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Wang Y, He C. ENDOANGEL improves detection of missed colorectal adenomas in second colonoscopy: A retrospective study. Medicine (Baltimore) 2024; 103:e38938. [PMID: 38996141 PMCID: PMC11245239 DOI: 10.1097/md.0000000000038938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/14/2024] Open
Abstract
The ENDOANGEL (EN) computer-assisted detection technique has emerged as a promising tool for enhancing the detection rate of colorectal adenomas during colonoscopies. However, its efficacy in identifying missed adenomas during subsequent colonoscopies remains unclear. Thus, we herein aimed to compare the adenoma miss rate (AMR) between EN-assisted and standard colonoscopies. Data from patients who underwent a second colonoscopy (EN-assisted or standard) within 6 months between September 2022 and May 2023 were analyzed. The EN-assisted group exhibited a significantly higher AMR (24.3% vs 11.9%, P = .005) than the standard group. After adjusting for potential confounders, multivariable analysis revealed that the EN-assisted group had a better ability to detect missed adenomas than the standard group (odds ratio = 2.89; 95% confidence interval = 1.14-7.80, P = .029). These findings suggest that EN-assisted colonoscopy represents a valuable advancement in improving AMR compared with standard colonoscopy. The integration of EN-assisted colonoscopy into routine clinical practice may offer significant benefits to patients requiring hospital resection of lesions following adenoma detection during their first colonoscopy.
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Affiliation(s)
- Yundong Wang
- Department of Gastroenterology, Yijishan Hospital of Wannan Medical College, Wuhu, Anhui, People's Republic of China
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2
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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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Maida M, Marasco G, Facciorusso A, Shahini E, Sinagra E, Pallio S, Ramai D, Murino A. Effectiveness and application of artificial intelligence for endoscopic screening of colorectal cancer: the future is now. Expert Rev Anticancer Ther 2023; 23:719-729. [PMID: 37194308 DOI: 10.1080/14737140.2023.2215436] [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: 12/02/2022] [Accepted: 05/15/2023] [Indexed: 05/18/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) in gastrointestinal endoscopy includes systems designed to interpret medical images and increase sensitivity during examination. This may be a promising solution to human biases and may provide support during diagnostic endoscopy. AREAS COVERED This review aims to summarize and evaluate data supporting AI technologies in lower endoscopy, addressing their effectiveness, limitations, and future perspectives. EXPERT OPINION Computer-aided detection (CADe) systems have been studied with promising results, allowing for an increase in adenoma detection rate (ADR), adenoma per colonoscopy (APC), and a reduction in adenoma miss rate (AMR). This may lead to an increase in the sensitivity of endoscopic examinations and a reduction in the risk of interval-colorectal cancer. In addition, computer-aided characterization (CADx) has also been implemented, aiming to distinguish adenomatous and non-adenomatous lesions through real-time assessment using advanced endoscopic imaging techniques. Moreover, computer-aided quality (CADq) systems have been developed with the aim of standardizing quality measures in colonoscopy (e.g. withdrawal time and adequacy of bowel cleansing) both to improve the quality of examinations and set a reference standard for randomized controlled trials.
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Affiliation(s)
- Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta, Italy
| | - Giovanni Marasco
- IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Antonio Facciorusso
- Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology-IRCCS "Saverio de Bellis", Castellana Grotte, Bari, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalu, Italy
| | - Socrate Pallio
- Digestive Diseases Endoscopy Unit, Policlinico G. Martino Hospital, University of Messina, Messina, Italy
| | - Daryl Ramai
- Gastroenterology & Hepatology, University of Utah Health, Salt Lake City, UT, USA
| | - Alberto Murino
- Royal Free Unit for Endoscopy, The Royal Free Hospital and University College London Institute for Liver and Digestive Health, Hampstead, London, UK
- Department of Gastroenterology, Cleveland Clinic London, London, UK
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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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Pallio S, Crinò SF, Maida M, Sinagra E, Tripodi VF, Facciorusso A, Ofosu A, Conti Bellocchi MC, Shahini E, Melita G. Endoscopic Ultrasound Advanced Techniques for Diagnosis of Gastrointestinal Stromal Tumours. Cancers (Basel) 2023; 15:1285. [PMID: 36831627 PMCID: PMC9954263 DOI: 10.3390/cancers15041285] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/08/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Gastrointestinal Stromal Tumors (GISTs) are subepithelial lesions (SELs) that commonly develop in the gastrointestinal tract. GISTs, unlike other SELs, can exhibit malignant behavior, so differential diagnosis is critical to the decision-making process. Endoscopic ultrasound (EUS) is considered the most accurate imaging method for diagnosing and differentiating SELs in the gastrointestinal tract by assessing the lesions precisely and evaluating their malignant risk. Due to their overlapping imaging characteristics, endosonographers may have difficulty distinguishing GISTs from other SELs using conventional EUS alone, and the collection of tissue samples from these lesions may be technically challenging. Even though it appears to be less effective in the case of smaller lesions, histology is now the gold standard for achieving a final diagnosis and avoiding unnecessary and invasive treatment for benign SELs. The use of enhanced EUS modalities and elastography has improved the diagnostic ability of EUS. Furthermore, recent advancements in artificial intelligence systems that use EUS images have allowed them to distinguish GISTs from other SELs, thereby improving their diagnostic accuracy.
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Affiliation(s)
- Socrate Pallio
- Department of Clinical and Experimental Medicine, University of Messina, 98100 Messina, Italy
| | | | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, 93100 Caltanissetta, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, 90015 Cefalù, Italy
| | | | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical and Surgical Sciences, University of Foggia, 71100 Foggia, Italy
| | - Andrew Ofosu
- Division of Digestive Diseases, University of Cincinnati, Cincinnati, OH 45201, USA
| | | | - Endrit Shahini
- Gastroenterology Unit, National Institute of Gastroenterology—IRCCS “Saverio de Bellis” Castellana Grotte, 70013 Castellana Grotte, Italy
| | - Giuseppinella Melita
- Human Pathology of Adult and Child Department, University of Messina, 98100 Messina, Italy
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Ghaffar Nia N, Kaplanoglu E, Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. DISCOVER ARTIFICIAL INTELLIGENCE 2023. [PMCID: PMC9885935 DOI: 10.1007/s44163-023-00049-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
AbstractA broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.
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Affiliation(s)
- Nafiseh Ghaffar Nia
- College of Engineering and Computer Science, The University of Tennessee at Chattanooga, Chattanooga, TN 37403 USA
| | - Erkan Kaplanoglu
- College of Engineering and Computer Science, The University of Tennessee at Chattanooga, Chattanooga, TN 37403 USA
| | - Ahad Nasab
- College of Engineering and Computer Science, The University of Tennessee at Chattanooga, Chattanooga, TN 37403 USA
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Da Rio L, Spadaccini M, Parigi TL, Gabbiadini R, Dal Buono A, Busacca A, Maselli R, Fugazza A, Colombo M, Carrara S, Franchellucci G, Alfarone L, Facciorusso A, Hassan C, Repici A, Armuzzi A. Artificial intelligence and inflammatory bowel disease: Where are we going? World J Gastroenterol 2023; 29:508-520. [PMID: 36688019 PMCID: PMC9850939 DOI: 10.3748/wjg.v29.i3.508] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/05/2022] [Accepted: 12/27/2022] [Indexed: 01/12/2023] Open
Abstract
Inflammatory bowel diseases, namely ulcerative colitis and Crohn's disease, are chronic and relapsing conditions that pose a growing burden on healthcare systems worldwide. Because of their complex and partly unknown etiology and pathogenesis, the management of ulcerative colitis and Crohn's disease can prove challenging not only from a clinical point of view but also for resource optimization. Artificial intelligence, an umbrella term that encompasses any cognitive function developed by machines for learning or problem solving, and its subsets machine learning and deep learning are becoming ever more essential tools with a plethora of applications in most medical specialties. In this regard gastroenterology is no exception, and due to the importance of endoscopy and imaging numerous clinical studies have been gradually highlighting the relevant role that artificial intelligence has in inflammatory bowel diseases as well. The aim of this review was to summarize the most recent evidence on the use of artificial intelligence in inflammatory bowel diseases in various contexts such as diagnosis, follow-up, treatment, prognosis, cancer surveillance, data collection, and analysis. Moreover, insights into the potential further developments in this field and their effects on future clinical practice were discussed.
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Affiliation(s)
- Leonardo Da Rio
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Tommaso Lorenzo Parigi
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberto Gabbiadini
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Arianna Dal Buono
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Anita Busacca
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Matteo Colombo
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
| | - Gianluca Franchellucci
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Ludovico Alfarone
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Antonio Facciorusso
- Gastroenterology Unit, Department of Medical Sciences, University of Foggia, Foggia 71122, Foggia, Italy
| | - Cesare Hassan
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Milano, Italy
| | - Alessandro Armuzzi
- IBD Center, Humanitas Research Hospital, IRCCS, Rozzano 20089, Milano, Italy
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Stature estimation by semi-automatic measurements of 3D CT images of the femur. Int J Legal Med 2023; 137:359-377. [PMID: 36474127 PMCID: PMC9902306 DOI: 10.1007/s00414-022-02921-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 11/15/2022] [Indexed: 12/12/2022]
Abstract
Stature estimation is one of the most basic and important methods of personal identification. The long bones of the limbs provide the most accurate stature estimation, with the femur being one of the most useful. In all the previously reported methods of stature estimation using computed tomography (CT) images of the femur, laborious manual measurement was necessary. A semi-automatic bone measuring method can simplify this process, so we firstly reported a stature estimation process using semi-automatic bone measurement software equipped with artificial intelligence. Multiple measurements of femurs of adult Japanese cadavers were performed using automatic three-dimensional reconstructed CT images of femurs. After manually setting four points on the femur, an automatic measurement was acquired. The relationships between stature and five femoral measurements, with acceptable intraobserver and interobserver errors, were analyzed with single regression analysis using the standard error of the estimate (SEE) and the coefficient of determination (R2). The maximum length of the femur (MLF) provided the lowest SEE and the highest R2; the SEE and R2 in all cadavers, males and females, respectively, were 3.913 cm (R2 = 0.842), 3.664 cm (R2 = 0.705), and 3.456 cm (R2 = 0.686) for MLF on the right femur, and 3.837 cm (R2 = 0.848), 3.667 cm (R2 = 0.705), and 3.384 cm (R2 = 0.699) for MLF on the left femur. These results were non-inferior to those of previous reports regarding stature estimation using the MLF. Stature estimation with this simple and time-saving method would be useful in forensic medical practice.
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Gubbiotti A, Spadaccini M, Badalamenti M, Hassan C, Repici A. Key factors for improving adenoma detection rate. Expert Rev Gastroenterol Hepatol 2022; 16:819-833. [PMID: 36151898 DOI: 10.1080/17474124.2022.2128761] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Colonoscopy is a fundamental tool in colorectal cancer (CRC) prevention. Nevertheless, one-fourth of colorectal neoplasms are still missed during colonoscopy, potentially being the main reason for post-colonoscopy colorectal cancer (PCCRC). Adenoma detection rate (ADR) is currently known as the best quality indicator correlating with PCCRC incidence. AREAS COVERED We performed a literature review in order to summarize evidences investigating key factors affecting ADR: endoscopists education and training, patient management, endoscopic techniques, improved navigation (exposition defect), and enhanced lesions recognition (vision defect) were considered. EXPERT OPINION 'Traditional' factors, such as split dose bowel preparation, adequate withdrawal time, and right colon second view, held a significant impact on ADR. Several devices and technologies have been developed to promote high-quality colonoscopy, however artificial intelligence may be considered the most promising tool for ADR improvement, provided that endoscopists education and recording are guaranteed.
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Affiliation(s)
- Alessandro Gubbiotti
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Marco Spadaccini
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Matteo Badalamenti
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
| | - Alessandro Repici
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Rozzano, Italy
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Biscaglia G, Cocomazzi F, Gentile M, Loconte I, Mileti A, Paolillo R, Marra A, Castellana S, Mazza T, Di Leo A, Perri F. Real-time, computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists. Endosc Int Open 2022; 10:E616-E621. [PMID: 35571479 PMCID: PMC9106428 DOI: 10.1055/a-1783-9678] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 01/03/2022] [Indexed: 12/31/2022] Open
Abstract
Background and study aims Adenoma detection rate (ADR) is a well-accepted quality indicator of screening colonoscopy. In recent years, the added value of artificial intelligence (AI) has been demonstrated in terms of ADR and adenoma miss rate (AMR). To date, there are no studies evaluating the impact of AI on the performance of trainee endoscopists (TEs). This study aimed to assess whether AI might eliminate any difference in ADR or AMR between TEs and experienced endoscopists (EEs). Patients and methods We performed a prospective observational study in 45 subjects referred for screening colonoscopy. A same-day tandem examination was carried out for each patient by a TE with the AI assistance and subsequently by an EE unaware of the lesions detected by the TE. Besides ADR and AMR, we also calculated for each subgroup of endoscopists the adenoma per colonoscopy (APC), polyp detection rate (PDR), polyp per colonoscopy (PPC) and polyp miss rate (PMR). Subgroup analyses according to size, morphology, and site were also performed. Results ADR, APC, PDR, and PPC of AI-supported TEs were 38 %, 0.93, 62 %, 1.93, respectively. The corresponding parameters for EEs were 40 %, 1.07, 58 %, 2.22. No significant difference was found for each analysis between the two groups ( P > 0.05). AMR and PMR for AI-assisted TEs were 12.5 % and 13 %, respectively. Sub-analyses did not show any significant difference ( P > 0.05) between the two categories of operators. Conclusions In this single-center prospective study, the possible impact of AI on endoscopist quality training was demonstrated. In the future, this could result in better efficacy of screening colonoscopy by reducing the incidence of interval or missed cancers.
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Affiliation(s)
- Giuseppe Biscaglia
- Division of Gastroenterology and Endoscopy, “Casa Sollievo della Sofferenza” Hospital, IRCCS, San Giovanni Rotondo, Italy
| | - Francesco Cocomazzi
- Division of Gastroenterology and Endoscopy, “Casa Sollievo della Sofferenza” Hospital, IRCCS, San Giovanni Rotondo, Italy
| | - Marco Gentile
- Division of Gastroenterology and Endoscopy, “Casa Sollievo della Sofferenza” Hospital, IRCCS, San Giovanni Rotondo, Italy
| | - Ilaria Loconte
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Italy
| | - Alessia Mileti
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Italy
| | - Rosa Paolillo
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Italy
| | - Antonella Marra
- Division of Gastroenterology and Endoscopy, “Casa Sollievo della Sofferenza” Hospital, IRCCS, San Giovanni Rotondo, Italy
| | - Stefano Castellana
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tommaso Mazza
- Laboratory of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Alfredo Di Leo
- Section of Gastroenterology, Department of Emergency and Organ Transplantation, University of Bari, Italy
| | - Francesco Perri
- Division of Gastroenterology and Endoscopy, “Casa Sollievo della Sofferenza” Hospital, IRCCS, San Giovanni Rotondo, Italy
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Spadaccini M, Marco AD, Franchellucci G, Sharma P, Hassan C, Repici A. Discovering the first US FDA-approved computer-aided polyp detection system. Future Oncol 2022; 18:1405-1412. [PMID: 35081745 DOI: 10.2217/fon-2021-1135] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is the third most common cancer worldwide. Because of the slow progression of the precancerous precursors, an efficient endoscopic surveillance strategy may be expected. It seems that around one-fourth of colorectal malignancies are still missed during colonoscopy. Several endoscopic technologies have been introduced, without radical changes. Interest in the development of artificial intelligence applications in the medical field has grown in the past decade. Artificial intelligence can help to highlight a specific region of interest that needs closer examination for the identification of polyps. The aim of this review is to report the first clinical experiences with the first US FDA-approved, real-time, deep-learning, computer-aided detection system (GI Genius™, Medtronic).
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Affiliation(s)
- Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Alessandro De Marco
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Gianluca Franchellucci
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
| | - Prateek Sharma
- Kansas City VA Medical Center, Gastroenterology & Hepatology, Kansas City, MO 66045, USA
| | - Cesare Hassan
- Nuovo Regina Margherita Hospital, Digestive Endoscopy Unit, Rome, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
- Humanitas Clinical & Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy
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Spadaccini M, Vespa E, Chandrasekar VT, Desai M, Patel HK, Maselli R, Fugazza A, Carrara S, Anderloni A, Franchellucci G, De Marco A, Hassan C, Bhandari P, Sharma P, Repici A. Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do. World J Gastroenterol 2022; 28:1113-1122. [PMID: 35431503 PMCID: PMC8985480 DOI: 10.3748/wjg.v28.i11.1113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/12/2021] [Accepted: 02/13/2022] [Indexed: 02/06/2023] Open
Abstract
Barrett’s esophagus (BE) is a well-established risk factor for esophageal adenocarcinoma. It is recommended that patients have regular endoscopic surveillance, with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma. Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments, and with this aim, thorough endoscopic assessment is crucial and improves outcomes. The burden of missed neoplasia in BE is still far from being negligible, likely due to inappropriate endoscopic surveillance. Over the last two decades, advanced imaging techniques, moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies, have been introduced with the aim to enhance neoplasia detection in BE. As witnessed in other fields, artificial intelligence (AI) has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well. The aim of this commentary is to comprehensively summarize present evidence, recent research advances, and future perspectives regarding advanced imaging technology and AI in BE; the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited. It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE, in order to increase the diagnostic yield of endoscopy in clinical practice.
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Affiliation(s)
- Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Edoardo Vespa
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | | | - Madhav Desai
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Harsh K Patel
- Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, LA 70124, United States
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
| | - Andrea Anderloni
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
| | - Gianluca Franchellucci
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Alessandro De Marco
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Cesare Hassan
- Endoscopy Unit, Nuovo Regina Margherita Hospital, Roma 00153, Italy
| | - Pradeep Bhandari
- Department of Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth PO6 3LY, United Kingdom
- School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth PO6 3LY, United Kingdom
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, IRCCS, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
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13
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Rajan V, Srinath H, Bong CYS, Cichowski A, Young CJ, Hewett PJ. Software Analysis of Colonoscopy Videos Enhances Teaching and Quality Metrics. Cureus 2022; 14:e23039. [PMID: 35464512 PMCID: PMC9001872 DOI: 10.7759/cureus.23039] [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] [Accepted: 03/10/2022] [Indexed: 11/29/2022] Open
Abstract
Purpose Machine learning algorithms were hypothesized as being able to predict the quality of colonoscopy luminal images. This is to enhance training and quality indicators in endoscopy. Methods A separate study involving a randomized controlled trial of capped vs. un-capped colonoscopies provided the colonoscopy videos for this study. Videos were analyzed with an algorithm devised by the Australian Institute for Machine Learning. The image analysis validated focus measure, steerable filters-based metrics (SFIL), was used to assess luminal visualization quality and was compared with two independent clinician assessments (C1 and C2). Goodman and Kruskal's gamma (G) measure was used to assess rank correlation data using IBM SPSS Statistics for Windows, version 25.0 (IBM Corp., Armonk, NY). Results A total of 500 random colonoscopy video clips were extracted and analyzed, 88 being excluded. SFIL scores matched with C1 in 45% and C2 in 42% of cases, respectively. There was a significant correlation between SFIL and C1 (G = 0.644, p < 0.005) and SFIL and C2 (G = 0.734, p < 0.005). Conclusion This study demonstrates that machine learning algorithms can recognize the quality of luminal visualization during colonoscopy. We intend to apply this in the future to enhance colonoscopy training and as a metric for quality assessment.
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Toyoshima O, Nishizawa T, Yoshida S, Yamada T, Odawara N, Matsuno T, Obata M, Kurokawa K, Uekura C, Fujishiro M. Texture and color enhancement imaging in magnifying endoscopic evaluation of colorectal adenomas. World J Gastrointest Endosc 2022; 14:96-105. [PMID: 35316981 PMCID: PMC8908327 DOI: 10.4253/wjge.v14.i2.96] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 09/18/2021] [Accepted: 01/06/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Olympus Corporation has developed texture and color enhancement imaging (TXI) as a novel image-enhancing endoscopic technique. AIM To investigate the effectiveness of TXI in identifying colorectal adenomas using magnifying observation. METHODS Colorectal adenomas were observed by magnified endoscopy using white light imaging (WLI), TXI, narrow band imaging (NBI), and chromoendoscopy (CE). This study adopted mode 1 of TXI. Adenomas were confirmed by histological examination. TXI visibility was compared with the visibility of WLI, NBI, and CE for tumor margin, and vessel and surface patterns of the Japan NBI expert team (JNET) classification. Three expert endoscopists and three non-expert endoscopists evaluated the visibility scores, which were classified as 1, 2, 3, and 4. RESULTS Sixty-one consecutive adenomas were evaluated. The visibility score for tumor margin of TXI (3.47 ± 0.79) was significantly higher than that of WLI (2.86 ± 1.02, P < 0.001), but lower than that of NBI (3.76 ± 0.52, P < 0.001), regardless of the endoscopist's expertise. TXI (3.05 ± 0.79) had a higher visibility score for the vessel pattern of JNET classification than WLI (2.17 ± 0.90, P < 0.001) and CE (2.47 ± 0.87, P < 0.001), but lower visibility score than NBI (3.79 ± 0.47, P < 0.001), regardless of the experience of endoscopists. For the visibility score for the surface pattern of JNET classification, TXI (2.89 ± 0.85) was superior to WLI (1.95 ± 0.79, P < 0.01) and CE (2.75 ± 0.90, P = 0.002), but inferior to NBI (3.67 ± 0.55, P < 0.001). CONCLUSION TXI provided higher visibility than WLI, lower than NBI, and comparable to or higher than CE in the magnified observation of colorectal adenomas.
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Affiliation(s)
- Osamu Toyoshima
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
| | - Toshihiro Nishizawa
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology and Hepatology, International University of Health and Welfare, Narita Hospital, Narita 286-8520, Chiba, Japan
| | - Shuntaro Yoshida
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
| | - Tomoharu Yamada
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Nariaki Odawara
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Tatsuya Matsuno
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Miho Obata
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Ken Kurokawa
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Chie Uekura
- Gastroenterology, Toyoshima Endoscopy Clinic, Tokyo 157-0066, Japan
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
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Cianci P, Restini E. Artificial intelligence in colorectal cancer management. Artif Intell Cancer 2021; 2:79-89. [DOI: 10.35713/aic.v2.i6.79] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/22/2021] [Accepted: 12/29/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a new branch of computer science involving many disciplines and technologies. Since its application in the medical field, it has been constantly studied and developed. AI includes machine learning and neural networks to create new technologies or to improve existing ones. Various AI supporting systems are available for a personalized and novel strategy for the management of colorectal cancer (CRC). This mini-review aims to summarize the progress of research and possible clinical applications of AI in the investigation, early diagnosis, treatment, and management of CRC, to offer elements of knowledge as a starting point for new studies and future applications.
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Affiliation(s)
- Pasquale Cianci
- Department of Surgery and Traumatology, ASL BAT, Lorenzo Bonomo Hospital, Andria 76123, Puglia, Italy
| | - Enrico Restini
- Department of Surgery and Traumatology, ASL BAT, Lorenzo Bonomo Hospital, Andria 76123, Puglia, Italy
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Chen Z, Zhao Z, Zhang Z. Obstructive Sleep Apnea Syndrome Treated Using a Positive Pressure Ventilator Based on Artificial Intelligence Processor. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:5683433. [PMID: 34603650 PMCID: PMC8486548 DOI: 10.1155/2021/5683433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 08/16/2021] [Accepted: 08/25/2021] [Indexed: 11/17/2022]
Abstract
With the acceleration of people's life rhythm, obstructive sleep apnea syndrome appears more and more frequently. This research mainly discusses the treatment of obstructive sleep apnea syndrome with a positive pressure ventilator based on artificial intelligence processor. The information storage function of the smart positive pressure ventilator is included in the local medical terminal, presented after logging in with the user authority. It is mainly composed of data collection, data processing, and medical interface design, which embeds data request, data transmission, data analysis, and detailed tasks such as data compression and storage, and functions such as data display, image drawing, and alarm notification are realized by the medical interface. When the CPAP ventilator transmits respiratory data to the local medical terminal, it sends real-time respiratory information data packets. The data packet is collected and sent in real time in a fixed period and then received and analyzed by the local medical terminal. In the CPAP ventilator telemedicine system, the function of alarm message processing is mainly used to detect the patient's breathing status in real time, extract the alarm-related information, and generate an alarm. This function specifically includes several tasks such as alarm detection, alarm prompt, alarm storage, and remote transmission of alarm messages. The confirmed OSAS patients were pressure-titrated with a smart CPAP ventilator and then treated for 5 hours a day, followed by echocardiography after 5 months of continuous treatment. During the study, the average BMI was (28.9 ± 7.2) kg/m2 and the average AHI index was (53.1 ± 37.8) times/h. This study may help improve the quality of life of patients with obstructive sleep apnea syndrome.
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Affiliation(s)
- Zhuxiang Chen
- Hubei No. 3 People's Hospital of Jianghan University, Wuhan 430033, Hubei, China
| | - Zhang Zhao
- Hubei No. 3 People's Hospital of Jianghan University, Wuhan 430033, Hubei, China
| | - Zhimin Zhang
- Hubei No. 3 People's Hospital of Jianghan University, Wuhan 430033, Hubei, China
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Badalamenti M, Maselli R, Spadaccini M, Galtieri PA, Capogreco A, Repici A. How to trick artificial intelligence: rectal heterotopic gastric lateral spreading tumor. VideoGIE 2021; 6:350-353. [PMID: 34401629 PMCID: PMC8351327 DOI: 10.1016/j.vgie.2021.05.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Affiliation(s)
- Matteo Badalamenti
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Milan, Italy
| | - Roberta Maselli
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Milan, Italy
| | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Piera Alessia Galtieri
- IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Milan, Italy
| | - Antonio Capogreco
- Department of Biomedical Sciences, Humanitas University, Milan, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Milan, Italy.,IRCCS Humanitas Research Hospital, Digestive Endoscopy Unit, Division of Gastroenterology, Milan, Italy
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Alloro R, Sinagra E. Artificial intelligence and colorectal cancer: How far can you go? Artif Intell Cancer 2021; 2:7-11. [DOI: 10.35713/aic.v2.i2.7] [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: 03/24/2021] [Revised: 04/01/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.
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Affiliation(s)
- Rita Alloro
- Department of Surgical, Oncological and Oral Sciences (Di.Chir.On.S.), Unit of General and Oncological Surgery, Paolo Giaccone University Hospital, University of Palermo, Palermo 90127, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto G. Giglio, Palermo 90015, Italy
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Solitano V, D’Amico F, Allocca M, Fiorino G, Zilli A, Loy L, Gilardi D, Radice S, Correale C, Danese S, Peyrin-Biroulet L, Furfaro F. Rediscovering histology: what is new in endoscopy for inflammatory bowel disease? Therap Adv Gastroenterol 2021; 14:17562848211005692. [PMID: 33948114 PMCID: PMC8053840 DOI: 10.1177/17562848211005692] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 03/08/2021] [Indexed: 02/04/2023] Open
Abstract
The potential of endoscopic evaluation in the management of inflammatory bowel diseases (IBD) has undoubtedly grown over the last few years. When dealing with IBD patients, histological remission (HR) is now considered a desirable target along with symptomatic and endoscopic remission, due to its association with better long-term outcomes. Consequently, the ability of endoscopic techniques to reflect microscopic findings in vivo without having to collect biopsies has become of upmost importance. In this context, a more accurate evaluation of inflammatory disease activity and the detection of dysplasia represent two mainstay targets for IBD endoscopists. New diagnostic technologies have been developed, such as dye-less chromoendoscopy, endomicroscopy, and molecular imaging, but their real incorporation in daily practice is not yet well defined. Although dye-chromoendoscopy is still recommended as the gold standard approach in dysplasia surveillance, recent research questioned the superiority of this technique over new advanced dye-less modalities [narrow band imaging (NBI), Fuji intelligent color enhancement (FICE), i-scan, blue light imaging (BLI) and linked color imaging (LCI)]. The endoscopic armamentarium might also be enriched by new video capsule endoscopy for monitoring disease activity, and high expectations are placed on the application of artificial intelligence (AI) systems to reduce operator-subjectivity and inter-observer variability. The goal of this review is to provide an updated insight on contemporary knowledge regarding new endoscopic techniques and devices, with special focus on their role in the assessment of disease activity and colorectal cancer surveillance.
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Affiliation(s)
- Virginia Solitano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Ferdinando D’Amico
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy,IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Mariangela Allocca
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy,IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Gionata Fiorino
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy,IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Alessandra Zilli
- IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Laura Loy
- IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Daniela Gilardi
- IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Simona Radice
- IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Carmen Correale
- IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Silvio Danese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy,IBD Center, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Department of Gastroenterology and Inserm NGERE U1256, University Hospital of Nancy, University of Lorraine, Vandoeuvre-lès-Nancy, France
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