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Lee JY, Park J, Lee HJ, Park H, Jin EH, Park K, Baek JE, Yang DH, Hong SW, Kim N, Byeon JS. Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability. J Gastroenterol Hepatol 2024. [PMID: 38766682 DOI: 10.1111/jgh.16618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/06/2024] [Accepted: 05/02/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND AND AIM Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time-consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. METHODS A still image-driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real-world applicability, the model was validated using 113 10-s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2-3)" or "inadequate (BBPS 0-1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter-rater variability was analyzed between the AI model and endoscopists. RESULTS The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. CONCLUSIONS The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large-scale screening programs.
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Affiliation(s)
- Ji Young Lee
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jooyoung Park
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hyo Jeong Lee
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Hana Park
- Health Screening Promotion Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Eun Hyo Jin
- Department of Internal Medicine, Healthcare Research Institute, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Kanggil Park
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Ji Eun Baek
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Dong-Hoon Yang
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seung Wook Hong
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jeong-Sik Byeon
- Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Tiankanon K, Karuehardsuwan J, Aniwan S, Mekaroonkamol P, Sunthornwechapong P, Navadurong H, Tantitanawat K, Mekritthikrai K, Samutrangsi S, Vateekul P, Rerknimitr R. Performance comparison between two computer-aided detection colonoscopy models by trainees using different false positive thresholds: a cross-sectional study in Thailand. Clin Endosc 2024; 57:217-225. [PMID: 38556473 PMCID: PMC10984740 DOI: 10.5946/ce.2023.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/24/2023] [Accepted: 09/25/2023] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND/AIMS This study aims to compare polyp detection performance of "Deep-GI," a newly developed artificial intelligence (AI) model, to a previously validated AI model computer-aided polyp detection (CADe) using various false positive (FP) thresholds and determining the best threshold for each model. METHODS Colonoscopy videos were collected prospectively and reviewed by three expert endoscopists (gold standard), trainees, CADe (CAD EYE; Fujifilm Corp.), and Deep-GI. Polyp detection sensitivity (PDS), polyp miss rates (PMR), and false-positive alarm rates (FPR) were compared among the three groups using different FP thresholds for the duration of bounding boxes appearing on the screen. RESULTS In total, 170 colonoscopy videos were used in this study. Deep-GI showed the highest PDS (99.4% vs. 85.4% vs. 66.7%, p<0.01) and the lowest PMR (0.6% vs. 14.6% vs. 33.3%, p<0.01) when compared to CADe and trainees, respectively. Compared to CADe, Deep-GI demonstrated lower FPR at FP thresholds of ≥0.5 (12.1 vs. 22.4) and ≥1 second (4.4 vs. 6.8) (both p<0.05). However, when the threshold was raised to ≥1.5 seconds, the FPR became comparable (2 vs. 2.4, p=0.3), while the PMR increased from 2% to 10%. CONCLUSION Compared to CADe, Deep-GI demonstrated a higher PDS with significantly lower FPR at ≥0.5- and ≥1-second thresholds. At the ≥1.5-second threshold, both systems showed comparable FPR with increased PMR.
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Affiliation(s)
- Kasenee Tiankanon
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Julalak Karuehardsuwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Satimai Aniwan
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Parit Mekaroonkamol
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | | | - Huttakan Navadurong
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Kittithat Tantitanawat
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Krittaya Mekritthikrai
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Salin Samutrangsi
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
| | - Peerapon Vateekul
- Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai red cross, Bangkok, Thailand
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Danieli MG, Brunetto S, Gammeri L, Palmeri D, Claudi I, Shoenfeld Y, Gangemi S. Machine learning application in autoimmune diseases: State of art and future prospectives. Autoimmun Rev 2024; 23:103496. [PMID: 38081493 DOI: 10.1016/j.autrev.2023.103496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 11/29/2023] [Indexed: 04/30/2024]
Abstract
Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.
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Affiliation(s)
- Maria Giovanna Danieli
- SOS Immunologia delle Malattie Rare e dei Trapianti. AOU delle Marche & Dipartimento di Scienze Cliniche e Molecolari, Università Politecnica delle Marche, via Tronto 10/A, 60126 Torrette di Ancona, Italy; Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy.
| | - Silvia Brunetto
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Luca Gammeri
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Davide Palmeri
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Ilaria Claudi
- Postgraduate School of Allergy and Clinical Immunology, Università Politecnica delle Marche, via Tronto 10/A, 60126 Ancona, Italy
| | - Yehuda Shoenfeld
- Zabludowicz Center for Autoimmune Diseases, Sheba Medical Center, and Reichman University Herzliya, Israel.
| | - Sebastiano Gangemi
- Operative Unit of Allergy and Clinical Immunology, Department of Clinical and Experimental Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy.
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Elshaarawy O, Alboraie M, El-Kassas M. Artificial Intelligence in endoscopy: A future poll. Arab J Gastroenterol 2024; 25:13-17. [PMID: 38220477 DOI: 10.1016/j.ajg.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 09/18/2022] [Accepted: 11/28/2023] [Indexed: 01/16/2024]
Abstract
Artificial Intelligence [AI] has been a trendy topic in recent years, with many developed medical applications. In gastrointestinal endoscopy, AI systems include computer-assisted detection [CADe] for lesion detection as bleedings and polyps and computer-assisted diagnosis [CADx] for optical biopsy and lesion characterization. The technology behind these systems is based on a computer algorithm that is trained for a specific function. This function could be to recognize or characterize target lesions such as colonic polyps. Moreover, AI systems can offer technical assistance to improve endoscopic performance as scope insertion guidance. Currently, we believe that such technologies still lack legal and regulatory validations as a large sector of doctors and patients have concerns. However, there is no doubt that these technologies will bring significant improvement in the endoscopic management of patients as well as save money and time.
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Affiliation(s)
- Omar Elshaarawy
- Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, Egypt; Gastroenterology Department, Royal Liverpool University Hospital, NHS, UK
| | - Mohamed Alboraie
- Department of Internal Medicine, Al-Azhar University, Cairo, Egypt
| | - Mohamed El-Kassas
- Endemic Medicine Department, Faculty of Medicine, Helwan University, Cairo, Egypt.
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Al-Qerem W, Eberhardt J, Jarab A, Al Bawab AQ, Hammad A, Alasmari F, Alazab B, Husein DA, Alazab J, Al-Beool S. Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan. BMC Med Inform Decis Mak 2023; 23:288. [PMID: 38098095 PMCID: PMC10722664 DOI: 10.1186/s12911-023-02403-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 12/11/2023] [Indexed: 12/17/2023] Open
Abstract
INTRODUCTION The integration of Artificial Intelligence (AI) in medical education and practice is a significant development. This study examined the Knowledge, Attitudes, and Practices (KAP) of health professions' students in Jordan concerning AI, providing insights into their preparedness and perceptions. METHODS An online questionnaire was distributed to 483 Jordanian health professions' students via social media. Demographic data, AI-related KAP, and barriers were collected. Quantile regression models analyzed associations between variables and KAP scores. RESULTS Moderate AI knowledge was observed among participants, with specific understanding of data requirements and barriers. Attitudes varied, combining skepticism about AI replacing human teachers with recognition of its value. While AI tools were used for specific tasks, broader integration in medical education and practice was limited. Barriers included lack of knowledge, access, time constraints, and curriculum gaps. CONCLUSIONS This study highlights the need to enhance medical education with AI topics and address barriers. Students need to be better prepared for AI integration, in order to enable medical education to harness AI's potential for improved patient care and training.
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Affiliation(s)
- Walid Al-Qerem
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan.
| | - Judith Eberhardt
- School of Social Sciences, Humanities and Law, Department of Psychology, Teesside University, TS1 3BX, Middlesbrough, UK
| | - Anan Jarab
- College of Pharmacy, Al Ain University, 64141, Abu Dhabi, UAE
- AAU Health and Biomedical Research Center, Al Ain University, 112612, Abu Dhabi, United Arab Emirates
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, 22110, Irbid, Jordan
| | - Abdel Qader Al Bawab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Alaa Hammad
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Fawaz Alasmari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 12372, Riyadh, Saudi Arabia
| | - Badi'ah Alazab
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Daoud Abu Husein
- Department of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah University of Jordan, 11733, Amman, Jordan
| | - Jumana Alazab
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
| | - Saed Al-Beool
- School of Medicine, The University of Jordan, 11910, Amman, Jordan
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Yadav A, Kumar A. Artificial intelligence in rectal cancer: What is the future? Artif Intell Cancer 2023; 4:11-22. [DOI: 10.35713/aic.v4.i2.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 12/07/2023] Open
Abstract
Colorectal cancer (CRC) is the third most prevalent cancer in both men and women, and it is the second leading cause of cancer-related deaths globally. Around 60%-70% of CRC patients are diagnosed at advanced stages, with nearly 20% having liver metastases. It is noteworthy that the 5-year survival rates decline significantly from 80%-90% for localized disease to a mere 10%-15% for patients with metastasis at the time of diagnosis. Early diagnosis, appropriate therapeutic strategy, accurate assessment of treatment response, and prognostication is essential for better outcome. There has been significant technological development in the last couple of decades to improve the outcome of rectal cancer including Artificial intelligence (AI). AI is a broad term used to describe the study of machines that mimic human intelligence, such as perceiving the environment, drawing logical conclusions from observations, and performing complex tasks. At present AI has demonstrated a promising role in early diagnosis, prognosis, and treatment outcomes for patients with rectal cancer, a limited role in surgical decision making, and had a bright future.
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Affiliation(s)
- Alka Yadav
- 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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Keshtkar K, Reza Safarpour A, Heshmat R, Sotoudehmanesh R, Keshtkar A. A Systematic Review and Meta-analysis of Convolutional Neural Network in the Diagnosis of Colorectal Polyps and Cancer. THE TURKISH JOURNAL OF GASTROENTEROLOGY : THE OFFICIAL JOURNAL OF TURKISH SOCIETY OF GASTROENTEROLOGY 2023; 34:985-997. [PMID: 37681266 PMCID: PMC10645297 DOI: 10.5152/tjg.2023.22491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 03/22/2023] [Indexed: 09/09/2023]
Abstract
Convolutional neural networks are a class of deep neural networks used for different clinical purposes, including improving the detection rate of colorectal lesions. This systematic review and meta-analysis aimed to assess the performance of convolutional neural network-based models in the detection or classification of colorectal polyps and colorectal cancer. A systematic search was performed in MEDLINE, SCOPUS, Web of Science, and other related databases. The performance measures of the convolutional neural network models in the detection of colorectal polyps and colorectal cancer were calculated in the 2 scenarios of the best and worst accuracy. Stata and R software were used for conducting the meta-analysis. From 3368 searched records, 24 primary studies were included. The sensitivity and specificity of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged from 84.7% to 91.6% and from 86.0% to 93.8%, respectively. These values in predicting colorectal cancer varied between 93.2% and 94.1% and between 94.6% and 97.7%. The positive and negative likelihood ratios varied between 6.2 and 14.5 and 0.09 and 0.17 in these scenarios, respectively, in predicting colorectal polyps, and 17.1-41.2 and 0.07-0.06 in predicting colorectal polyps. The diagnostic odds ratio and accuracy measures of convolutional neural network models in predicting colorectal polyps in worst and best scenarios ranged between 36% and 162% and between 80.5% and 88.6%, respectively. These values in predicting colorectal cancer in the worst and the best scenarios varied between 239.63% and 677.47% and between 88.2% and 96.4%. The area under the receiver operating characteristic varied between 0.92 and 0.97 in the worst and the best scenarios in colorectal polyps, respectively, and between 0.98 and 0.99 in colorectal polyps prediction. Convolutional neural network-based models showed an acceptable accuracy in detecting colorectal polyps and colorectal cancer.
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Affiliation(s)
- Kamyab Keshtkar
- University of Tehran School of Electrical and Computer Engineering, Tehran, Iran
| | - Ali Reza Safarpour
- Gastroenterohepatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ramin Heshmat
- Chronic Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Rasoul Sotoudehmanesh
- Department of Gastroenterology, Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Abbas Keshtkar
- Department of Health Sciences Education Development, Tehran University of Medical Sciences School of Public Health, Tehran, Iran
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Chen Q, Cai M, Fan X, Liu W, Fang G, Yao S, Xu Y, Li Q, Zhao Y, Zhao K, Liu Z, Chen Z. An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer. BMC Cancer 2023; 23:763. [PMID: 37592224 PMCID: PMC10433587 DOI: 10.1186/s12885-023-11289-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 08/11/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND AND OBJECTIVE In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in predicting the prognosis of colorectal cancer. This study introduces a novel approach based on artificial intelligence (AI) and immunohistochemistry (IHC)-stained whole-slide images (WSIs) of colorectal cancer (CRC) patients to quantitatively assess the spatial associations between tumor cells and immune cells. To achieve this, we employ the Morisita-Horn ecological index (Mor-index), which allows for a comprehensive analysis of the spatial distribution patterns between tumor cells and immune cells within the TME. MATERIALS AND METHODS In this study, we employed a combination of deep learning technology and traditional computer segmentation methods to accurately segment the tumor nuclei, immune nuclei, and stroma nuclei within the tumor regions of IHC-stained WSIs. The Mor-index was used to assess the spatial association between tumor cells and immune cells in TME of CRC patients by obtaining the results of cell nuclei segmentation. A discovery cohort (N = 432) and validation cohort (N = 137) were used to evaluate the prognostic value of the Mor-index for overall survival (OS). RESULTS The efficacy of our method was demonstrated through experiments conducted on two datasets comprising a total of 569 patients. Compared to other studies, our method is not only superior to the QuPath tool but also produces better segmentation results with an accuracy of 0.85. Mor-index was quantified automatically by our method. Survival analysis indicated that the higher Mor-index correlated with better OS in the discovery cohorts (HR for high vs. low 0.49, 95% CI 0.27-0.77, P = 0.0014) and validation cohort (0.21, 0.10-0.46, < 0.0001). CONCLUSION This study provided a novel AI-based approach to segmenting various nuclei in the TME. The Mor-index can reflect the immune status of CRC patients and is associated with favorable survival. Thus, Mor-index can potentially make a significant role in aiding clinical prognosis and decision-making.
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Affiliation(s)
- Qicong Chen
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Ming Cai
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Xinjuan Fan
- Department of Pathology, Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wenbin Liu
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
| | - Gang Fang
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
| | - Su Yao
- Department of Pathology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yao Xu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Qian Li
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Yingnan Zhao
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China
| | - Ke Zhao
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial People's Hospital, Guangdong Cardiovascular Institute, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Zaiyi Liu
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, 510080, China.
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Zhihua Chen
- Institute of Computing Science and Technology, Guangzhou University, No. 230, Outer Ring West Road, Guangzhou, 510006, China.
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Ahmed AA, Brychcy A, Abouzid M, Witt M, Kaczmarek E. Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis-A Cross-Sectional Study. J Pers Med 2023; 13:962. [PMID: 37373951 DOI: 10.3390/jpm13060962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/31/2023] [Accepted: 06/04/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients' diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. METHODS We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v.1.8.2.238, and RStudio Build 351. RESULTS Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57-89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05-20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. CONCLUSION Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis.
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Affiliation(s)
- Alhassan Ali Ahmed
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Agnieszka Brychcy
- Department of Clinical Patomorphology, Heliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Mohamed Abouzid
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Martin Witt
- Department of Anatomy, Rostock University Medical Centre, 18057 Rostock, Germany
- Department of Anatomy, Technische Universität Dresden, 01307 Dresden, Germany
| | - Elżbieta Kaczmarek
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland
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Chin SE, Wan FT, Ladlad J, Chue KM, Teo EK, Lin CL, Foo FJ, Koh FH. One-year review of real-time artificial intelligence (AI)-aided endoscopy performance. Surg Endosc 2023:10.1007/s00464-023-09979-8. [PMID: 36932187 DOI: 10.1007/s00464-023-09979-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 02/21/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND Colonoscopies have long been the gold standard for detection of pre-malignant neoplastic lesions of the colon. Our previous study tried real-time artificial intelligence (AI)-aided colonoscopy over a three-month period and found significant improvements in collective and individual endoscopist's adenoma detection rates compared to baseline. As an expansion, this study evaluates the 1-year performance of AI-aided colonoscopy in the same institution. METHODS A prospective cohort study was conducted in a single institution in Singapore. The AI software used was GI Genius™ Intelligent Endoscopy Module, US-DG-2000309 © 2021 Medtronic. Between July 2021 and June 2022, polypectomy rates in non-AI-aided colonoscopies and AI-aided colonoscopies were calculated and compared. Some of the AI-aided colonoscopies were recorded and video reviewed. A "hit" was defined as a sustained detection of an area by the AI. If a polypectomy was performed for a "hit," its histology was reviewed. Additional calculations for polyp detection rate (PDR), adenoma detection rate (ADR), and adenoma detection per colonoscopy (ADPC) were performed. Cost analysis was performed to determine cost effectiveness of subscription to the AI program. RESULTS 2433 AI-aided colonoscopies were performed between July 2021 and June 2022 and compared against 1770 non-AI-aided colonoscopies. AI-aided colonoscopies yielded significantly higher rates of polypectomies (33.6%) as compared with non-AI-aided colonoscopies (28.4%) (p < 0.001). Among the AI-aided colonoscopies, 1050 were reviewed and a final 843 were included for additional analysis. The polypectomy to "hit" ratio was 57.4%, PDR = 45.6%, ADR = 32.4%, and ADPC = 2.08. Histological review showed that 25 polyps (3.13%) were sessile-serrated adenomas. Cost analysis found that the increased polypectomy rates in AI-aided colonoscopes led to an increase in revenue, which covered the subscription cost with an excess of USD 20,000. CONCLUSION AI-aided colonoscopy is a cost effective means of improving colonoscopy quality and may help advance colorectal cancer screening in Singapore.
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Affiliation(s)
- Shuen-Ern Chin
- Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore, 308232, Singapore
| | - Fang-Ting Wan
- Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore, 308232, Singapore
| | - Jasmine Ladlad
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Koy-Min Chue
- Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Eng-Kiong Teo
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Cui-Li Lin
- Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Fung-Joon Foo
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore
| | - Frederick H Koh
- Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth, 110 Sengkang East Way, Singapore, 544886, Singapore.
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The Role of Artificial Intelligence in Monitoring Inflammatory Bowel Disease-The Future Is Now. Diagnostics (Basel) 2023; 13:diagnostics13040735. [PMID: 36832222 PMCID: PMC9954871 DOI: 10.3390/diagnostics13040735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/17/2023] Open
Abstract
Crohn's disease and ulcerative colitis remain debilitating disorders, characterized by progressive bowel damage and possible lethal complications. The growing number of applications for artificial intelligence in gastrointestinal endoscopy has already shown great potential, especially in the field of neoplastic and pre-neoplastic lesion detection and characterization, and is currently under evaluation in the field of inflammatory bowel disease management. The application of artificial intelligence in inflammatory bowel diseases can range from genomic dataset analysis and risk prediction model construction to the disease grading severity and assessment of the response to treatment using machine learning. We aimed to assess the current and future role of artificial intelligence in assessing the key outcomes in inflammatory bowel disease patients: endoscopic activity, mucosal healing, response to treatment, and neoplasia surveillance.
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12
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The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy. Diagnostics (Basel) 2023; 13:diagnostics13040701. [PMID: 36832189 PMCID: PMC9955100 DOI: 10.3390/diagnostics13040701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 01/30/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area. METHODS A prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group). RESULTS None of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies. CONCLUSIONS Based on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.
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Goenka MK, Afzalpurkar S, Jejurikar S, Rodge GA, Tiwari A. Role of artificial intelligence-guided esophagogastroduodenoscopy in assessing the procedural completeness and quality. Indian J Gastroenterol 2023; 42:128-135. [PMID: 36715841 DOI: 10.1007/s12664-022-01294-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 08/12/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS The quality of esophagogastroduodenoscopy (EGD) can have great impact on the detection of esophageal and gastric lesions, including malignancies. The aim of the study is to investigate the use of artificial intelligence (AI) during EGD by the endoscopists-in-training so that a real-time feedback can be provided, ensuring compliance to a pre-decided protocol for examination. METHODS This is an observational pilot study. The videos of the EGD procedure performed between August 1, 2021, and September 30, 2021, were prospectively analyzed using AI system. The assessment of completeness of the procedure was done based on the visualizsation of pre-defined 29 locations. Endoscopists were divided into two categories - whether they are in the training period (category A) or have competed their endoscopy training (category B). RESULTS A total of 277 procedures, which included 114 category-A and 163 category-B endoscopists, respectively, were included. Most commonly covered areas by the endoscopists were greater curvature of antrum (97.47%), second part of duodenum (96.75%), other parts of antrum such as the anterior, lesser curvature and the posterior aspect (96.75%, 94.95%, and 94.22%, respectively). Commonly missed or inadequately seen areas were vocal cords (99.28%), epiglottis (93.14%) and posterior, anterior, and lateral aspect of incisura (78.70%, 73.65%, and 73.53%, respectively). The good quality procedures were done predominantly by categoryB endoscopists (88.68% vs. 11.32%, p < 0.00001). CONCLUSION AI can play an important role in assessing the quality and completeness of EGD and can be a part of training of endoscopy in future.
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Affiliation(s)
- Mahesh Kumar Goenka
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India.
| | - Shivaraj Afzalpurkar
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | | | - Gajanan Ashokrao Rodge
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
| | - Awanish Tiwari
- Institute of Gastrosciences and Liver, Apollo Multispeciality Hospitals, Kolkata, Day Care Building, 4th Floor, AMHL, EM Bypass Road, Kolkata, 700 054, India
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14
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Wang P, Liu XG, Kang M, Peng X, Shu ML, Zhou GY, Liu PX, Xiong F, Deng MM, Xia HF, Li JJ, Long XQ, Song Y, Li LP. Artificial intelligence empowers the second-observer strategy for colonoscopy: a randomized clinical trial. Gastroenterol Rep (Oxf) 2023; 11:goac081. [PMID: 36686571 PMCID: PMC9850273 DOI: 10.1093/gastro/goac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 01/21/2023] Open
Abstract
Background In colonoscopy screening for colorectal cancer, human vision limitations may lead to higher miss rate of lesions; artificial intelligence (AI) assistance has been demonstrated to improve polyp detection. However, there still lacks direct evidence to demonstrate whether AI is superior to trainees or experienced nurses as a second observer to increase adenoma detection during colonoscopy. In this study, we aimed to compare the effectiveness of assistance from AI and human observer during colonoscopy. Methods A prospective multicenter randomized study was conducted from 2 September 2019 to 29 May 2020 at four endoscopy centers in China. Eligible patients were randomized to either computer-aided detection (CADe)-assisted group or observer-assisted group. The primary outcome was adenoma per colonoscopy (APC). Secondary outcomes included polyp per colonoscopy (PPC), adenoma detection rate (ADR), and polyp detection rate (PDR). We compared continuous variables and categorical variables by using R studio (version 3.4.4). Results A total of 1,261 (636 in the CADe-assisted group and 625 in the observer-assisted group) eligible patients were analysed. APC (0.42 vs 0.35, P = 0.034), PPC (1.13 vs 0.81, P < 0.001), PDR (47.5% vs 37.4%, P < 0.001), ADR (25.8% vs 24.0%, P = 0.464), the number of detected sessile polyps (683 vs 464, P < 0.001), and sessile adenomas (244 vs 182, P = 0.005) were significantly higher in the CADe-assisted group than in the observer-assisted group. False detections of the CADe system were lower than those of the human observer (122 vs 191, P < 0.001). Conclusions Compared with the human observer, the CADe system may improve the clinical outcome of colonoscopy and reduce disturbance to routine practice (Chictr.org.cn No.: ChiCTR1900025235).
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Affiliation(s)
| | | | - Min Kang
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Xue Peng
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Mei-Ling Shu
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Guan-Yu Zhou
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Pei-Xi Liu
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Fei Xiong
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Ming-Ming Deng
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Hong-Fen Xia
- Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, P. R. China
| | - Jian-Jun Li
- Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing, P. R. China
| | - Xiao-Qi Long
- Department of Gastroenterology, Suining Central Hospital, Suining, Sichuan, P. R. China
| | - Yan Song
- Department of Gastroenterology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, P. R. China
| | - Liang-Ping Li
- Corresponding author. Department of Gastroenterology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, No.32 West Second Section, First Ring Road, Chengdu, Sichuan 610072, China. Tel: +86-28-8739 3927;
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15
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Koh FH, Ladlad J, Teo EK, Lin CL, Foo FJ. Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore. Surg Endosc 2023; 37:165-171. [PMID: 35882667 PMCID: PMC9321269 DOI: 10.1007/s00464-022-09470-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 07/10/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Colonoscopy is a mainstay to detect premalignant neoplastic lesions in the colon. Real-time Artificial Intelligence (AI)-aided colonoscopy purportedly improves the polyp detection rate, especially for small flat lesions. The aim of this study is to evaluate the performance of real-time AI-aided colonoscopy in the detection of colonic polyps. METHODS A prospective single institution cohort study was conducted in Singapore. All real-time AI-aided colonoscopies, regardless of indication, performed by specialist-grade endoscopists were anonymously recorded from July to September 2021 and reviewed by 2 independent authors (FHK, JL). Sustained detection of an area by the program was regarded as a "hit". Histology for the polypectomies were reviewed to determine adenoma detection rate (ADR). Individual endoscopist's performance with AI were compared against their baseline performance without AI endoscopy. RESULTS A total of 24 (82.8%) endoscopists participated with 18 (62.1%) performing ≥ 5 AI-aided colonoscopies. Of the 18, 72.2% (n = 13) were general surgeons. During that 3-months period, 487 "hits" encountered in 298 colonoscopies. Polypectomies were performed for 51.3% and 68.4% of these polypectomies were adenomas on histology. The post-intervention median ADR was 30.4% was higher than the median baseline polypectomy rate of 24.3% (p = 0.02). Of the adenomas excised, 14 (5.6%) were sessile serrated adenomas. Of those who performed ≥ 5 AI-aided colonoscopies, 13 (72.2%) had an improvement of ADR compared to their polypectomy rate before the introduction of AI, of which 2 of them had significant improvement. CONCLUSIONS Real-time AI-aided colonoscopy have the potential to improved ADR even for experienced endoscopists and would therefore, improve the quality of colonoscopy.
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Affiliation(s)
- Frederick H. Koh
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | - Jasmine Ladlad
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore
| | | | - Eng-Kiong Teo
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Cui-Li Lin
- grid.508163.90000 0004 7665 4668Department of Gastroenterology and Hepatology, Sengkang General Hospital, SingHealth Services, Singapore, Singapore
| | - Fung-Joon Foo
- grid.508163.90000 0004 7665 4668Colorectal Service, Department of General Surgery, Sengkang General Hospital, SingHealth Services, 110 Sengkang East Way, Singapore, 544886 Singapore ,grid.508163.90000 0004 7665 4668Endoscopy Centre, Division of Hyperacute Care, Sengkang General Hospital, Singapore, Singapore
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16
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Li MD, Huang ZR, Shan QY, Chen SL, Zhang N, Hu HT, Wang W. Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps. BMC Gastroenterol 2022; 22:517. [PMID: 36513975 PMCID: PMC9749329 DOI: 10.1186/s12876-022-02605-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 12/05/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience. METHODS We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria. The random-effects model was calculated using Meta-DISC 1.4 and RevMan 5.3. RESULTS A total of 16 studies were included for meta-analysis. Only one study (1/16) presented externally validated results. The area under the curve (AUC) of AI group, expert group and non-expert group for detection and classification of colonic polyps were 0.940, 0.918, and 0.871, respectively. AI group had slightly lower pooled specificity than the expert group (79% vs. 86%, P < 0.05), but the pooled sensitivity was higher than the expert group (88% vs. 80%, P < 0.05). While the non-experts had less pooled specificity in polyp recognition than the experts (81% vs. 86%, P < 0.05), and higher pooled sensitivity than the experts (85% vs. 80%, P < 0.05). CONCLUSION The performance of AI in polyp detection and classification is similar to that of human experts, with high sensitivity and moderate specificity. Different tasks may have an impact on the performance of deep learning models and human experts, especially in terms of sensitivity and specificity.
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Affiliation(s)
- Ming-De Li
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ze-Rong Huang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Quan-Yuan Shan
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Shu-Ling Chen
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Ning Zhang
- grid.412615.50000 0004 1803 6239Department of Gastroenterology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-Tong Hu
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
| | - Wei Wang
- grid.412615.50000 0004 1803 6239Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080 People’s Republic of China
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Rondonotti E, Di Paolo D, Rizzotto ER, Alvisi C, Buscarini E, Spadaccini M, Tamanini G, Paggi S, Amato A, Scardino G, Romeo S, Alicante S, Ancona F, Guido E, Marzo V, Chicco F, Agazzi S, Rosa C, Correale L, Repici A, Hassan C, Radaelli F. Efficacy of a computer-aided detection system in a fecal immunochemical test-based organized colorectal cancer screening program: a randomized controlled trial (AIFIT study). Endoscopy 2022; 54:1171-1179. [PMID: 35545122 DOI: 10.1055/a-1849-6878] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND Computer-aided detection (CADe) increases adenoma detection in primary screening colonoscopy. The potential benefit of CADe in a fecal immunochemical test (FIT)-based colorectal cancer (CRC) screening program is unknown. This study assessed whether use of CADe increases the adenoma detection rate (ADR) in a FIT-based CRC screening program. METHODS In a multicenter, randomized trial, FIT-positive individuals aged 50-74 years undergoing colonoscopy, were randomized (1:1) to receive high definition white-light (HDWL) colonoscopy, with or without a real-time deep-learning CADe by endoscopists with baseline ADR > 25 %. The primary outcome was ADR. Secondary outcomes were mean number of adenomas per colonoscopy (APC) and advanced adenoma detection rate (advanced-ADR). Subgroup analysis according to baseline endoscopists' ADR (≤ 40 %, 41 %-45 %, ≥ 46 %) was also performed. RESULTS 800 individuals (median age 61.0 years [interquartile range 55-67]; 409 men) were included: 405 underwent CADe-assisted colonoscopy and 395 underwent HDWL colonoscopy alone. ADR and APC were significantly higher in the CADe group than in the HDWL arm: ADR 53.6 % (95 %CI 48.6 %-58.5 %) vs. 45.3 % (95 %CI 40.3 %-50.45 %; RR 1.18; 95 %CI 1.03-1.36); APC 1.13 (SD 1.54) vs. 0.90 (SD 1.32; P = 0.03). No significant difference in advanced-ADR was found (18.5 % [95 %CI 14.8 %-22.6 %] vs. 15.9 % [95 %CI 12.5 %-19.9 %], respectively). An increase in ADR was observed in all endoscopist groups regardless of baseline ADR. CONCLUSIONS Incorporating CADe significantly increased ADR and APC in the framework of a FIT-based CRC screening program. The impact of CADe appeared to be consistent regardless of endoscopist baseline ADR.
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Affiliation(s)
| | - Dhanai Di Paolo
- Gastroenterology Unit, Valduce Hospital, Como, Italy.,Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Department of Gastroenterology and Hepatology, Milan, Italy
| | - Erik Rosa Rizzotto
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | | | | | - Marco Spadaccini
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | | | - Silvia Paggi
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | - Arnaldo Amato
- Gastroenterology Unit, Valduce Hospital, Como, Italy
| | | | - Samanta Romeo
- Gastroenterology Unit, Azienda Ospedaliera "Ospedale Maggiore", Crema, Italy
| | - Saverio Alicante
- Gastroenterology Unit, Azienda Ospedaliera "Ospedale Maggiore", Crema, Italy
| | - Fabio Ancona
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | - Ennio Guido
- Gastroenterology Unit, St. Antonio Hospital, Azienda Ospedaliera Universitaria, Padova, Italy
| | | | - Fabio Chicco
- USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
| | | | - Cesare Rosa
- USD Endoscopia Digestiva, ASST Pavia, Pavia, Italy
| | - Loredana Correale
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy
| | - Alessandro Repici
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
| | - Cesare Hassan
- Department of Biomedical Sciences, Humanitas University, Rozzano, Milan, Italy.,Endoscopy Unit, Humanitas Clinical and Research Center IRCCS, Rozzano, Milan, Italy
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Doumat G, Daher D, Ghanem NN, Khater B. Knowledge and attitudes of medical students in Lebanon toward artificial intelligence: A national survey study. Front Artif Intell 2022; 5:1015418. [PMID: 36406470 PMCID: PMC9668059 DOI: 10.3389/frai.2022.1015418] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 11/07/2022] Open
Abstract
Purpose This study assesses the knowledge and attitudes of medical students in Lebanon toward Artificial Intelligence (AI) in medical education. It also explores the students' perspectives regarding the role of AI in medical education as a subject in the curriculum and a teaching tool. Methods This is a cross-sectional study using an online survey consisting of close-ended questions. The survey targets medical students at all medical levels across the 7 medical schools in Lebanon. Results A total of 206 medical students responded. When assessing AI knowledge sources (81.1%) got their information from the media as compared to (9.7%) from medical school curriculum. However, Students who learned the basics of AI as part of the medical school curriculum were more knowledge about AI than their peers who did not. Students in their clinical years appear to be more knowledgeable about AI in medicine. The advancements in AI affected the choice of specialty of around a quarter of the students (26.8%). Finally, only a quarter of students (26.5%) want to be assessed by AI, even though the majority (57.7%) reported that assessment by AI is more objective. Conclusions Education about AI should be incorporated in the medical school curriculum to improve the knowledge and attitudes of medical students. Improving AI knowledge in medical students will in turn increase acceptance of AI as a tool in medical education, thus unlocking its potential in revolutionizing medical education.
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Affiliation(s)
- George Doumat
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Darine Daher
- Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Nadim-Nicolas Ghanem
- Department of Family Medicine, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
| | - Beatrice Khater
- Department of Family Medicine, Faculty of Medicine, American University of Beirut, Beirut, Lebanon
- *Correspondence: Beatrice Khater
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An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis. Sci Rep 2022; 12:11115. [PMID: 35778456 PMCID: PMC9249895 DOI: 10.1038/s41598-022-14605-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/09/2022] [Indexed: 11/17/2022] Open
Abstract
The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.
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20
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Tang CP, Lin TL, Hsieh YH, Hsieh CH, Tseng CW, Leung FW. Polyp detection and false-positive rates by computer-aided analysis of withdrawal-phase videos of colonoscopy of the right-sided colon segment in a randomized controlled trial comparing water exchange and air insufflation. Gastrointest Endosc 2022; 95:1198-1206.e6. [PMID: 34973967 DOI: 10.1016/j.gie.2021.12.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/17/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Water exchange (WE) improves lesion detection but misses polyps because of human limitations. Computer-aided detection (CADe) identifies additional polyps overlooked by the colonoscopist. Additional polyp detection rate (APDR) is the proportion of patients with at least 1 additional polyp detected by CADe. The number of false positives (because of feces and air bubble) per colonoscopy (FPPC) is a major CADe limitation, which might be reduced by salvage cleaning with WE. We compared the APDR and FPPC by CADe between videos of WE and air insufflation in the right-sided colon. METHODS CADe used a convolutional neural network with transfer learning. We edited and coded withdrawal-phase videos in a randomized controlled trial that compared right-sided colon findings between air insufflation and WE. Two experienced blinded endoscopists analyzed the CADe-overlaid videos and identified additional polyps by consensus. An artifact triggered by CADe but not considered a polyp by the reviewers was defined as a false positive. The primary outcome was APDR. RESULTS Two hundred forty-five coded videos of colonoscopies inserted with WE (n = 123) and air insufflation (n = 122) methods were analyzed. The APDR in the WE group was significantly higher (37 [30.1%] vs 15 [12.3%], P = .001). The mean [standard deviation] FPPC related to feces (1.78 [1.67] vs 2.09 [2.09], P = .007) and bubbles (.53 [.89] vs 1.25 [2.45], P = .001) in the WE group were significantly lower. CONCLUSIONS CADe showed significantly higher APDR and lower number of FPPC related to feces and bubbles in the WE group. The results support the hypothesis that the strengths of CADe and WE complement the weaknesses of each other in optimizing polyp detection.
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Affiliation(s)
- Chia-Pei Tang
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan; School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Tu-Liang Lin
- Department of Management Information Systems, National Chiayi University, Chiayi, Taiwan
| | - Yu-Hsi Hsieh
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan; School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Chen-Hung Hsieh
- Department of Management Information Systems, National Chiayi University, Chiayi, Taiwan
| | - Chih-Wei Tseng
- Division of Gastroenterology, Department of Internal Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi, Taiwan; School of Medicine, Tzu Chi University, Hualien City, Taiwan
| | - Felix W Leung
- Sepulveda Ambulatory Care Center, Veterans Affairs Greater Los Angeles Healthcare System, North Hills, California, USA; David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA
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21
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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] [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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22
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Mutaguchi J, Morooka KI, Kobayashi S, Umehara A, Miyauchi S, Kinoshita F, Inokuchi J, Oda Y, Kurazume R, Eto M. Artificial intelligence for segmentation of bladder tumor cystoscopic images performed by U-Net with dilated convolution. J Endourol 2022; 36:827-834. [PMID: 35018828 DOI: 10.1089/end.2021.0483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Early intravesical recurrence after transurethral resection of bladder tumors (TURBT) is often caused by overlooking of tumors during TURBT. Although narrow-band imaging and photodynamic diagnosis were developed to detect more tumors than conventional white-light imaging, the accuracy of these systems has been subjective, along with poor reproducibility due to their dependence on the physician's experience and skills. To create an objective and reproducible diagnosing system, we aimed to assess the utility of artificial intelligence (AI) with Dilated U-Net to reduce the risk of overlooked bladder tumors when compared with the conventional AI system, termed U-Net. MATERIAL AND METHODS We retrospectively obtained cystoscopic images by converting videos obtained from 120 patients who underwent TURBT into 1,790 cystoscopic images. The Dilated U-Net, which is an extension of the conventional U-Net, analyzed these image datasets. The diagnostic accuracy of the Dilated U-Net and conventional U-Net were compared using the following four measurements: pixel-wise sensitivity (PWSe); pixel-wise specificity (PWSp); pixel-wise positive predictive value (PWPPV), representing the AI diagnostic accuracy per pixel; and dice similarity coefficient (DSC), representing the overlap area between the bladder tumors in the ground truth images and segmentation maps. RESULTS The cystoscopic images were divided as follows, according to the pathological T-stage: 944, Ta; 412, T1; 329, T2; and 116, carcinoma in-situ. The PWSe, PWSp, PWPPV, and DSC of the Dilated U-Net were 84.9%, 88.5%, 86.7%, and 83.0%, respectively, which had improved when compared to that with the conventional U-Net by 1.7%, 1.3%, 2.1%, and 2.3%, respectively. The DSC values were high for elevated lesions and low for flat lesions for both Dilated and conventional U-Net. CONCLUSIONS Dilated U-Net, with higher DSC values than conventional U-Net, might reduce the risk of overlooking bladder tumors during cystoscopy and TURBT.
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Affiliation(s)
- Jun Mutaguchi
- Kyushu University Hospital, 145181, Urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Advanced Medical Initiatives Faculty of Medical Sciences, Fukuoka, Japan;
| | - Ken Ichi Morooka
- Okayama University, 12997, Graduate School of Natural Science and Technology, Okayama, Japan;
| | - Satoshi Kobayashi
- Kyushu University Hospital, 145181, urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Advanced Medical Initiatives Faculty of Medical Sciences, Fukuoka, Japan;
| | - Aiko Umehara
- Kyushu University, 12923, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan;
| | - Shoko Miyauchi
- Kyushu University, 12923, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan;
| | - Fumio Kinoshita
- Kyushu University Hospital, 145181, Urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Anatomic Pathology, Graduate School of Medical Sciences, Fukuoka, Japan;
| | | | - Yoshinao Oda
- Kyushu University Hospital, 145181, Anatomic Pathology, Graduate School of Medical Sciences, Fukuoka, Japan;
| | - Ryo Kurazume
- Kyushu University, 12923, Graduate School of Information Science and Electrical Engineering, Fukuoka, Japan;
| | - Masatoshi Eto
- Kyushu University Hospital, 145181, Urology, Fukuoka, Japan.,Kyushu University Hospital, 145181, Advanced Medical Initiatives Faculty of Medical Sciences, Fukuoka, Japan;
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Glissen Brown JR, Waljee AK, Mori Y, Sharma P, Berzin TM. Charting a path forward for clinical research in artificial intelligence and gastroenterology. Dig Endosc 2022; 34:4-12. [PMID: 33715244 DOI: 10.1111/den.13974] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/02/2021] [Accepted: 03/11/2021] [Indexed: 12/12/2022]
Abstract
Gastroenterology has been an early leader in bridging the gap between artificial intelligence (AI) model development and clinical trial validation, and in recent years we have seen the publication of several randomized clinical trials examining the role of AI in gastroenterology. As AI applications for clinical medicine advance rapidly, there is a clear need for guidance surrounding AI-specific study design, evaluation, comparison, analysis and reporting of results. Several initiatives are in the publication or pre-publication phase including AI-specific amendments to minimum reporting guidelines for clinical trials, society task force initiatives aimed at priority use cases and research priorities, and minimum reporting guidelines that guide the reporting of clinical prediction models. In this paper, we examine applications of AI in clinical trials and discuss elements of newly published AI-specific extensions to the Consolidated Standards of Reporting Trials and Standard Protocol Items: Recommendations for Interventional Trials statements that guide clinical trial reporting and development. We then review AI applications at the pre-trial level in both endoscopy and other subfields of gastroenterology and explore areas where further guidance is needed to supplement the current guidance available at the pre-trial level.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
| | - Akbar K Waljee
- Division of Gastroenterology, University of Michigan Health System, University of Michigan, Ann Arbor, USA
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Kanagawa, Japan.,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, USA
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, USA
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24
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AIM in Endoscopy Procedures. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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25
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Ayyaz MS, Lali MIU, Hussain M, Rauf HT, Alouffi B, Alyami H, Wasti S. Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos. Diagnostics (Basel) 2021; 12:diagnostics12010043. [PMID: 35054210 PMCID: PMC8775223 DOI: 10.3390/diagnostics12010043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.
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Affiliation(s)
- M Shahbaz Ayyaz
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan; (M.S.A.); (M.H.)
| | - Muhammad Ikram Ullah Lali
- Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan; (M.I.U.L.); (S.W.)
| | - Mubbashar Hussain
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan; (M.S.A.); (M.H.)
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
- Correspondence:
| | - Bader Alouffi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia; (B.A.); (H.A.)
| | - Hashem Alyami
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia; (B.A.); (H.A.)
| | - Shahbaz Wasti
- Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan; (M.I.U.L.); (S.W.)
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Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience. Eur J Gastroenterol Hepatol 2021; 33:e662-e669. [PMID: 34034272 PMCID: PMC8734627 DOI: 10.1097/meg.0000000000002209] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AIM The use of artificial intelligence represents an objective approach to increase endoscopist's adenoma detection rate (ADR) and limit interoperator variability. In this study, we evaluated a newly developed deep convolutional neural network (DCNN) for automated detection of colorectal polyps ex vivo as well as in a first in-human trial. METHODS For training of the DCNN, 116 529 colonoscopy images from 278 patients with 788 different polyps were collected. A subset of 10 467 images containing 504 different polyps were manually annotated and treated as the gold standard. An independent set of 45 videos consisting of 15 534 single frames was used for ex vivo performance testing. In vivo real-time detection of colorectal polyps during routine colonoscopy by the DCNN was tested in 42 patients in a back-to-back approach. RESULTS When analyzing the test set of 15 534 single frames, the DCNN's sensitivity and specificity for polyp detection and localization within the frame was 90% and 80%, respectively, with an area under the curve of 0.92. In vivo, baseline polyp detection rate and ADR were 38% and 26% and significantly increased to 50% (P = 0.023) and 36% (P = 0.044), respectively, with the use of the DCNN. Of the 13 additionally with the DCNN detected lesions, the majority were diminutive and flat, among them three sessile serrated adenomas. CONCLUSION This newly developed DCNN enables highly sensitive automated detection of colorectal polyps both ex vivo and during first in-human clinical testing and could potentially increase the detection of colorectal polyps during colonoscopy.
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27
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The emerging role of artificial intelligence in gastrointestinal endoscopy: A review. GASTROENTEROLOGIA Y HEPATOLOGIA 2021; 45:492-497. [PMID: 34793895 DOI: 10.1016/j.gastrohep.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 10/15/2021] [Accepted: 11/07/2021] [Indexed: 11/19/2022]
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28
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Deliwala SS, Hamid K, Barbarawi M, Lakshman H, Zayed Y, Kandel P, Malladi S, Singh A, Bachuwa G, Gurvits GE, Chawla S. Artificial intelligence (AI) real-time detection vs. routine colonoscopy for colorectal neoplasia: a meta-analysis and trial sequential analysis. Int J Colorectal Dis 2021; 36:2291-2303. [PMID: 33934173 DOI: 10.1007/s00384-021-03929-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 02/04/2023]
Abstract
GOALS AND BACKGROUND Studies analyzing artificial intelligence (AI) in colonoscopies have reported improvements in detecting colorectal cancer (CRC) lesions, however its utility in the realworld remains limited. In this systematic review and meta-analysis, we evaluate the efficacy of AI-assisted colonoscopies against routine colonoscopy (RC). STUDY We performed an extensive search of major databases (through January 2021) for randomized controlled trials (RCTs) reporting adenoma and polyp detection rates. Odds ratio (OR) and standardized mean differences (SMD) with 95% confidence intervals (CIs) were reported. Additionally, trial sequential analysis (TSA) was performed to guard against errors. RESULTS Six RCTs were included (4996 participants). The mean age (SD) was 51.99 (4.43) years, and 49% were females. Detection rates favored AI over RC for adenomas (OR 1.77; 95% CI: 1.570-2.08) and polyps (OR 1.91; 95% CI: 1.68-2.16). Secondary outcomes including mean number of adenomas (SMD 0.23; 95% CI: 0.18-0.29) and polyps (SMD 0.23; 95% CI: 0.17-0.29) detected per procedure favored AI. However, RC outperformed AI in detecting pedunculated polyps. Withdrawal times (WTs) favored AI when biopsies were included, while WTs without biopsies, cecal intubation times, and bowel preparation adequacy were similar. CONCLUSIONS Colonoscopies equipped with AI detection algorithms could significantly detect previously missed adenomas and polyps while retaining the ability to self-assess and improve periodically. More effective clearance of diminutive adenomas may allow lengthening in surveillance intervals, reducing the burden of surveillance colonoscopies, and increasing its accessibility to those at higher risk. TSA ruled out the risk for false-positive results and confirmed a sufficient sample size to detect the observed effect. Currently, these findings suggest that AI-assisted colonoscopy can serve as a useful proxy to address critical gaps in CRC identification.
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Affiliation(s)
- Smit S Deliwala
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA.
| | - Kewan Hamid
- Department of Internal Medicine/Pediatrics, Michigan State University at Hurley Medical Center, Flint, MI, USA
| | - Mahmoud Barbarawi
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Harini Lakshman
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Yazan Zayed
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Pujan Kandel
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Srikanth Malladi
- Department of Internal Medicine/Pediatrics, Michigan State University at Hurley Medical Center, Flint, MI, USA
| | - Adiraj Singh
- Department of Internal Medicine/Pediatrics, Michigan State University at Hurley Medical Center, Flint, MI, USA
| | - Ghassan Bachuwa
- Department of Internal Medicine, Michigan State University at Hurley Medical Center, Two Hurley Plaza, Ste 212, Flint, MI, 48503, USA
| | - Grigoriy E Gurvits
- Department of Internal Medicine - Division of Gastroenterology, New York University/Langone Medical Center, New York, NY, USA
| | - Saurabh Chawla
- Department of Internal Medicine - Division of Gastroenterology, Emory University, Atlanta, GA, USA
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Zhu XW, Yan J, He YL, Liu G, Li X. Application of deep learning based artificial intelligence technology in identification of colorectal polyps. Shijie Huaren Xiaohua Zazhi 2021; 29:1201-1206. [DOI: 10.11569/wcjd.v29.i20.1201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is a cancer type that is most suitable for screening since subjects at risk of this malignancy can clearly benefit from colonoscopy screening. In 2017, there were about 431951 new cases of colorectal cancer in China, with an increase of 203.5% in 28 years. Early detection and early removal of adenomatous polyps and other precancerous lesions during colonoscopy can prevent the occurrence of colorectal cancer. However, various factors lead to missed diagnosis of polyps during colonoscopy, which increases the risk of colorectal cancer. In recent years, with the rapid development of artificial intelligence technology in the medical field, colonoscopy assisted by artificial intelligence can increase the detection rate of polyps and improve the quality of colonoscopy. This paper mainly reviews the quality control, bowel preparation, diagnosis and classification of colorectal polyps, and the future opportunities and challenges faced by convolutional neural network based artificial intelligence technology in the field of colonoscopy, hoping to provide some reference for clinical work.
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Affiliation(s)
- Xing-Wang Zhu
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Jun Yan
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Ying-Li He
- Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
| | - Gang Liu
- Lanzhou University School of Information Science & Engineering, Lanzhou 730000, Gansu Province, China
| | - Xun Li
- The First Clinical Medical College of Lanzhou University, Lanzhou 730000, Gansu Province, China,Gansu Province Key Laboratory of Biological Therapy and Regenerative Medicine, Lanzhou 730000, Gansu Province, China,Cancer Prevention and Treatment Center of Lanzhou University School of Medicine, Lanzhou 730000, Gansu Province, China,Gansu Provincial Institute of Hepatobiliary and Pancreatic Surgery, Lanzhou 730000, Gansu Province, China,Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, Gansu Province, China
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Viscaino M, Torres Bustos J, Muñoz P, Auat Cheein C, Cheein FA. Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions. World J Gastroenterol 2021; 27:6399-6414. [PMID: 34720530 PMCID: PMC8517786 DOI: 10.3748/wjg.v27.i38.6399] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 04/26/2021] [Accepted: 09/14/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants. It can be prevented if glandular tissue (adenomatous polyps) is detected early. Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps. However, it has some limitations that include the high polyp miss rate for smaller (< 10 mm) or flat polyps, which are easily missed during visual inspection. Due to the rapid advancement of technology, artificial intelligence (AI) has been a thriving area in different fields, including medicine. Particularly, in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC. This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps, as well as an insightful analysis of the main advantages and misconceptions in the field.
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Affiliation(s)
- Michelle Viscaino
- Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria, Valpaiso 2340000, Chile
| | - Javier Torres Bustos
- Department of Electronic Engineering, Universidad Tecnica Federico Santa Maria, Valpaiso 2340000, Chile
| | - Pablo Muñoz
- Hospital Clinico, University of Chile, Santiago 8380456, Chile
| | - Cecilia Auat Cheein
- Facultad de Medicina, Universidad Nacional de Santiago del Estero, Santiago del Estero 4200, Argentina
| | - Fernando Auat Cheein
- Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaiso 2340000, Chile
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31
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Smart pills for gastrointestinal diagnostics and therapy. Adv Drug Deliv Rev 2021; 177:113931. [PMID: 34416311 DOI: 10.1016/j.addr.2021.113931] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 08/03/2021] [Accepted: 08/13/2021] [Indexed: 12/13/2022]
Abstract
Ingestible smart pills have the potential to be a powerful clinical tool in the diagnosis and treatment of gastrointestinal disease. Though examples of this technology, such as capsule endoscopy, have been successfully translated from the lab into clinically used products, there are still numerous challenges that need to be overcome. This review gives an overview of the research being done in the area of ingestible smart pills and reports on the technical challenges in this field.
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32
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Glissen Brown JR, Berzin TM. Adoption of New Technologies: Artificial Intelligence. Gastrointest Endosc Clin N Am 2021; 31:743-758. [PMID: 34538413 DOI: 10.1016/j.giec.2021.05.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Over the past decade, artificial intelligence (AI) has been broadly applied to many aspects of human life, with recent groundbreaking successes in facial recognition, natural language processing, autonomous driving, and medical imaging. Gastroenterology has applied AI to a vast array of clinical problems, and some of the earliest prospective trials examining AI in medicine have been in computer vision applied to endoscopy. Evidence is mounting for 2 broad areas of AI as applied to gastroenterology: computer-aided detection and computer-aided diagnosis.
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Affiliation(s)
- Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA.
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology and Hepatology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Boston, MA 02130, USA
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Tang Y, Anandasabapathy S, Richards‐Kortum R. Advances in optical gastrointestinal endoscopy: a technical review. Mol Oncol 2021; 15:2580-2599. [PMID: 32915503 PMCID: PMC8486567 DOI: 10.1002/1878-0261.12792] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/23/2020] [Accepted: 09/01/2020] [Indexed: 12/11/2022] Open
Abstract
Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.
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Affiliation(s)
- Yubo Tang
- Department of BioengineeringRice UniversityHoustonTXUSA
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Racz I, Horvath A, Kranitz N, Kiss G, Regoczi H, Horvath Z. Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy. Clin Endosc 2021; 55:113-121. [PMID: 34551512 PMCID: PMC8831420 DOI: 10.5946/ce.2021.149] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 06/19/2021] [Indexed: 12/03/2022] Open
Abstract
Background/Aims We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods.
Methods We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image.
Results The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001)
Conclusions Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.
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Affiliation(s)
- Istvan Racz
- Department of Internal Medicine and Gastroenterology, Petz Aladar University Teaching Hospital, Gyor, Hungary
| | - Andras Horvath
- Department of Physics and Chemistry, Szechenyi Istvan University, Gyor, Hungary
| | - Noemi Kranitz
- Department of Pathology, Petz Aladar University Teaching Hospital, Gyor, Hungary
| | - Gyongyi Kiss
- Department of Internal Medicine and Gastroenterology, Petz Aladar University Teaching Hospital, Gyor, Hungary
| | - Henriett Regoczi
- Department of Internal Medicine and Gastroenterology, Petz Aladar University Teaching Hospital, Gyor, Hungary
| | - Zoltan Horvath
- Department of Mathematics and Informatics, Szechenyi Istvan University, Gyor, Hungary
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Kader R, Hadjinicolaou AV, Georgiades F, Stoyanov D, Lovat LB. Optical diagnosis of colorectal polyps using convolutional neural networks. World J Gastroenterol 2021; 27:5908-5918. [PMID: 34629808 PMCID: PMC8475008 DOI: 10.3748/wjg.v27.i35.5908] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 04/29/2021] [Accepted: 08/24/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a “resect and discard“ or ”leave in“ strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.
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Affiliation(s)
- Rawen Kader
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
| | - Andreas V Hadjinicolaou
- MRC Cancer Unit, Department of Gastroenterology, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Fanourios Georgiades
- Department of Surgery, University of Cambridge, Cambridge CB2 0QQ, United Kingdom
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Department of Computer Science, University College London, London W1W 7TY, United Kingdom
| | - Laurence B Lovat
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, United Kingdom
- Division of Surgery and Interventional Sciences, University College London, London W1W 7TY, United Kingdom
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Park JH, Yang MJ, Kim JS, Park B, Kim JH, Sunwoo MH. Deep-Learning-Based Smartphone Application for Self-Diagnosis of Scleral Jaundice in Patients with Hepatobiliary and Pancreatic Diseases. J Pers Med 2021; 11:jpm11090928. [PMID: 34575705 PMCID: PMC8466674 DOI: 10.3390/jpm11090928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 01/16/2023] Open
Abstract
Outpatient detection of total bilirubin levels should be performed regularly to monitor the recurrence of jaundice in hepatobiliary and pancreatic disease patients. However, frequent hospital visits for blood testing are burdensome for patients with poor medical conditions. This study validates a novel deep-learning-based smartphone application for the self-diagnosis of scleral jaundice in such patients. The system predicts total serum bilirubin levels using the deep-learning-based regression analysis of scleral photos taken by the smartphone's built-in camera. Enrolled patients were randomly assigned to either the training cohort (n = 90, 1034 photos) or the validation cohort (n = 40, 426 photos). The intraclass correlation coefficient value for predicted serum total bilirubin (PSB) derived from the images repeatedly taken at the same time for the same patient showed good reliability (0.86). A strong correlation between measured serum total bilirubin (MSB) and PSB was observed in the subgroup with MSB levels ≥1.5 mg/dL (Spearman rho = 0.70, p < 0.001). The receiver operating characteristic curve for PSB showed that the area under the curve was 0.93, demonstrating good test performance as a predictor of hyperbilirubinemia (p < 0.001). Using a cut-off PSB ≥1.5, the prediction sensitivity of hyperbilirubinemia was 80.0%, with a specificity of 92.6%. Hence, the tool is effective for patient monitoring.
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Affiliation(s)
- Joon Hyeon Park
- Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea;
| | - Min Jae Yang
- Department of Gastroenterology, Ajou University School of Medicine, Suwon 16499, Korea;
| | - Ji Su Kim
- Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovation, Ajou University Medical Center, Suwon 16499, Korea; (J.S.K.); (B.P.)
| | - Bumhee Park
- Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovation, Ajou University Medical Center, Suwon 16499, Korea; (J.S.K.); (B.P.)
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon 16499, Korea
| | - Jin Hong Kim
- Department of Gastroenterology, Ajou University School of Medicine, Suwon 16499, Korea;
- Correspondence: (J.H.K.); (M.H.S.); Tel.: +82-31-219-6937 (J.H.K.); +82-31-219-2390 (M.H.S.); Fax: +82-31-219-5999 (J.H.K.); +82-31-219-1545 (M.H.S.)
| | - Myung Hoon Sunwoo
- Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea;
- Correspondence: (J.H.K.); (M.H.S.); Tel.: +82-31-219-6937 (J.H.K.); +82-31-219-2390 (M.H.S.); Fax: +82-31-219-5999 (J.H.K.); +82-31-219-1545 (M.H.S.)
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Holzwanger EA, Bilal M, Brown JRG, Singh S, Becq A, Ernest-Suarez K, Berzin TM. Benchmarking definitions of false-positive alerts during computer-aided polyp detection in colonoscopy. Endoscopy 2021; 53:937-940. [PMID: 33137833 PMCID: PMC8386281 DOI: 10.1055/a-1302-2942] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND The occurrence of false-positive alerts is an important outcome measure in computer-aided colon polyp detection (CADe) studies. However, there is no consensus definition of a false positive in clinical trials evaluating CADe in colonoscopy. We aimed to study the diagnostic performance of CADe based on different threshold definitions for false-positive alerts. METHODS A previously validated CADe system was applied to screening/surveillance colonoscopy videos. Different thresholds for false-positive alerts were defined based on the time an alert box was continuously traced by the system. Primary outcomes were false-positive results and specificity using different threshold definitions of false positive. RESULTS 62 colonoscopies were analyzed. CADe specificity and accuracy were 93.2 % and 97.8 %, respectively, for a threshold definition of ≥ 0.5 seconds, 98.6 % and 99.5 % for a threshold definition of ≥ 1 second, and 99.8 % and 99.9 % for a threshold definition of ≥ 2 seconds. CONCLUSION Our analysis demonstrated how different threshold definitions of false positive can impact the reported diagnostic performance of CADe for colon polyp detection.
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Affiliation(s)
- Erik A. Holzwanger
- Division of Gastroenterology and Hepatology, Tufts Medical Center, Boston, Massachusetts, United States
| | - Mohammad Bilal
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
| | - Jeremy R. Glissen Brown
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
| | - Shailendra Singh
- West Virginia University Health Sciences Center Charleston Division, Charleston, West Virginia, United States
| | - Aymeric Becq
- Sorbonne Université, Centre d’Endoscopie Digestive, Hôpital Saint Antoine, APHP, Paris, France
| | - Kenneth Ernest-Suarez
- Gastroenterology Department, Hospital México, University of Costa Rica, San Jose, Costa Rica
| | - Tyler M. Berzin
- Center for Advanced Endoscopy, Division of Gastroenterology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
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Tziortziotis I, Laskaratos FM, Coda S. Role of Artificial Intelligence in Video Capsule Endoscopy. Diagnostics (Basel) 2021; 11:1192. [PMID: 34209029 PMCID: PMC8303156 DOI: 10.3390/diagnostics11071192] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.
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Affiliation(s)
- Ioannis Tziortziotis
- Endoscopy Unit, Digestive Diseases Centre, Queen's Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK
| | - Faidon-Marios Laskaratos
- Endoscopy Unit, Digestive Diseases Centre, Queen's Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK
| | - Sergio Coda
- Endoscopy Unit, Digestive Diseases Centre, Queen's Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK
- Photonics Group-Department of Physics, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, UK
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Parsa N, Byrne MF. Artificial intelligence for identification and characterization of colonic polyps. Ther Adv Gastrointest Endosc 2021; 14:26317745211014698. [PMID: 34263163 PMCID: PMC8252334 DOI: 10.1177/26317745211014698] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/07/2021] [Indexed: 12/27/2022] Open
Abstract
Colonoscopy remains the gold standard exam for colorectal cancer screening due to its ability to detect and resect pre-cancerous lesions in the colon. However, its performance is greatly operator dependent. Studies have shown that up to one-quarter of colorectal polyps can be missed on a single colonoscopy, leading to high rates of interval colorectal cancer. In addition, the American Society for Gastrointestinal Endoscopy has proposed the “resect-and-discard” and “diagnose-and-leave” strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the performance of optical biopsy has been suboptimal in community practice. With recent improvements in machine-learning techniques, artificial intelligence–assisted computer-aided detection and diagnosis have been increasingly utilized by endoscopists. The application of computer-aided design on real-time colonoscopy has been shown to increase the adenoma detection rate while decreasing the withdrawal time and improve endoscopists’ optical biopsy accuracy, while reducing the time to make the diagnosis. These are promising steps toward standardization and improvement of colonoscopy quality, and implementation of “resect-and-discard” and “diagnose-and-leave” strategies. Yet, issues such as real-world applications and regulatory approval need to be addressed before artificial intelligence models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of artificial intelligence for detection and characterization of colorectal polyps and review the limitation of existing artificial intelligence technologies and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- Division of Gastroenterology and Hepatology, Department of Medicine, University of Missouri, Columbia, MO 65211, USA
| | - Michael F Byrne
- Division of Gastroenterology, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada; Satisfai Health, Vancouver, BC, Canada
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40
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Lan L, Ye C. Recurrent generative adversarial networks for unsupervised WCE video summarization. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.106971] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Ebigbo A, Messmann H. Artificial intelligence in the upper GI tract: the future is fast approaching. Gastrointest Endosc 2021; 93:1342-1343. [PMID: 33715878 DOI: 10.1016/j.gie.2021.01.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Accepted: 01/15/2021] [Indexed: 02/08/2023]
Affiliation(s)
- Alanna Ebigbo
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
| | - Helmut Messmann
- Department of Gastroenterology, University Hospital of Augsburg, Augsburg, Germany
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Bhatti KM, Khanzada ZS, Kuzman M, Ali SM, Iftikhar SY, Small P. Diagnostic Performance of Artificial Intelligence-Based Models for the Detection of Early Esophageal Cancers in Barret's Esophagus: A Meta-Analysis of Patient-Based Studies. Cureus 2021; 13:e15447. [PMID: 34258114 PMCID: PMC8255083 DOI: 10.7759/cureus.15447] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 02/07/2023] Open
Abstract
Introduction Barret's esophagus (BE) is a precursor of adenocarcinoma of the esophagus. The detection of high-grade dysplasia and adenocarcinoma at an early stage can improve survival but is very challenging. Artificial intelligence (AI)-based models have been claimed to improve diagnostic accuracy. The aim of the current study was to carry out a meta-analysis of papers reporting the results of artificial intelligence-based models used in real-time white light endoscopy of patients with BE to detect early esophageal adenocarcinoma (EEAC). Methods This meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO; Reg No. CRD42021246148) and its conduction and reporting followed the Preferred Reporting Items for Systematic Review and Meta-Analysis of Diagnostic Test Accuracy (PRISMA-DTA) statement guidelines. All peer-reviewed and preprint original articles that reported the sensitivity and specificity of AI-based models on white light endoscopic imaging as an index test against the standard criterion of histologically proven early oesophageal cancer on the background of Barret's esophagus reported as per-patient analysis were considered for inclusion. There was no restriction on type and year of publication, however, articles published in the English language were searched. The search engines used included Medline, PubMed, EMBASE, EMCARE, AMED, BNI, and HMIC. The search strategy included the following keywords for all search engines: ("Esophageal Cancer" OR "Esophageal Neoplasms" OR " Oesophageal Cancer" OR "Oesophageal Neoplasms" OR "Barrett's Esophagus" OR "Barrett's Oesophagus") And ("Artificial Intelligence" OR "Deep Learning" OR "Machine Learning" OR "Convolutional Network"). This search was conducted on November 30, 2020. Duplicate studies were excluded. Studies that reported more than one dataset per patient for the diagnostic accuracy of the AI-based model were included twice. Quantitative and qualitative data, including first author, year of publication, true positives (TP), false negatives (FN), false positives (FP), true negatives (TN), the threshold of the index test, and country where the study was conducted, were extracted using a data extraction sheet. The Quality Appraisal for Diverse Studies 2 (QUADS-2) tool was used to assess the quality of each study. Data were analyzed using MetaDTA, interactive online software for meta-analysis of diagnostic studies. The diagnostic performance of the meta-analysis was assessed by a summary receiver operating characteristics (sROC) plot. A meta-analysis tree was constructed using MetaDTA software to determine the effect of cumulative sensitivity and specificity on surveillance of patients with BE in terms of miss rate and overdiagnosis. Results The literature search revealed 171 relevant records. After removing duplicates, 117 records were screened. Full-text articles of 28 studies were assessed for eligibility. Only three studies reporting four datasets met the inclusion criteria. The summary sensitivity and specificity of AI-based models were 0.90 (95% CI, 0.83- 0.944) and 0.86 (95% CI, 0.781-0.91), respectively. The area under the curve for all the available evidence was 0.88. Conclusion Collective evidence for the routine usage of AI-based models in the detection of EEAC is encouraging but is limited by the low number of studies. Further prospective studies reporting the patient-based diagnostic accuracy of such models are required.
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Affiliation(s)
- Khalid M Bhatti
- Surgery, Health Education England, North West, Blackburn, GBR
| | | | - Matta Kuzman
- Surgery, Health Education England, North East, Newcastle Upon Tyne, GBR
| | - Syed M Ali
- Acute Care Surgery, Hamad General Hospital, Doha, QAT
| | - Syed Y Iftikhar
- Surgery, University Hospital of Derby and Burton, Derby, GBR
| | - Peter Small
- Surgery, Sunderland Royal Hospital, Sunderland, GBR
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Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
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Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
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Raines DL, Adler DG. The Role of Provocative Testing and Localization of the Video Capsule Endoscope in the Management of Small Intestinal Bleeding. Gastrointest Endosc Clin N Am 2021; 31:317-330. [PMID: 33743928 DOI: 10.1016/j.giec.2021.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The cause of small intestinal bleeding (SIB) may be elusive despite exhaustive testing. This article describes the current understanding of SIB regarding evaluation, with emphasis on the use of video capsule endoscopy (VCE) as a diagnostic procedure. This article addresses the utility of provocative testing in challenging cases and the performance of endoscopic procedures on active antithrombotic therapy. Specific recommendations accompany this article, including use of antithrombotic agents to stimulate bleeding when clearly indicated; performance of endoscopic procedures on active antithrombotic therapy; and progressive adoption of VCE and device-assisted enteroscopy in the inpatient setting.
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Affiliation(s)
| | - Douglas G Adler
- University of Utah School of Medicine, 30 North 1900 East 4R118, Salt Lake City, UT 84132, USA
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Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach. Diagnostics (Basel) 2021; 11:diagnostics11030514. [PMID: 33799452 PMCID: PMC8001232 DOI: 10.3390/diagnostics11030514] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 03/10/2021] [Accepted: 03/11/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer is the third most common and second most lethal tumor globally, causing 900,000 deaths annually. In this research, a computer aided diagnosis system was designed that detects colorectal cancer, using an innovative dataset composing of both numeric (blood and urine analysis) and qualitative data (living environment of the patient, tumor position, T, N, M, Dukes classification, associated pathology, technical approach, complications, incidents, ultrasonography-dimensions as well as localization). The intelligent computer aided colorectal cancer diagnosis system was designed using different machine learning techniques, such as classification and shallow and deep neural networks. The maximum accuracy obtained from solving the binary classification problem with traditional machine learning algorithms was 77.8%. However, the regression problem solved with deep neural networks yielded with significantly better performance in terms of mean squared error minimization, reaching the value of 0.0000529.
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Maassen O, Fritsch S, Palm J, Deffge S, Kunze J, Marx G, Riedel M, Schuppert A, Bickenbach J. Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey. J Med Internet Res 2021; 23:e26646. [PMID: 33666563 PMCID: PMC7980122 DOI: 10.2196/26646] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/29/2021] [Accepted: 02/15/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians' requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals. OBJECTIVE This study aimed to evaluate physicians' requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany. METHODS A web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals. RESULTS The online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research. CONCLUSIONS Physicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians' expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.
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Affiliation(s)
- Oliver Maassen
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Sebastian Fritsch
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
| | - Julia Palm
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany
| | - Saskia Deffge
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Julian Kunze
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Gernot Marx
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
| | - Morris Riedel
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
- School of Natural Sciences and Engineering, University of Iceland, Reykjavik, Iceland
| | - Andreas Schuppert
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
- Institute for Computational Biomedicine II, University Hospital RWTH Aachen, Aachen, Germany
| | - Johannes Bickenbach
- Department of Intensive Care Medicine, University Hospital RWTH Aachen, Aachen, Germany
- SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany
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Wang X, Huang J, Ji X, Zhu Z. [Application of artificial intelligence for detection and classification of colon polyps]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2021; 41:310-313. [PMID: 33624608 DOI: 10.12122/j.issn.1673-4254.2021.02.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
Colorectal cancer is one of the most common cancers worldwide, and colonoscopy has proven to be a preferable modality for screening and surveillance of colorectal cancer. This review discusses the clinical application of artificial intelligence (AI) and computer-aided diagnosis for automated colonoscopic detection and diagnosis of colorectal polyps for better understanding of the application of AI-based computer-aided diagnosis systems especially in terms of machine learning, deep learning and convolutional neural network for screening and surveillance of colorectal cancer.
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Affiliation(s)
- X Wang
- Information Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
| | - J Huang
- Department of Oncology, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
| | - X Ji
- Day Surgery Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
| | - Z Zhu
- Day Surgery Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China
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Parsa N, Rex DK, Byrne MF. Colorectal polyp characterization with standard endoscopy: Will Artificial Intelligence succeed where human eyes failed? Best Pract Res Clin Gastroenterol 2021; 52-53:101736. [PMID: 34172255 DOI: 10.1016/j.bpg.2021.101736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field.
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Affiliation(s)
- Nasim Parsa
- University of Missouri, Department of Medicine, Division of Gastroenterology and Hepatology, Columbia, MO, United States
| | - Douglas K Rex
- Indiana University School of Medicine, Department of Medicine, Division of Gastroenterology and Hepatology, Indianapolis, IN, United States
| | - Michael F Byrne
- University of British Columbia, Department of Medicine, Division of Gastroenterology and Hepatology Vancouver, British Columbia, Canada; Satisfai Health and AI4GI Joint Venture, Vancouver, British Columbia, Canada.
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Wavelet Transform and Deep Convolutional Neural Network-Based Smart Healthcare System for Gastrointestinal Disease Detection. Interdiscip Sci 2021; 13:212-228. [PMID: 33566337 DOI: 10.1007/s12539-021-00417-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 01/16/2021] [Accepted: 01/23/2021] [Indexed: 12/19/2022]
Abstract
This work presents a smart healthcare system for the detection of various abnormalities present in the gastrointestinal (GI) region with the help of time-frequency analysis and convolutional neural network. In this regard, the KVASIR V2 dataset comprising of eight classes of GI-tract images such as Normal cecum, Normal pylorus, Normal Z-line, Esophagitis, Polyps, Ulcerative Colitis, Dyed and lifted polyp, and Dyed resection margins are used for training and validation. The initial phase of the work involves an image pre-processing step, followed by the extraction of approximate discrete wavelet transform coefficients. Each class of decomposed images is later given as input to a couple of considered convolutional neural network (CNN) models for training and testing in two different classification levels to recognize its predicted value. Afterward, the classification performance is measured through the following measuring indices: accuracy, precision, recall, specificity, and F1 score. The experimental result shows 97.25% and 93.75% of accuracy in the first level and second level of classification, respectively. Lastly, a comparative performance analysis is carried out with several other previously published works on a similar dataset where the proposed approach performs better than its contemporary methods.
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Barrett esophagus: What to expect from Artificial Intelligence? Best Pract Res Clin Gastroenterol 2021; 52-53:101726. [PMID: 34172253 DOI: 10.1016/j.bpg.2021.101726] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 01/30/2021] [Accepted: 02/01/2021] [Indexed: 01/31/2023]
Abstract
The evaluation and assessment of Barrett's esophagus is challenging for both expert and nonexpert endoscopists. However, the early diagnosis of cancer in Barrett's esophagus is crucial for its prognosis, and could save costs. Pre-clinical and clinical studies on the application of Artificial Intelligence (AI) in Barrett's esophagus have shown promising results. In this review, we focus on the current challenges and future perspectives of implementing AI systems in the management of patients with Barrett's esophagus.
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