. A number of studies have demonstrated that those systems allow accurate diagnoses with histological precision, thus facilitating decision-making by clinicians in real time. In the field of gastroenterology, AI has been applied in the diagnosis of pathologies of the entire digestive tract and their attached glands, and are increasingly accepted for the detection of colorectal polyps and confirming their histological classification. Studies have shown high accuracy, sensitivity, and specificity in relation to expert endoscopists, and mainly in relation to those with less experience. Other applications that are increasingly studied and with very promising results are the investigation of dysplasia in patients with Barrett's esophagus and the endoscopic and histological assessment of colon inflammation in patients with ulcerative colitis. In some cases AI is thus better than or at least equal to human abilities. However, additional studies are needed to reinforce the existing data, and mainly to determine the applicability of this technology in other indications. This review summarizes the state of the art of AI in gastroenterological pathology.
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Affiliation(s)
- Fábio Pereira Correia
- Department of Gastroenterology, Hospital Prof. Dr Fernando Fonseca, Lisbon 2720-276, Portugal
| | - Luís Carvalho Lourenço
- Department of Gastroenterology, Hospital Prof. Dr Fernando Fonseca, Lisbon 2720-276, Portugal
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44
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Joseph J, LePage EM, Cheney CP, Pawa R. Artificial intelligence in colonoscopy. World J Gastroenterol 2021; 27:4802-4817. [PMID: 34447227 PMCID: PMC8371500 DOI: 10.3748/wjg.v27.i29.4802] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/12/2021] [Accepted: 07/16/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer remains a leading cause of morbidity and mortality in the United States. Advances in artificial intelligence (AI), specifically computer aided detection and computer-aided diagnosis offer promising methods of increasing adenoma detection rates with the goal of removing more pre-cancerous polyps. Conversely, these methods also may allow for smaller non-cancerous lesions to be diagnosed in vivo and left in place, decreasing the risks that come with unnecessary polypectomies. This review will provide an overview of current advances in the use of AI in colonoscopy to aid in polyp detection and characterization as well as areas of developing research.
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Affiliation(s)
- Joel Joseph
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
| | - Ella Marie LePage
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston Salem, NC 27157, United States
| | - Catherine Phillips Cheney
- Department of Internal Medicine, Wake Forest School of Medicine, Winston Salem, NC 27157, United States
| | - Rishi Pawa
- Department of Internal Medicine, Section of Gastroenterology and Hepatology, Wake Forest Baptist Medical Center, Winston-Salem, NC 27157, United States
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45
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Klein C, Zeng Q, Arbaretaz F, Devêvre E, Calderaro J, Lomenie N, Maiuri MC. Artificial Intelligence for solid tumor diagnosis in digital pathology. Br J Pharmacol 2021; 178:4291-4315. [PMID: 34302297 DOI: 10.1111/bph.15633] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 11/30/2022] Open
Abstract
Tumor diagnosis relies on the visual examination of histological slides by pathologists through a microscope eyepiece. Digital pathology, the digitalization of histological slides at high magnification with slides scanners, has raised the opportunity to extract quantitative information thanks to image analysis. In the last decade, medical image analysis has made exceptional progress due to the development of artificial intelligence (AI) algorithms. AI has been successfully used in the field of medical imaging and more recently in digital pathology. The feasibility and usefulness of AI assisted pathology tasks have been demonstrated in the very last years and we can expect those developments to be applied on routine histopathology in the future. In this review, we will describe and illustrate this technique and present the most recent applications in the field of tumor histopathology.
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Affiliation(s)
- Christophe Klein
- Centre de recherche des Cordeliers, Centre d'Imagerie, Histologie et Cytométrie (CHIC), INSERM, Sorbonne Université, Université de Paris, Paris, France
| | - Qinghe Zeng
- Centre de recherche des Cordeliers, Centre d'Imagerie, Histologie et Cytométrie (CHIC), INSERM, Sorbonne Université, Université de Paris, Paris, France.,Laboratoire d'informatique Paris Descartes (LIPADE), Université de Paris, Paris, France
| | - Floriane Arbaretaz
- Centre de recherche des Cordeliers, Centre d'Imagerie, Histologie et Cytométrie (CHIC), INSERM, Sorbonne Université, Université de Paris, Paris, France
| | - Estelle Devêvre
- Centre de recherche des Cordeliers, Centre d'Imagerie, Histologie et Cytométrie (CHIC), INSERM, Sorbonne Université, Université de Paris, Paris, France
| | - Julien Calderaro
- Département de pathologie, Hôpital Henri Mondor, Créteil, France
| | - Nicolas Lomenie
- Laboratoire d'informatique Paris Descartes (LIPADE), Université de Paris, Paris, France
| | - Maria Chiara Maiuri
- Centre de recherche des Cordeliers, Centre d'Imagerie, Histologie et Cytométrie (CHIC), INSERM, Sorbonne Université, Université de Paris, Paris, France
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Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc 2021; 14:26317745211014698. [PMID: 34263163 PMCID: PMC8252334 DOI: 10.1177/26317745211014698] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/07/2021] [Indexed: 12/27/2022] Open
Abstract
Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Missouri, Columbia, MO 65211, USA
| | - Michael F Byrne
- Division of Gastroenterology, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada; Satisfai Health, Vancouver, BC, Canada
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Kim JH, Nam SJ, Park SC. Usefulness of artificial intelligence in gastric neoplasms. World J Gastroenterol 2021; 27:3543-3555. [PMID: 34239268 PMCID: PMC8240061 DOI: 10.3748/wjg.v27.i24.3543] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 04/09/2021] [Accepted: 05/21/2021] [Indexed: 02/06/2023] Open
Abstract
Recently, studies in many medical fields have reported that image analysis based on artificial intelligence (AI) can be used to analyze structures or features that are difficult to identify with human eyes. To diagnose early gastric cancer, related efforts such as narrow-band imaging technology are on-going. However, diagnosis is often difficult. Therefore, a diagnostic method based on AI for endoscopic imaging was developed and its effectiveness was confirmed in many studies. The gastric cancer diagnostic program based on AI showed relatively high diagnostic accuracy and could differentially diagnose non-neoplastic lesions including benign gastric ulcers and dysplasia. An AI system has also been developed that helps to predict the invasion depth of gastric cancer through endoscopic images and observe the stomach during endoscopy without blind spots. Therefore, if AI is used in the field of endoscopy, it is expected to aid in the diagnosis of gastric neoplasms and determine the application of endoscopic therapy by predicting the invasion depth.
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Affiliation(s)
- Ji Hyun Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
| | - Seung-Joo Nam
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
| | - Sung Chul Park
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Kangwon National University School of Medicine, Chuncheon 24289, Kangwon Do, South Korea
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Bardhi O, Sierra-Sosa D, Garcia-Zapirain B, Bujanda L. Deep Learning Models for Colorectal Polyps. INFORMATION 2021; 12:245. [DOI: 10.3390/info12060245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023] Open
Abstract
Colorectal cancer is one of the main causes of cancer incident cases and cancer deaths worldwide. Undetected colon polyps, be them benign or malignant, lead to late diagnosis of colorectal cancer. Computer aided devices have helped to decrease the polyp miss rate. The application of deep learning algorithms and techniques has escalated during this last decade. Many scientific studies are published to detect, localize, and classify colon polyps. We present here a brief review of the latest published studies. We compare the accuracy of these studies with our results obtained from training and testing three independent datasets using a convolutional neural network and autoencoder model. A train, validate and test split was performed for each dataset, 75%, 15%, and 15%, respectively. An accuracy of 0.937 was achieved for CVC-ColonDB, 0.951 for CVC-ClinicDB, and 0.967 for ETIS-LaribPolypDB. Our results suggest slight improvements compared to the algorithms used to date.
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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.
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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
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A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. Cancers (Basel) 2021; 13:cancers13092025. [PMID: 33922197 PMCID: PMC8122718 DOI: 10.3390/cancers13092025] [Citation(s) in RCA: 305] [Impact Index Per Article: 101.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/20/2021] [Accepted: 04/21/2021] [Indexed: 02/07/2023] Open
Abstract
This review article contains a concise consideration of genetic and environmental risk factors for colorectal cancer. Known risk factors associated with colorectal cancer include familial and hereditary factors and lifestyle-related and ecological factors. Lifestyle factors are significant because of the potential for improving our understanding of the disease. Physical inactivity, obesity, smoking and alcohol consumption can also be addressed through therapeutic interventions. We also made efforts to systematize available literature and data on epidemiology, diagnosis, type and nature of symptoms and disease stages. Further study of colorectal cancer and progress made globally is crucial to inform future strategies in controlling the disease's burden through population-based preventative initiatives.
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