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Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] [Imported: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
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Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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2
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Mosca V, Fuschillo G, Sciaudone G, Sahnan K, Selvaggi F, Pellino G. Use of artificial intelligence in total mesorectal excision in rectal cancer surgery: State of the art and perspectives. Artif Intell Gastroenterol 2023; 4:64-71. [DOI: 10.35712/aig.v4.i3.64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/13/2023] [Accepted: 10/23/2023] [Indexed: 12/07/2023] [Imported: 12/07/2023] Open
Abstract
BACKGROUND Colorectal cancer is a major public health problem, with 1.9 million new cases and 953000 deaths worldwide in 2020. Total mesorectal excision (TME) is the standard of care for the treatment of rectal cancer and is crucial to prevent local recurrence, but it is a technically challenging surgery. The use of artificial intelligence (AI) could help improve the performance and safety of TME surgery.
AIM To review the literature on the use of AI and machine learning in rectal surgery and potential future developments.
METHODS Online scientific databases were searched for articles on the use of AI in rectal cancer surgery between 2020 and 2023.
RESULTS The literature search yielded 876 results, and only 13 studies were selected for review. The use of AI in rectal cancer surgery and specifically in TME is a rapidly evolving field. There are a number of different AI algorithms that have been developed for use in TME, including algorithms for instrument detection, anatomical structure identification, and image-guided navigation systems.
CONCLUSION AI has the potential to revolutionize TME surgery by providing real-time surgical guidance, preventing complications, and improving training. However, further research is needed to fully understand the benefits and risks of AI in TME surgery.
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Affiliation(s)
- Vinicio Mosca
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli 80138, Italy
| | - Giacomo Fuschillo
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli 80138, Italy
| | - Guido Sciaudone
- Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso 86100, Italy
| | - Kapil Sahnan
- Department of Colorectal Surgery, St Mark’s Hospital, London HA1 3UJ, United Kingdom
- Department of Surgery and Cancer, Imperial College London, London SW7 5NH, United Kingdom
| | - Francesco Selvaggi
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli 80138, Italy
| | - Gianluca Pellino
- Department of Advanced Medical and Surgical Sciences, Università degli Studi della Campania “Luigi Vanvitelli”, Napoli 80138, Italy
- Colorectal Surgery, Vall d’Hebron University Hospital, Barcelona 08035, Spain
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3
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Tee CHN, Ravi R, Ang TL, Li JW. Role of artificial intelligence in Barrett’s esophagus. Artif Intell Gastroenterol 2023; 4:28-35. [DOI: 10.35712/aig.v4.i2.28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 05/17/2023] [Accepted: 06/12/2023] [Indexed: 09/07/2023] [Imported: 09/07/2023] Open
Abstract
The application of artificial intelligence (AI) in gastrointestinal endoscopy has gained significant traction over the last decade. One of the more recent applications of AI in this field includes the detection of dysplasia and cancer in Barrett’s esophagus (BE). AI using deep learning methods has shown promise as an adjunct to the endoscopist in detecting dysplasia and cancer. Apart from visual detection and diagnosis, AI may also aid in reducing the considerable interobserver variability in identifying and distinguishing dysplasia on whole slide images from digitized BE histology slides. This review aims to provide a comprehensive summary of the key studies thus far as well as providing an insight into the future role of AI in Barrett’s esophagus.
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Affiliation(s)
- Chin Hock Nicholas Tee
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Rajesh Ravi
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore 529889, Singapore
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Ortiz GX, Ulbrich AHDPDS, Lenhart G, dos Santos HDP, Schwambach KH, Becker MW, Blatt CR. Drug-induced liver injury and COVID-19: Use of artificial intelligence and the updated Roussel Uclaf Causality Assessment Method in clinical practice. Artif Intell Gastroenterol 2023; 4:36-47. [DOI: 10.35712/aig.v4.i2.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/18/2023] [Accepted: 09/05/2023] [Indexed: 09/07/2023] [Imported: 09/07/2023] Open
Abstract
BACKGROUND Liver injury is a relevant condition in coronavirus disease 2019 (COVID-19) inpatients. Pathophysiology varies from direct infection by virus, systemic inflammation or drug-induced adverse reaction (DILI). DILI detection and monitoring is clinically relevant, as it may contribute to poor prognosis, prolonged hospitalization and increase indirect healthcare costs. Artificial Intelligence (AI) applied in data mining of electronic medical records combining abnormal liver tests, keyword searching tools, and risk factors analysis is a relevant opportunity for early DILI detection by automated algorithms.
AIM To describe DILI cases in COVID-19 inpatients detected from data mining in electronic medical records (EMR) using AI and the updated Roussel Uclaf Causality Assessment Method (RUCAM).
METHODS The study was conducted in March 2021 in a hospital in southern Brazil. The NoHarm© system uses AI to support decision making in clinical pharmacy. Hospital admissions were 100523 during this period, of which 478 met the inclusion criteria. From these, 290 inpatients were excluded due to alternative causes of liver injury and/or due to not having COVID-19. We manually reviewed the EMR of 188 patients for DILI investigation. Absence of clinical information excluded most eligible patients. The DILI assessment causality was possible via the updated RUCAM in 17 patients.
RESULTS Mean patient age was 53 years (SD ± 18.37; range 22-83), most were male (70%), and admitted to the non-intensive care unit sector (65%). Liver injury pattern was mainly mixed, mean time to normalization of liver markers was 10 d, and mean length of hospitalization was 20.5 d (SD ± 16; range 7-70). Almost all patients recovered from DILI and one patient died of multiple organ failure. There were 31 suspected drugs with the following RUCAM score: Possible (n = 24), probable (n = 5), and unlikely (n = 2). DILI agents in our study were ivermectin, bicalutamide, linezolid, azithromycin, ceftriaxone, amoxicillin-clavulanate, tocilizumab, piperacillin-tazobactam, and albendazole. Lack of essential clinical information excluded most patients. Although rare, DILI is a relevant clinical condition in COVID-19 patients and may contribute to poor prognostics.
CONCLUSION The incidence of DILI in COVID-19 inpatients is rare and the absence of relevant clinical information on EMR may underestimate DILI rates. Prospects involve creation and validation of alerts for risk factors in all DILI patients based on RUCAM assessment causality, alterations of liver biomarkers and AI and machine learning.
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Affiliation(s)
- Gabriela Xavier Ortiz
- Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
| | | | - Gabriele Lenhart
- Multiprofessional Residency Integrated in Health, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
| | | | - Karin Hepp Schwambach
- Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
| | - Matheus William Becker
- Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
| | - Carine Raquel Blatt
- Department of Pharmacoscience, Graduate Program in Medicine – Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
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Yuan Q, Zhao WL, Qin B. Big data and variceal rebleeding prediction in cirrhosis patients. Artif Intell Gastroenterol 2023; 4:1-9. [DOI: 10.35712/aig.v4.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/03/2023] [Accepted: 03/10/2023] [Indexed: 06/08/2023] [Imported: 07/06/2023] Open
Abstract
Big data has convincing merits in developing risk stratification strategies for diseases. The 6 “V”s of big data, namely, volume, velocity, variety, veracity, value, and variability, have shown promise for real-world scenarios. Big data can be applied to analyze health data and advance research in preclinical biology, medicine, and especially disease initiation, development, and control. A study design comprises data selection, inclusion and exclusion criteria, standard confirmation and cohort establishment, follow-up strategy, and events of interest. The development and efficiency verification of a prognosis model consists of deciding the data source, taking previous models as references while selecting candidate predictors, assessing model performance, choosing appropriate statistical methods, and model optimization. The model should be able to inform disease development and outcomes, such as predicting variceal rebleeding in patients with cirrhosis. Our work has merits beyond those of other colleagues with respect to cirrhosis patient screening and data source regarding variceal bleeding.
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Affiliation(s)
- Quan Yuan
- Department of Gastroenterology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
| | - Wen-Long Zhao
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
- Medical Data Science Academy, Chongqing 400016, China
- Chongqing Engineering Research Centre for Clinical Big-data and Drug Evaluation, Chongqing 400016, China
| | - Bo Qin
- Department of Infectious Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400042, China
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Aziz S, König S, Umer M, Akhter TS, Iqbal S, Ibrar M, Ur-Rehman T, Ahmad T, Hanafiah A, Zahra R, Rasheed F. Risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori: A study of a tertiary care hospital in Pakistan. Artif Intell Gastroenterol 2023; 4:10-27. [DOI: 10.35712/aig.v4.i1.10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/01/2023] [Accepted: 04/20/2023] [Indexed: 06/08/2023] [Imported: 07/06/2023] Open
Abstract
BACKGROUND Gastric cancer (GC) is the fourth leading cause of cancer-related deaths worldwide. Diagnosis relies on histopathology and the number of endoscopies is increasing. Helicobacter pylori (H. pylori) infection is a major risk factor.
AIM To develop an in-silico GC prediction model to reduce the number of diagnostic surgical procedures. The meta-data of patients with gastroduodenal symptoms, risk factors associated with GC, and H. pylori infection status from Holy Family Hospital Rawalpindi, Pakistan, were used with machine learning.
METHODS A cohort of 341 patients was divided into three groups [normal gastric mucosa (NGM), gastroduodenal diseases (GDD), and GC]. Information associated with socioeconomic and demographic conditions and GC risk factors was collected using a questionnaire. H. pylori infection status was determined based on urea breath test. The association of these factors and histopathological grades was assessed statistically. K-Nearest Neighbors and Random Forest (RF) machine learning models were tested.
RESULTS This study reported an overall frequency of 64.2% (219/341) of H. pylori infection among enrolled subjects. It was higher in GC (74.2%, 23/31) as compared to NGM and GDD and higher in males (54.3%, 119/219) as compared to females. More abdominal pain (72.4%, 247/341) was observed than other clinical symptoms including vomiting, bloating, acid reflux and heartburn. The majority of the GC patients experienced symptoms of vomiting (91%, 20/22) with abdominal pain (100%, 22/22). The multinomial logistic regression model was statistically significant and correctly classified 80% of the GDD/GC cases. Age, income level, vomiting, bloating and medication had significant association with GDD and GC. A dynamic RF GC-predictive model was developed, which achieved > 80% test accuracy.
CONCLUSION GC risk factors were incorporated into a computer model to predict the likelihood of developing GC with high sensitivity and specificity. The model is dynamic and will be further improved and validated by including new data in future research studies. Its use may reduce unnecessary endoscopic procedures. It is freely available.
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Affiliation(s)
- Shahid Aziz
- Patients Diagnostic Lab, Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, Islamabad 44000, Pakistan
- Department of Microbiology, Quaid-i-Azam University, Islamabad 45320, Pakistan
- Interdisciplinary Center for Clinical Research, Core Unit Proteomics, University of Münster, Münster 48149, Germany
| | - Simone König
- Interdisciplinary Center for Clinical Research, Core Unit Proteomics, University of Münster, Münster 48149, Germany
| | - Muhammad Umer
- Management Information System Division, Pakistan Institute of Nuclear Science and Technology, Islamabad 44000, Pakistan
| | - Tayyab Saeed Akhter
- Centre for Liver and Digestive Diseases, Holy Family Hospital, Rawalpindi 46300, Pakistan
| | - Shafqat Iqbal
- Centre for Liver and Digestive Diseases, Holy Family Hospital, Rawalpindi 46300, Pakistan
| | - Maryum Ibrar
- Pakistan Scientific and Technological Information Centre, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Tofeeq Ur-Rehman
- Department of Pharmacy, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Tanvir Ahmad
- Patients Diagnostic Lab, Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, Islamabad 44000, Pakistan
| | - Alfizah Hanafiah
- Faculty of Medicine, Department of Medical Microbiology and Immunology, Universiti Kebangsan Malaysia, Cheras, Kuala Lumpur 56000, Malaysia
| | - Rabaab Zahra
- Department of Microbiology, Quaid-i-Azam University, Islamabad 45320, Pakistan
| | - Faisal Rasheed
- Patients Diagnostic Lab, Isotope Application Division, Pakistan Institute of Nuclear Science and Technology, Islamabad 44000, Pakistan
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Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3:142-162. [DOI: 10.35712/aig.v3.i5.142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/25/2022] [Accepted: 12/14/2022] [Indexed: 12/28/2022] Open
Abstract
Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.
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Affiliation(s)
- Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Elif Ocak Gedik
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | | | | | - Betul Unal
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Kusano Y, Funada K, Yamaguchi M, Sugawara M, Tamano M. Dietary counseling based on artificial intelligence for patients with nonalcoholic fatty liver disease. Artif Intell Gastroenterol 2022; 3:105-116. [DOI: 10.35712/aig.v3.i4.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/13/2022] [Accepted: 10/27/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND About 25% of the general population in Japan are reported to have nonalcoholic fatty liver disease (NAFLD). NAFLD and nonalcoholic steatohepatitis carry a risk of progressing further to hepatocellular carcinoma. The primary treatment for NAFLD is dietary therapy. Dietary counseling plays an essential role in dietary therapy. Although artificial intelligence (AI)-based nutrition management software applications have been developed and put into practical use in recent years, the majority focus on weight loss or muscle strengthening, and no software has been developed for patient use in clinical practice.
AIM To examine whether effective dietary counseling is possible using AI-based nutrition management software.
METHODS NAFLD patients who had been assessed using an AI-based nutrition management software application (Calomeal) that automatically analyzed images of meals photographed by patients and agreed to receive dietary counseling were given dietary counseling. Blood biochemistry tests were performed before (baseline) and 6 mo after (6M follow-up) dietary counseling. After the dietary counseling, the patients were asked to complete a questionnaire survey.
RESULTS A total of 29 patients diagnosed with NAFLD between August 2020 and March 2022 were included. There were significant decreases in liver enzyme and triglyceride levels at the 6M follow-up compared to baseline. The food analysis capability of the AI used by Calomeal in this study was 75.1%. Patient satisfaction with the AI-based dietary counselling was high.
CONCLUSION AI-based nutrition management appeared to raise awareness of dietary habits among NAFLD patients. However, it did not directly alleviate the burden of registered dietitians, and improvements are much anticipated.
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Affiliation(s)
- Yumi Kusano
- Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
| | - Kei Funada
- Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
| | - Mayumi Yamaguchi
- Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
| | - Miwa Sugawara
- Nutrition Unit, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
| | - Masaya Tamano
- Department of Gastroenterology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Saitama, Japan
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Mokhria RK, Singh J. Role of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma. Artif Intell Gastroenterol 2022; 3:96-104. [DOI: 10.35712/aig.v3.i4.96] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/30/2022] [Accepted: 09/14/2022] [Indexed: 02/07/2023] Open
Abstract
Artificial intelligence (AI) evolved many years ago, but it gained much advancement in recent years for its use in the medical domain. AI with its different subsidiaries, i.e. deep learning and machine learning, examine a large amount of data and performs an essential part in decision-making in addition to conquering the limitations related to human evaluation. Deep learning tries to imitate the functioning of the human brain. It utilizes much more data and intricate algorithms. Machine learning is AI based on automated learning. It utilizes earlier given data and uses algorithms to arrange and identify models. Globally, hepatocellular carcinoma is a major cause of illness and fatality. Although with substantial progress in the whole treatment strategy for hepatocellular carcinoma, managing it is still a major issue. AI in the area of gastroenterology, especially in hepatology, is particularly useful for various investigations of hepatocellular carcinoma because it is a commonly found tumor, and has specific radiological features that enable diagnostic procedures without the requirement of the histological study. However, interpreting and analyzing the resulting images is not always easy due to change of images throughout the disease process. Further, the prognostic process and response to the treatment process could be influenced by numerous components. Currently, AI is utilized in order to diagnose, curative and prediction goals. Future investigations are essential to prevent likely bias, which might subsequently influence the analysis of images and therefore restrict the consent and utilization of such models in medical practices. Moreover, experts are required to realize the real utility of such approaches, along with their associated potencies and constraints.
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Affiliation(s)
- Rajesh Kumar Mokhria
- Government Model Sanskriti Senior Secondary School, Chulkana, 132101, Panipat, Haryana, India
| | - Jasbir Singh
- Department of Biochemistry, Kurukshetra University, Kurukshetra, 136119, Haryana, India
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Brenner AR, Laoveeravat P, Carey PJ, Joiner D, Mardini SH, Jovani M. Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction. Artif Intell Gastroenterol 2022; 3:88-95. [DOI: 10.35712/aig.v3.i3.88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/16/2022] [Accepted: 05/08/2022] [Indexed: 02/06/2023] Open
Abstract
While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.
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Affiliation(s)
- Aaron R Brenner
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Passisd Laoveeravat
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Patrick J Carey
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Danielle Joiner
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Samuel H Mardini
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KENTUCKY 40536, United States
| | - Manol Jovani
- Digestive Diseases and Nutrition, University of Kentucky Albert B. Chandler Hospital, Lexington, KY 40536, United States
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12
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Carteri RB, Grellert M, Borba DL, Marroni CA, Fernandes SA. Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases. Artif Intell Gastroenterol 2022; 3:80-87. [DOI: 10.35712/aig.v3.i3.80] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 04/15/2022] [Accepted: 05/08/2022] [Indexed: 02/06/2023] Open
Abstract
The prevalence of nonalcoholic fatty liver disease (NAFLD) is an important public health concern. Early diagnosis of NAFLD and potential progression to nonalcoholic steatohepatitis (NASH), could reduce the further advance of the disease, and improve patient outcomes. Aiming to support patient diagnostic and predict specific outcomes, the interest in artificial intelligence (AI) methods in hepatology has dramatically increased, especially with the application of less-invasive biomarkers. In this review, our objective was twofold: Firstly, we presented the most frequent blood biomarkers in NAFLD and NASH and secondly, we reviewed recent literature regarding the use of machine learning (ML) methods to predict NAFLD and NASH in large cohorts. Strikingly, these studies provide insights into ML application in NAFLD patients' prognostics and ranked blood biomarkers are able to provide a recognizable signature allowing cost-effective NAFLD prediction and also differentiating NASH patients. Future studies should consider the limitations in the current literature and expand the application of these algorithms in different populations, fortifying an already promising tool in medical science.
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Affiliation(s)
- Randhall B Carteri
- Department of Nutrition, Methodist University Center - IPA, Porto Alegre 90420-060, Rio Grande do Sul, Brazil
- Department of Health and Behaviour, Catholic University of Pelotas, Pelotas 96015-560, Rio Grande do Sul, Brazil
| | - Mateus Grellert
- Department of Informatics and Statistics, Federal University of Santa Catarina, Florianópolis 88040-900, Santa Catarina, Brazil
| | - Daniela Luisa Borba
- Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Rio Grande do Sul, Brazil
| | - Claudio Augusto Marroni
- Department of Gastroenterology and Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Rio Grande do Sul, Brazil
| | - Sabrina Alves Fernandes
- Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Rio Grande do Sul, Brazil
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13
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Gunasekharan A, Jiang J, Nickerson A, Jalil S, Mumtaz K. Application of artificial intelligence in non-alcoholic fatty liver disease and viral hepatitis. Artif Intell Gastroenterol 2022; 3:46-53. [DOI: 10.35712/aig.v3.i2.46] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 02/18/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide. It is critical to develop reliable methods of predicting progression to fibrosis, cirrhosis, and decompensated liver disease. Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data. Artificial intelligence (AI) provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis, and to identify patients at high risk for disease progression. We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis. Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis. We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction (MELD-Na score, liver biopsy, FIB-4 score, and biomarkers). They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy, for which there are currently no established prediction tools. These studies show that AI could be a helpful adjunct to existing techniques for diagnosing, monitoring, and treating both NAFLD and viral hepatitis.
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Affiliation(s)
| | - Joanna Jiang
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Ashley Nickerson
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Sajid Jalil
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
| | - Khalid Mumtaz
- Department of Medicine, Ohio State University, Columbus, OH 43210, United States
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14
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Ghosh NK, Kumar A. Colorectal cancer: Artificial intelligence and its role in surgical decision making. Artif Intell Gastroenterol 2022; 3:36-45. [DOI: 10.35712/aig.v3.i2.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/02/2022] [Accepted: 04/26/2022] [Indexed: 02/06/2023] Open
Abstract
Despite several advances in the oncological management of colorectal cancer (CRC), there still remains a lacuna in the treatment strategy, which differs from center to center and on the philosophy of the treating clinician that is not without bias. Personalized treatment is essential for the treatment of CRC to achieve better long-term outcomes and to reduce morbidity. Surgery has an important role to play in the treatment. Surgical treatment of CRC is decided based on clinical parameters and investigations and hence likely to have judgmental errors. Artificial intelligence has been reported to be useful in the surveillance, diagnosis, treatment, and follow-up with accuracy in several malignancies. However, it is still evolving and yet to be established in surgical decision making in CRC. It is not only useful preoperatively but also intraoperatively. Artificial intelligence helps to rectify the human surgical decision when clinical data and radiological and laboratory parameters are fed into the computer and may guide correct surgical treatment.
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Affiliation(s)
- Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, UP, India
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15
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Juneja D, Gupta A, Singh O. Artificial intelligence in critically ill diabetic patients: current status and future prospects. Artif Intell Gastroenterol 2022; 3:66-79. [DOI: 10.35712/aig.v3.i2.66] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Recent years have witnessed increasing numbers of artificial intelligence (AI) based applications and devices being tested and approved for medical care. Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis, to predict and diagnose early complications, increase adherence to therapy, and even motivate patients to manage diabetes and maintain glycemic control. However, these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems. Intensive care units (ICUs) have a dynamic environment generating huge data, which AI can extract and organize simultaneously, thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest. Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia, complicating their ICU course and affecting outcomes. In addition, to maintain glycemic control frequent blood sampling and insulin dose adjustments are required, increasing nursing workload and chances of error. AI has the potential to improve glycemic control while reducing the nursing workload and errors. Continuous glucose monitoring (CGM) devices, which are Food and Drug Administration (FDA) approved for use in non-critically ill patients, are now being recommended for use in specific ICU populations with increased accuracy. AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients. Even though many of these AI applications have shown potential, these devices need to be tested in larger number of ICU patients, have wider availability, show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems, before they become acceptable to ICU physicians for routine use.
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Affiliation(s)
- Deven Juneja
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| | - Anish Gupta
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| | - Omender Singh
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
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Tonini V, Vigutto G, Donati R. Liver surgery for colorectal metastasis: New paths and new goals with the help of artificial intelligence. Artif Intell Gastroenterol 2022; 3:28-35. [DOI: 10.35712/aig.v3.i2.28] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is one of the most common neoplasia with an high risk to metastatic spread. Improving medical and surgical treatment is moving along with improving the precision of diagnosis and patient's assessment, the latter two aided more and more with the use of artificial intelligence (AI). The management of colorectal liver metastasis is multidisciplinary, and surgery is the main option. After the diagnosis, a surgical assessment of the patient is fundamental. Reaching a R0 resection with a proper remnant liver volume can be done using new techniques involving also artificial intelligence. Considering the recent application of artificial intelligence as a valid substitute for liver biopsy in chronic liver diseases, several authors tried to apply similar techniques to pre-operative imaging of liver metastasis. Radiomics showed good results in identifying structural changes in a unhealthy liver and in evaluating the prognosis after a liver resection. Recently deep learning has been successfully applied in estimating the remnant liver volume before surgery. Moreover AI techniques can help surgeons to perform an early diagnosis of neoplastic relapse or a better differentiation between a colorectal metastasis and a benign lesion. AI could be applied also in the histopathological diagnostic tool. Although AI implementation is still partially automatized, it appears faster and more precise than the usual diagnostic tools and, in the short future, could become the new gold standard in liver surgery.
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Affiliation(s)
- Valeria Tonini
- Department of Medical and Surgical Sciences, Sant' Orsola Hospital University of Bologna, Bologna 40138, Italy
| | - Gabriele Vigutto
- Department of Medical and Surgical Sciences, St Orsola Hospital, University of Bologna, Bologna 40138, Italy
| | - Riccardo Donati
- Department of Electrical, Electronic and Information Engineering ”Guglielmo Marconi” (DEI), University of Bologna, Bologna 40138, Italy
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Simsek C, Lee LS. Machine learning in endoscopic ultrasonography and the pancreas: The new frontier? Artif Intell Gastroenterol 2022; 3:54-65. [DOI: 10.35712/aig.v3.i2.54] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.
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Affiliation(s)
- Cem Simsek
- Department of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, United States
| | - Linda S Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
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18
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Maulahela H, Annisa NG. Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding. Artif Intell Gastroenterol 2022; 3:13-20. [DOI: 10.35712/aig.v3.i1.13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/24/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial Intelligence (AI) is a type of intelligence that comes from machines or computer systems that mimics human cognitive function. Recently, AI has been utilized in medicine and helped clinicians make clinical decisions. In gastroenterology, AI has assisted colon polyp detection, optical biopsy, and diagnosis of Helicobacter pylori infection. AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding. Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding. AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion, determining bleeding stigmata during endoscopy, and predicting recurrence of gastrointestinal bleeding. The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding, primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods. This review will also discuss challenges in implementing AI in routine practice.
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Affiliation(s)
- Hasan Maulahela
- Department of Internal Medicine, Gastroenterology Division, Faculty of Medicine University of Indonesia - Cipto Mangunkusumo General Central National Hospital, Jakarta 10430, Indonesia
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19
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Al-Biltagi M, Saeed NK, Qaraghuli S. Gastrointestinal disorders in children with autism: Could artificial intelligence help? Artif Intell Gastroenterol 2022; 3:1-12. [DOI: 10.35712/aig.v3.i1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/12/2022] [Accepted: 02/20/2022] [Indexed: 02/06/2023] Open
Abstract
Autism is one of the pervasive neurodevelopmental disorders usually associated with many medical comorbidities. Gastrointestinal (GI) disorders are pervasive in children, with a 46%-84% prevalence rate. Children with Autism have an increased frequency of diarrhea, nausea and/or vomiting, gastroesophageal reflux and/or disease, abdominal pain, chronic flatulence due to various factors as food allergies, gastrointestinal dysmotility, irritable bowel syndrome (IBS), and inflammatory bowel diseases (IBD). These GI disorders have a significant negative impact on both the child and his/her family. Artificial intelligence (AI) could help diagnose and manage Autism by improving children's communication, social, and emotional skills for a long time. AI is an effective method to enhance early detection of GI disorders, including GI bleeding, gastroesophageal reflux disease, Coeliac disease, food allergies, IBS, IBD, and rectal polyps. AI can also help personalize the diet for children with Autism by microbiome modification. It can help to provide modified gluten without initiating an immune response. However, AI has many obstacles in treating digestive diseases, especially in children with Autism. We need to do more studies and adopt specific algorithms for children with Autism. In this article, we will highlight the role of AI in helping children with gastrointestinal disorders, with particular emphasis on children with Autism.
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Affiliation(s)
- Mohammed Al-Biltagi
- Department of Pediatrics, Faculty of Medicine, Tanta University, Tanta 31511, Alghrabia, Egypt
- Department of Pediatrics, University Medical Center, King Abdulla Medical City, Arabian Gulf University, Dr Sulaiman Al Habib Medical Group, Manama 26671, Manama, Bahrain
| | - Nermin Kamal Saeed
- Medical Microbiology Section, Pathology Department, Salmaniya Medical Complex, Ministry of Health, Kingdom of Bahrain, Manama 12, Manama, Bahrain
- Microbiology Section, Pathology Department, Irish Royal College of Surgeon, Bahrain, Busaiteen 15503, Muharraq, Bahrain
| | - Samara Qaraghuli
- Department of Pharmacognosy and Medicinal Plant, Faculty of Pharmacy, Al-Mustansiriya University, Baghdad 14022, Baghdad, Iraq
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20
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Mucenic M, de Mello Brandão AB, Marroni CA. Artificial intelligence and human liver allocation: Potential benefits and ethical implications. Artif Intell Gastroenterol 2022; 3:21-27. [DOI: 10.35712/aig.v3.i1.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/13/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Since its implementation almost two decades ago, the urgency allocation policy has improved the survival of patients on the waiting list for liver transplantation worldwide. The Model for End-Stage Liver Disease score is widely used to predict waiting list mortality. Due to some limitations related to its use, there is an active investigation to develop other prognostic scores. Liver allocation (LA) entails complex decision-making, and grafts are occasionally not directed to the recipients who are more likely to survive. Prognostic scores have, thus far, failed to predict post-operatory survival. Furthermore, the increasing use of marginal donors is associated with worse outcomes. Adequate donor-recipient pairing could help avoid retransplantation or futile procedures and reduce postoperative complications, mortality, hospitalization time, and costs. Artificial intelligence has applications in several medical fields. Machine learning algorithms (MLAs) use large amounts of data to detect unforeseen patterns and complex interactions between variables. Artificial neural networks and decision trees were the most common forms of MLA tested on LA. Some researchers have shown them to be superior for predicting waiting list mortality and graft failure than conventional statistical methods. These promising techniques are increasingly being considered for implementation.
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Affiliation(s)
- Marcos Mucenic
- Liver Transplant Adult Group, Irmandade da Santa Casa de Misericórdia de Porto Alegre, Porto Alegre 90020-090, RS, Brazil
| | | | - Claudio Augusto Marroni
- Hepatology, Universidade Federal de Ciencias da Saude de Porto Alegre, Porto Alegre 90050-170, RS, Brazil
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21
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Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2:141-156. [DOI: 10.35712/aig.v2.i6.141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/19/2021] [Accepted: 12/27/2021] [Indexed: 02/06/2023] Open
Abstract
The integration of artificial intelligence (AI) has shown promising benefits in many fields of diagnostic histopathology, including for gastrointestinal cancers (GCs), such as tumor identification, classification, and prognosis prediction. In parallel, recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters. In addition, AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC, as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists. From this point of view, it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology. Unfortunately, these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice. These include a broad spectrum of challenges from needs identification to cost-effectiveness. Therefore, unlike other disciplines of medicine, no histopathology-based AI application, including in GC, has ever been approved either by a regulatory authority or approved for public reimbursement. The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation.
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Affiliation(s)
- Anil Alpsoy
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Aysen Yavuz
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
| | - Gulsum Ozlem Elpek
- Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
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22
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Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2:133-140. [DOI: 10.35712/aig.v2.i5.133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/09/2021] [Accepted: 08/31/2021] [Indexed: 02/06/2023] Open
Abstract
Infectious or noninfectious liver disease has inexorably risen as one of the leading causes of global death and disease burden. There were an estimated 2.14 million liver-related deaths in 2017, representing an 11.4% increase since 2012. Traditional diagnosis and treatment methods have various dilemmas in different causes of liver disease. As a hot research topic in recent years, the application of artificial intelligence (AI) in different fields has attracted extensive attention, and new technologies have brought more ideas for the diagnosis and treatment of some liver diseases. Machine learning (ML) is the core of AI and the basic way to make a computer intelligent. ML technology has many potential uses in hepatology, ranging from exploring new noninvasive means to predict or diagnose different liver diseases to automated image analysis. The application of ML in liver diseases can help clinical staff to diagnose and treat different liver diseases quickly, accurately and scientifically, which is of importance for reducing the incidence and mortality of liver diseases, reducing medical errors, and promoting the development of medicine. This paper reviews the application and prospects of AI in liver diseases, and aims to improve clinicians’ awareness of the importance of AI in the diagnosis and treatment of liver diseases.
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Affiliation(s)
- Qiong Li
- The First Clinical Medical College, Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun-Feng Li
- Department of Infectious Diseases & Institute of Infectious Diseases, the First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Xiao-Rong Mao
- Department of Infectious Diseases, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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23
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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24
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Li ZM, Zhuang X. Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer. Artif Intell Gastroenterol 2021; 2:105-110. [DOI: 10.35712/aig.v2.i4.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/22/2021] [Accepted: 07/09/2021] [Indexed: 02/06/2023] Open
Abstract
The microbiome has been identified as a causing factor for many cancers. Helicobacter pylori contributes to the development of gastric cancer (GC) and impacts disease treatments. The rapid development of sequencing technology is increasingly producing large-scale and complex big data. However, there are many obstacles in the analysis of these data by humans, which limit clinicians from making rapid decisions. Recently, the emergence of artificial intelligence (AI), including machine learning and deep learning, has greatly assisted clinicians in processing and interpreting large microbiome data. This paper reviews the application of AI in the study of the microbiome and discusses its potential in the diagnosis and therapy of GC. We also exemplify strategies for implementing microbiome-based precision medicines for patients with GC.
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Affiliation(s)
- Zhi-Ming Li
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- Department of Urology, The First Affiliated Hospital of Xiamen University, Xiamen 361003, Fujian Province, China
| | - Xuan Zhuang
- Department of Urology, The First Affiliated Hospital of Xiamen University, Xiamen 361003, Fujian Province, China
- Department of Clinical Medicine, Fujian Medical University, Fuzhou 350122, Fujian Province, China
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25
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Wahba R, Thomas MN, Bunck AC, Bruns CJ, Stippel DL. Clinical use of augmented reality, mixed reality, three-dimensional-navigation and artificial intelligence in liver surgery. Artif Intell Gastroenterol 2021; 2:94-104. [DOI: 10.35712/aig.v2.i4.94] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 07/10/2021] [Accepted: 08/27/2021] [Indexed: 02/06/2023] Open
Abstract
A precise knowledge of intra-parenchymal vascular and biliary architecture and the location of lesions in relation to the complex anatomy is indispensable to perform liver surgery. Therefore, virtual three-dimensional (3D)-reconstruction models from computed tomography/magnetic resonance imaging scans of the liver might be helpful for visualization. Augmented reality, mixed reality and 3D-navigation could transfer such 3D-image data directly into the operation theater to support the surgeon. This review examines the literature about the clinical and intraoperative use of these image guidance techniques in liver surgery and provides the reader with the opportunity to learn about these techniques. Augmented reality and mixed reality have been shown to be feasible for the use in open and minimally invasive liver surgery. 3D-navigation facilitated targeting of intraparenchymal lesions. The existing data is limited to small cohorts and description about technical details e.g., accordance between the virtual 3D-model and the real liver anatomy. Randomized controlled trials regarding clinical data or oncological outcome are not available. Up to now there is no intraoperative application of artificial intelligence in liver surgery. The usability of all these sophisticated image guidance tools has still not reached the grade of immersion which would be necessary for a widespread use in the daily surgical routine. Although there are many challenges, augmented reality, mixed reality, 3D-navigation and artificial intelligence are emerging fields in hepato-biliary surgery.
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Affiliation(s)
- Roger Wahba
- Department of General, Visceral, Cancer and Transplantation Surgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50937, Germany
| | - Michael N Thomas
- Department of General, Visceral, Cancer and Transplantation Surgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50937, Germany
| | - Alexander C Bunck
- Department of Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50937, Germany
| | - Christiane J Bruns
- Department of General, Visceral, Cancer and Transplantation Surgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50937, Germany
| | - Dirk L Stippel
- Department of General, Visceral, Cancer and Transplantation Surgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne 50937, Germany
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Fernandes SA, Rossoni C, Koch VW, Imbrizi M, Evangelista-Poderoso R, Pinto LP, Magro DO. Phase angle through electrical bioimpedance as a predictor of cellularity in inflammatory bowel disease. Artif Intell Gastroenterol 2021; 2:111-123. [DOI: 10.35712/aig.v2.i4.111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 06/19/2021] [Accepted: 07/26/2021] [Indexed: 02/06/2023] Open
Abstract
It is estimated in Western industrialized countries that inflammatory bowel disease (IBD) has a prevalence of 1 for every 200 inhabitants. In the past, the fat mass disproportionate increase in relation to the fat-free mass was considered uncommon in patients with IBD, due to the observation of the disease being more common with weight loss and malnutrition. However, more in-depth investigations demonstrate that the fat/lean mass disproportion stands out both in prevalence in patients with new diagnoses of ulcerative colitis or Crohn's disease as well as a factor of poor prognosis to the natural evolution of the disease or to the therapeutic response. Another important aspect associated with obesity in IBD is the increased risk of drug clearance [including anti-tumor necrosis factor (TNF) and anti-integrin agents], resulting in short half-life and low trough drug concentrations, since the levels of TNF secreted by adipocytes sequester anti-TNF agents, which could result in suboptimal response to biologics. In view of these characteristic aspects of the inflammatory process of IBD, the identification of cellular functioning is necessary, which can be associated with the staging of the underlying disease, biochemical parameters, and body composition, helping as an indicator for a more accurate clinical and nutritional conduct.
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Affiliation(s)
- Sabrina A Fernandes
- Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre 90050-170, Brazil
| | - Carina Rossoni
- Institute of Environmental Health (ISAMB), Faculty of Medicine, University of Lisbon, Lisbon 1649-028, Portugal
| | - Vivian W Koch
- Gastroenterology Department, Santa Casa de Misericórdia de Porto Alegre, Porto Alegre 90020-090, Brazil
- Gastroenterology Department, Grupo Hospitalar Conceição, Porto Alegre 91350-250, Brazil
| | - Marcello Imbrizi
- Coloproctology Department, State University of Campinas (UNICAMP-SP), São Paulo 13056-405, Brazil
| | | | - Letícia Pereira Pinto
- Postgraduate Program in Hepatology, Federal University of Health Sciences of Porto Alegre (UFCSPA), Porto Alegre 90050-170, Brazil
| | - Daniéla Oliveira Magro
- Department of Surgery, State University of Campinas (UNICAMP-SP) and Faculty of Medical Sciences (FCM), São Paulo 13056-405, Brazil
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27
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Hardy NP, Dalli J, Mac Aonghusa P, Neary PM, Cahill RA. Biophysics inspired artificial intelligence for colorectal cancer characterization. Artif Intell Gastroenterol 2021; 2:77-84. [DOI: 10.35712/aig.v2.i3.77] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/21/2021] [Accepted: 06/18/2021] [Indexed: 02/06/2023] Open
Abstract
Over the last ten years artificial intelligence (AI) methods have begun to pervade even the most common everyday tasks such as email filtering and mobile banking. While the necessary quality and safety standards may have understandably slowed the introduction of AI to healthcare when compared with other industries, we are now beginning to see AI methods becoming more available to the clinician in select settings. In this paper we discuss current AI methods as they pertain to gastrointestinal procedures including both gastroenterology and gastrointestinal surgery. The current state of the art for polyp detection in gastroenterology is explored with a particular focus on deep leaning, its strengths, as well as some of the factors that may limit its application to the field of surgery. The use of biophysics (utilizing physics to study and explain biological phenomena) in combination with more traditional machine learning is also discussed and proposed as an alternative approach that may solve some of the challenges associated with deep learning. Past and present uses of biophysics inspired AI methods, such as the use of fluorescence guided surgery to aid in the characterization of colorectal lesions, are used to illustrate the role biophysics-inspired AI can play in the exciting future of the gastrointestinal proceduralist.
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Affiliation(s)
- Niall P Hardy
- UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
| | - Jeffrey Dalli
- UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
| | | | - Peter M Neary
- Department of Surgery, University Hospital Waterford, University College Cork, Waterford X91 ER8E, Ireland
| | - Ronan A Cahill
- UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
- Department of Surgery, Mater Misericordiae University Hospital (MMUH), Dublin 7, Ireland
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28
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Almomani A, Hitawala A, Abureesh M, Qapaja T, Alshaikh D, Zmaili M, Saleh MA, Alkhayyat M. Implications of artificial intelligence in inflammatory bowel disease: Diagnosis, prognosis and treatment follow up. Artif Intell Gastroenterol 2021; 2:85-93. [DOI: 10.35712/aig.v2.i3.85] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 05/18/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Driven by the tremendous availability of data, artificial intelligence (AI) using deep learning has emerged as a breakthrough computer technology in the last few decades and has recently been acknowledged by the Task Force on AI as a golden opportunity for research. With its ability to understand, learn from and build on non-linear relationships, AI aims to individualize medical care in an attempt to save time, cost, effort and improve patient’s safety. AI has been applied in multiple medical fields with substantial progress made in gastroenterology mainly to facilitate accurate detection of pathology in different disease processes, among which inflammatory bowel disease (IBD) seems to drag significant attention, specifically by interpreting imaging studies, endoscopic images and videos and -to a lesser extent- disease genomics. Moreover, models have been built to predict IBD occurrence, flare ups, persistence of histological inflammation, disease-related structural abnormalities as well as disease remission. In this article, we will review the applications of AI in IBD in the present medical literature at multiple points of IBD timeline, starting from disease prediction via genomic assessment, diagnostic phase via interpretation of radiological studies and AI-assisted endoscopy, and the role of AI in the evaluation of therapy response and prognosis of IBD patients.
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Affiliation(s)
- Ashraf Almomani
- Department of Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, United States
| | - Asif Hitawala
- Department of Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, United States
| | - Mohammad Abureesh
- Department of Internal Medicine, Staten Island University Hospital, New York City, NY 10305, United States
| | - Thabet Qapaja
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
| | - Dana Alshaikh
- School of Medicine, Mutah University, Alkarak 61710, Jordan
| | - Mohammad Zmaili
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
| | - Mohannad Abou Saleh
- Department of Gastroenterology and Hepatology, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
| | - Motasem Alkhayyat
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, OH 44195, United States
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29
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Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2:69-76. [DOI: 10.35712/aig.v2.i3.69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/09/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.
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Affiliation(s)
- Shihori Tanabe
- Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Edward J Perkins
- Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 3180, United States
| | - Ryuichi Ono
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Hiroki Sasaki
- Department of Clinical Genomics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo 104-0045, Japan
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30
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Yi PS, Hu CJ, Li CH, Yu F. Clinical value of artificial intelligence in hepatocellular carcinoma: Current status and prospect. Artif Intell Gastroenterol 2021; 2:42-55. [DOI: 10.35712/aig.v2.i2.42] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/25/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most commonly diagnosed type of liver cancer and the fourth leading cause of cancer-related mortality worldwide. The early identification of HCC and effective treatments for it have been challenging. Due to the sufficient compensatory ability of early patients and its nonspecific symptoms, HCC is more likely to escape diagnosis in the incipient stage, during which patients can achieve a more satisfying overall survival if they undergo resection or liver transplantation. Patients at advanced stages can profit from radical therapies in a limited way. In order to improve the unfavorable prognosis of HCC, diagnostic ability and treatment efficiency must be improved. The past decade has seen rapid advancements in artificial intelligence, underlying its unique usefulness in almost every field, including that of medicine. Herein, we sought and reviewed studies that put emphasis on artificial intelligence and HCC.
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Affiliation(s)
- Peng-Sheng Yi
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Jun Hu
- Department of Hepato-Biliary-Pancreas II, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Chen-Hui Li
- Department of Obstetrics and Gynecology, Nanchong Traditional Chinese Medicine Hospital, Nanchong 637000, Sichuan Province, China
| | - Fei Yu
- Department of Radiology, Yingshan County People’s Hospital, Nanchong 610041, Sichuan Province, China
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Abstract
Accurate and rapid diagnosis is essential for correct treatment in rectal cancer. Determining the optimal treatment plan for a patient with rectal cancer is a complex process, and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage. In recent years, the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology. Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer. It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician. Prediction algorithms can be standardized by sharing data between centers, diversifying data, and creating big data.
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Affiliation(s)
- Melek Yakar
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir 26040, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir 26040, Turkey
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32
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Chang KP, Lin SH, Chu YW. Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography. Artif Intell Gastroenterol 2021; 2:27-41. [DOI: 10.35712/aig.v2.i2.27] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/21/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI), particularly the deep learning technology, have been proven influential to radiology in the recent decade. Its ability in image classification, segmentation, detection and reconstruction tasks have substantially assisted diagnostic radiology, and has even been viewed as having the potential to perform better than radiologists in some tasks. Gastrointestinal radiology, an important subspecialty dealing with complex anatomy and various modalities including endoscopy, have especially attracted the attention of AI researchers and engineers worldwide. Consequently, recently many tools have been developed for lesion detection and image construction in gastrointestinal radiology, particularly in the fields for which public databases are available, such as diagnostic abdominal magnetic resonance imaging (MRI) and computed tomography (CT). This review will provide a framework for understanding recent advancements of AI in gastrointestinal radiology, with a special focus on hepatic and pancreatobiliary diagnostic radiology with MRI and CT. For fields where AI is less developed, this review will also explain the difficulty in AI model training and possible strategies to overcome the technical issues. The authors’ insights of possible future development will be addressed in the last section.
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Affiliation(s)
- Kai-Po Chang
- PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
- Department of Pathology, China Medical University Hospital, Taichung 40447, Taiwan
| | - Shih-Huan Lin
- PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
| | - Yen-Wei Chu
- PhD Program in Medical Biotechnology, National Chung Hsing University, Taichung 40227, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung 40227, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, Taichung 40227, Taiwan
- Agricultural Biotechnology Center, National Chung Hsing University, Taichung 40227, Taiwan
- Biotechnology Center, National Chung Hsing University, Taichung 40227, Taiwan
- PhD Program in Translational Medicine, National Chung Hsing University, Taichung 40227, Taiwan
- Rong Hsing Research Center for Translational Medicine, Taichung 40227, Taiwan
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33
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Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction. Artif Intell Gastroenterol 2021; 2:56-68. [DOI: 10.35712/aig.v2.i2.56] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 03/31/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.
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Affiliation(s)
- Passisd Laoveeravat
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Priya R Abhyankar
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Aaron R Brenner
- Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Moamen M Gabr
- Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
| | - Fadlallah G Habr
- Division of Gastroenterology, Warren Alpert Medical School of Brown University, Providence, RI 02903, United States
| | - Amporn Atsawarungruangkit
- Division of Gastroenterology, Warren Alpert Medical School of Brown University, Providence, RI 02903, United States
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34
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Kanda T, Sasaki R, Masuzaki R, Moriyama M. Artificial intelligence and machine learning could support drug development for hepatitis A virus internal ribosomal entry sites. Artif Intell Gastroenterol 2021; 2:1-9. [DOI: 10.35712/aig.v2.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/29/2020] [Accepted: 02/12/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatitis A virus (HAV) infection is still an important health issue worldwide. Although several effective HAV vaccines are available, it is difficult to perform universal vaccination in certain countries. Therefore, it may be better to develop antivirals against HAV for the prevention of severe hepatitis A. We found that several drugs potentially inhibit HAV internal ribosomal entry site-dependent translation and HAV replication. Artificial intelligence and machine learning could also support screening of anti-HAV drugs, using drug repositioning and drug rescue approaches.
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Affiliation(s)
- Tatsuo Kanda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Itabashi-ku 173-8610, Tokyo, Japan
| | - Reina Sasaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Itabashi-ku 173-8610, Tokyo, Japan
| | - Ryota Masuzaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Itabashi-ku 173-8610, Tokyo, Japan
| | - Mitsuhiko Moriyama
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Itabashi-ku 173-8610, Tokyo, Japan
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35
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Wang WA, Dong P, Zhang A, Wang WJ, Guo CA, Wang J, Liu HB. Artificial intelligence: A new budding star in gastric cancer. Artif Intell Gastroenterol 2020; 1:60-70. [DOI: 10.35712/aig.v1.i4.60] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 11/01/2020] [Accepted: 11/27/2020] [Indexed: 02/06/2023] Open
Abstract
The pursuit of health has always been the driving force for the advancement of human society, and social development will be profoundly affected by every breakthrough in the medical industry. With the arrival of the information technology revolution era, artificial intelligence (AI) technology has been rapidly developed. AI has been combined with medicine but it has been less studied with gastric cancer (GC). AI is a new budding star in GC, and its contribution to GC is mainly focused on diagnosis and treatment. For early GC, AI’s impact is not only reflected in its high accuracy but also its ability to quickly train primary doctors, improve the diagnosis rate of early GC, and reduce missed cases. At the same time, it will also reduce the possibility of missed diagnosis of advanced GC in cardia. Furthermore, it is used to assist imaging doctors to determine the location of lymph nodes and, more importantly, it can more effectively judge the lymph node metastasis of GC, which is conducive to the prognosis of patients. In surgical treatment of GC, it also has great potential. Robotic surgery is the latest technology in GC surgery. It is a bright star for minimally invasive treatment of GC, and together with laparoscopic surgery, it has become a common treatment for GC. Through machine learning, robotic systems can reduce operator errors and trauma of patients, and can predict the prognosis of GC patients. Throughout the centuries of development of surgery, the history gradually changes from traumatic to minimally invasive. In the future, AI will help GC patients reduce surgical trauma and further improve the efficiency of minimally invasive treatment of GC.
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Affiliation(s)
- Wen-An Wang
- Graduate School, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu Province, China
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
| | - Peng Dong
- Department of General Surgery, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
| | - An Zhang
- Graduate School, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu Province, China
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
| | - Wen-Jie Wang
- Department of General Surgery, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
| | - Chang-An Guo
- Department of Emergency Medicine, Lanzhou University Second Hospital, Lanzhou 730000, Gansu Province, China
| | - Jing Wang
- Graduate School, Gansu University of Traditional Chinese Medicine, Lanzhou 730000, Gansu Province, China
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
| | - Hong-Bin Liu
- Department of General Surgery, The 940th Hospital of Joint Logistics Support Force of Chinese People’s Liberation Army, Lanzhou 730050, Gansu Province, China
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36
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Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1:71-85. [DOI: 10.35712/aig.v1.i4.71] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/28/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.
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Affiliation(s)
- Michihiro Kudou
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Kyoto Okamoto Memorial Hospital, Kyoto 613-0034, Japan
| | - Toshiyuki Kosuga
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Saiseikai Shiga Hospital, Ritto 520-3046, Japan
| | - Eigo Otsuji
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
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37
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Formica V, Morelli C, Riondino S, Renzi N, Nitti D, Roselli M. Artificial intelligence for the study of colorectal cancer tissue slides. Artif Intell Gastroenterol 2020; 1:51-59. [DOI: 10.35712/aig.v1.i3.51] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/25/2020] [Accepted: 09/27/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is gaining incredible momentum as a companion diagnostic in a number of fields in oncology. In the present mini-review, we summarize the main uses and findings of AI applied to the analysis of digital histopathological images of slides from colorectal cancer (CRC) patients. Machine learning tools have been developed to automatically and objectively recognize specific CRC subtypes, such as those with microsatellite instability and high lymphocyte infiltration that would optimally respond to specific therapies. Also, AI-based classification in distinct prognostic groups with no studies of the basic biological features of the tumor have been attempted in a methodological approach that we called “biology-agnostic”.
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Affiliation(s)
- Vincenzo Formica
- Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy
| | - Cristina Morelli
- Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy
| | - Silvia Riondino
- Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy
| | - Nicola Renzi
- Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy
| | - Daniele Nitti
- Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy
| | - Mario Roselli
- Department of Systems Medicine, Medical Oncology Unit, Tor Vergata University Hospital, Rome 00133, Italy
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38
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Feng P, Wang ZD, Fan W, Liu H, Pan JJ. Diagnostic advances of artificial intelligence and radiomics in gastroenterology. Artif Intell Gastroenterol 2020; 1:37-50. [DOI: 10.35712/aig.v1.i2.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 08/22/2020] [Accepted: 08/27/2020] [Indexed: 02/06/2023] Open
Abstract
Traditional medical imaging, including ultrasound, computed tomography, magnetic resonance imaging, or positron emission tomography, remains widely used diagnostic modalities for gastrointestinal diseases at present. These modalities are used to assess changes in morphology, attenuation, signal intensity, and enhancement characteristics. Gastrointestinal tumors, especially malignant tumors, are commonly seen in clinical practice with an increasing number of deaths each year. Because the imaging manifestations of different diseases usually overlap, accurate early diagnosis of tumor lesions, noninvasive and effective evaluation of tumor staging, and prediction of prognosis remain challenging. Fortunately, traditional medical images contain a great deal of important information that cannot be recognized by human eyes but can be extracted by artificial intelligence (AI) technology, which can quantitatively assess the heterogeneity of lesions and provide valuable information, including therapeutic effects and patient prognosis. With the development of computer technology, the combination of medical imaging and AI technology is considered to represent a promising field in medical image analysis. This new emerging field is called “radiomics”, which makes big data mining and extraction from medical imagery possible and can help clinicians make effective decisions and develop personalized treatment plans. Recently, AI and radiomics have been gradually applied to lesion detection, qualitative and quantitative diagnosis, histopathological grading and staging of tumors, therapeutic efficacy assessment, and prognosis evaluation. In this minireview, we briefly introduce the basic principles and technology of radiomics. Then, we review the research and application of AI and radiomics in gastrointestinal diseases, especially diagnostic advancements of radiomics in the differential diagnosis, treatment option, assessment of therapeutic efficacy, and prognosis evaluation of esophageal, gastric, hepatic, pancreatic, and colorectal diseases.
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Affiliation(s)
- Pei Feng
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Zhen-Dong Wang
- Department of Ultrasound, Beijing Sihui Hospital of Traditional Chinese Medicine, Beijing 100022, China
| | - Wei Fan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Heng Liu
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
| | - Jing-Jing Pan
- Department of Radiology, PLA Rocket Force Characteristic Medical Center, Beijing 100088, China
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Lin HM, Xue XF, Wang XG, Dang SC, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. Artif Intell Gastroenterol 2020; 1:19-29. [DOI: 10.35712/aig.v1.i1.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/12/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Pancreatic cancer is a complex cancer of the digestive tract. Diagnosis and treatment can be very difficult because of unclear early symptoms, the deep anatomical location of cancer tissues, and the high degree of cancer cell invasion. The prognosis is extremely poor; the 5-year survival rate of patients with pancreatic cancer is less than 1%. Artificial intelligence (AI) has great potential for application in the medical field. In addition to AI-based applications, such as disease data processing, imaging, and pathological image recognition, robotic surgery has revolutionized surgical procedures. To better understand the current role of AI in pancreatic cancer and predict future development trends, this article comprehensively reports the application of AI to the diagnosis, treatment, and prognosis of pancreatic cancer.
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Affiliation(s)
- Hai-Min Lin
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
| | - Xiao-Fei Xue
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Xiao-Gang Wang
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Sheng-Chun Dang
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Min Gu
- Department of Oncology, Zhenjiang Hospital of Traditional Chinese and Western Medicine, Zhenjiang 212000 Jiangsu Province, China
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Qie YY, Xue XF, Wang XG, Dang SC. Application of artificial intelligence in the diagnosis and prediction of gastric cancer. Artif Intell Gastroenterol 2020; 1:12-18. [DOI: 10.35712/aig.v1.i1.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer is the second leading cause of cancer deaths worldwide. Despite the great progress in the diagnosis and treatment of gastric cancer, the incidence and mortality rate of the disease in China are still relatively high. The high mortality rate of gastric cancer may be related to its low early diagnosis rate and poor prognosis. Much research has been focused on improving the sensitivity and specificity of diagnostic tools for gastric cancer, in order to more accurately predict the survival times of gastric cancer patients. Taking appropriate treatment measures is the key to reducing the mortality rate of gastric cancer. In the past decade, artificial intelligence technology has been applied to various fields of medicine as a branch of computer science. This article discusses the application and research status of artificial intelligence in gastric cancer diagnosis and survival prediction.
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Affiliation(s)
- Yin-Yin Qie
- Department of General Surgery, The Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
| | - Xiao-Fei Xue
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Xiao-Gang Wang
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Sheng-Chun Dang
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
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Balaban DV, Jinga M. Digital histology in celiac disease: A practice changer. Artif Intell Gastroenterol 2020; 1:1-4. [DOI: 10.35712/aig.v1.i1.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/18/2020] [Accepted: 07/20/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has grown tremendously in the last decades and is undoubtedly the future era in medicine. Concerning digestive diseases, applications of AI include clinical gastroenterology, gastrointestinal endoscopy and imaging, and not least pathological diagnosis. Several gastrointestinal pathologies require histological confirmation for a positive diagnosis. Among them, celiac disease (CD) diagnosis has been in the spotlight over time, but controversy is still ongoing with regard to the so-called celiac-type histology. Despite efforts to improve histological diagnosis in CD, there are still several issues and pitfalls associated with duodenal histology reading. Several papers have assessed the accuracy of AI techniques in detecting CD on duodenal biopsy images and have shown high diagnostic performance over standard histology reading. We discuss the role of computer-assisted histology in improving the assessment of mucosal architectural injury and inflammation in CD patients, both for diagnosis and follow-up.
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Affiliation(s)
- Daniel Vasile Balaban
- Internal Medicine and Gastroenterology, Carol Davila University of Medicine and Pharmacy, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest 020021, Romania
| | - Mariana Jinga
- Internal Medicine and Gastroenterology, Carol Davila University of Medicine and Pharmacy, Dr. Carol Davila Central Military Emergency University Hospital, Bucharest 020021, Romania
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Masuzaki R, Kanda T, Sasaki R, Matsumoto N, Nirei K, Ogawa M, Moriyama M. Application of artificial intelligence in hepatology: Minireview. Artif Intell Gastroenterol 2020; 1:5-11. [DOI: 10.35712/aig.v1.i1.5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 06/23/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
With the rapid advancements in computer science, artificial intelligence (AI) has become an intrinsic part of our daily life and clinical practices. The concepts of AI, such as machine learning, deep learning, and big data, are extensively used in clinical and basic research. In this review, we searched for the articles in PubMed and summarized recent developments of AI concerning hepatology while focusing on the diagnosis and risk assessment of liver diseases. Ultrasound is widely conducted for the routine surveillance of hepatocellular carcinoma along with tumor markers. Computer-aided diagnosis is useful in the detection of tumors and characterization of space-occupying lesions. The prognosis of hepatocellular carcinoma can be estimated via AI using large-scale and high-quality training datasets. The prevalence of nonalcoholic fatty liver disease is increasing worldwide and pivotal concern in the field is who will progress and develop hepatocellular carcinoma. Most AI studies require a large dataset, including laboratory or radiological findings and outcome data. AI will be useful in reducing medical errors, supporting clinical decisions, and predicting clinical outcomes. Thus, cooperation between AI and humans is expected to improve healthcare.
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Affiliation(s)
- Ryota Masuzaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Tatsuo Kanda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Reina Sasaki
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Naoki Matsumoto
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Kazushige Nirei
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Masahiro Ogawa
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
| | - Mitsuhiko Moriyama
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo 173-8610, Japan
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Podboy AJ, Scheinker D. Machine learning better predicts colonoscopy duration. Artif Intell Gastroenterol 2020; 1:30-36. [DOI: 10.35712/aig.v1.i1.30] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Revised: 06/15/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The use of machine learning (ML) to predict colonoscopy procedure duration has not been examined.
AIM To assess if ML and data available at the time a colonoscopy procedure is scheduled could be used to estimate procedure duration more accurately than the current practice.
METHODS Total 40168 colonoscopies from the Clinical Outcomes Research Initiative database were collected. ML models predicting procedure duration were developed using data available at time of scheduling. The top performing model was compared against historical practice. Models were evaluated based on accuracy (prediction – actual time) ± 5, 10, and 15 min.
RESULTS ML outperformed historical practice with 77.1% to 68.9%, 87.3% to 79.6%, and 92.1% to 86.8% accuracy at 5, 10 and 15 min thresholds.
CONCLUSION The use of ML to estimate colonoscopy procedure duration may lead to more accurate scheduling.
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Affiliation(s)
- Alexander Joseph Podboy
- Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA 94305, United States
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA 94305, United States
- Department of Preoperative Services, Lucile Packard Children's Hospital Stanford, Stanford, CA 94304, United States
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