51
|
Sterkenburg AJ, Hooghiemstra WTR, Schmidt I, Ntziachristos V, Nagengast WB, Gorpas D. Standardization and implementation of fluorescence molecular endoscopy in the clinic. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-210302SS-PERR. [PMID: 35170264 PMCID: PMC8847121 DOI: 10.1117/1.jbo.27.7.074704] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 01/19/2022] [Indexed: 05/26/2023]
Abstract
SIGNIFICANCE Near-infrared fluorescence molecular endoscopy (NIR-FME) is an innovative technique allowing for in vivo visualization of molecular processes in hollow organs. Despite its potential for clinical translation, NIR-FME still faces challenges, for example, the lack of consensus in performing quality control and standardization of procedures and systems. This may hamper the clinical approval of the technology by authorities and its acceptance by endoscopists. Until now, several clinical trials using NIR-FME have been performed. However, most of these trials had different study designs, making comparison difficult. AIM We describe the need for standardization in NIR-FME, provide a pathway for setting up a standardized clinical study, and describe future perspectives for NIR-FME. Body: Standardization is challenging due to many parameters. Invariable parameters refer to the hardware specifications. Variable parameters refer to movement or tissue optical properties. Phantoms can be of aid when defining the influence of these variables or when standardizing a procedure. CONCLUSION There is a need for standardization in NIR-FME and hurdles still need to be overcome before a widespread clinical implementation of NIR-FME can be realized. When these hurdles are overcome, clinical outcomes can be compared and systems can be benchmarked, enabling clinical implementation.
Collapse
Affiliation(s)
- Andrea J. Sterkenburg
- University of Groningen, University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands
| | - Wouter T. R. Hooghiemstra
- University of Groningen, University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands
| | - Iris Schmidt
- University of Groningen, University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands
| | - Vasilis Ntziachristos
- Technical University of Munich, School of Medicine, Chair of Biological Imaging, Central Institute for Translational Cancer Research (TranslaTUM), Munich, Germany
- Helmholtz Zentrum München (GmbH), Institute of Biological and Medical Imaging, Neuherberg, Germany
| | - Wouter B. Nagengast
- University of Groningen, University Medical Center Groningen, Department of Gastroenterology and Hepatology, Groningen, The Netherlands
| | - Dimitris Gorpas
- Technical University of Munich, School of Medicine, Chair of Biological Imaging, Central Institute for Translational Cancer Research (TranslaTUM), Munich, Germany
- Helmholtz Zentrum München (GmbH), Institute of Biological and Medical Imaging, Neuherberg, Germany
| |
Collapse
|
52
|
Kou W, Carlson DA, Baumann AJ, Donnan EN, Schauer JM, Etemadi M, Pandolfino JE. A multi-stage machine learning model for diagnosis of esophageal manometry. Artif Intell Med 2022; 124:102233. [PMID: 35115131 PMCID: PMC8817064 DOI: 10.1016/j.artmed.2021.102233] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 02/03/2023]
Abstract
High-resolution manometry (HRM) is the primary procedure used to diagnose esophageal motility disorders. Its manual interpretation and classification, including evaluation of swallow-level outcomes and then derivation of a study-level diagnosis based on Chicago Classification (CC), may be limited by inter-rater variability and inaccuracy of an individual interpreter. We hypothesized that an automatic diagnosis platform using machine learning and artificial intelligence approaches could be developed to accurately identify esophageal motility diagnoses. Further, a multi-stage modeling framework, akin to the step-wise approach of the CC, was utilized to leverage advantages of a combination of machine learning approaches including deep-learning models and feature-based models. Models were trained and tested using a dataset comprised of 1741 patients' HRM studies with CC diagnoses assigned by expert physician raters. In the swallow-level stage, three models based on convolutional neural networks (CNNs) were developed to predict swallow type and swallow pressurization (test accuracies of 0.88 and 0.93, respectively), and integrated relaxation pressure (IRP)(regression model with test error of 4.49 mmHg). At the study-level stage, model selection from families of the expert-knowledge-based rule models, xgboost models and artificial neural network(ANN) models were conducted. A simple model-agnostic strategy of model balancing motivated by Bayesian principles was utilized, which gave rise to model averaging weighted by precision scores. The averaged (blended) models and individual models were compared and evaluated, of which the best performance on test dataset is 0.81 in top-1 prediction, 0.92 in top-2 predictions. This is the first artificial-intelligence style model to automatically predict esophageal motility (CC) diagnoses from HRM studies using raw multi-swallow data and it achieved high accuracy. Thus, this proposed modeling framework could be broadly applied to assist with HRM interpretation in a clinical setting.
Collapse
Affiliation(s)
- Wenjun Kou
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA,Corresponding author (Wenjun Kou)
| | - Dustin A. Carlson
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Alexandra J. Baumann
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Erica N. Donnan
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Jacob M. Schauer
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 North Lake Shore Drive, 11th Floor, Chicago, IL 60611, USA
| | - Mozziyar Etemadi
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston IL 60201, USA
| | - John E. Pandolfino
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| |
Collapse
|
53
|
Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14:124-152. [PMID: 35116107 PMCID: PMC8790413 DOI: 10.4251/wjgo.v14.i1.124] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/19/2021] [Accepted: 11/15/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) technology has made leaps and bounds since its invention. AI technology can be subdivided into many technologies such as machine learning and deep learning. The application scope and prospect of different technologies are also totally different. Currently, AI technologies play a pivotal role in the highly complex and wide-ranging medical field, such as medical image recognition, biotechnology, auxiliary diagnosis, drug research and development, and nutrition. Colorectal cancer (CRC) is a common gastrointestinal cancer that has a high mortality, posing a serious threat to human health. Many CRCs are caused by the malignant transformation of colorectal polyps. Therefore, early diagnosis and treatment are crucial to CRC prognosis. The methods of diagnosing CRC are divided into imaging diagnosis, endoscopy, and pathology diagnosis. Treatment methods are divided into endoscopic treatment, surgical treatment, and drug treatment. AI technology is in the weak era and does not have communication capabilities. Therefore, the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients. This article reviews the application of AI in the diagnosis, treatment, and prognosis of CRC and provides the prospects for the broader application of AI in CRC.
Collapse
Affiliation(s)
- Feng Liang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Shu Wang
- Department of Radiotherapy, Jilin University Second Hospital, Changchun 130041, Jilin Province, China
| | - Kai Zhang
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Tong-Jun Liu
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| | - Jian-Nan Li
- Department of General Surgery, The Second Hospital of Jilin University, Changchun 130041, Jilin Province, China
| |
Collapse
|
54
|
Automatic Classification of Fatty Liver Disease Based on Supervised Learning and Genetic Algorithm. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12010521] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Fatty liver disease is considered a critical illness that should be diagnosed and detected at an early stage. In advanced stages, liver cancer or cirrhosis arise, and to identify this disease, radiologists commonly use ultrasound images. However, because of their low quality, radiologists found it challenging to recognize this disease using ultrasonic images. To avoid this problem, a Computer-Aided Diagnosis technique is developed in the current study, using Machine Learning Algorithms and a voting-based classifier to categorize liver tissues as being fatty or normal, based on extracting ultrasound image features and a voting-based classifier. Four main contributions are provided by our developed method: firstly, the classification of liver images is achieved as normal or fatty without a segmentation phase. Secondly, compared to our proposed work, the dataset in previous works was insufficient. A combination of 26 features is the third contribution. Based on the proposed methods, the extracted features are Gray-Level Co-Occurrence Matrix (GLCM) and First-Order Statistics (FOS). The fourth contribution is the voting classifier used to determine the liver tissue type. Several trials have been performed by examining the voting-based classifier and J48 algorithm on a dataset. The obtained TP, TN, FP, and FN were 94.28%, 97.14%, 5.71%, and 2.85%, respectively. The achieved precision, sensitivity, specificity, and F1-score were 94.28%, 97.05%, 94.44%, and 95.64%, respectively. The achieved classification accuracy using a voting-based classifier was 95.71% and in the case of using the J48 algorithm was 93.12%. The proposed work achieved a high performance compared with the research works.
Collapse
|
55
|
Karstensen J, Nayahangan L, Konge L, Vilmann P. A core curriculum for basic EUS skills: An international consensus using the Delphi methodology. Endosc Ultrasound 2022; 11:122-132. [PMID: 35488624 PMCID: PMC9059795 DOI: 10.4103/eus-d-21-00125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Background and Objectives: Materials and Methods: Results: Conclusions:
Collapse
|
56
|
Taghiakbari M, Mori Y, von Renteln D. Artificial intelligence-assisted colonoscopy: A review of current state of practice and research. World J Gastroenterol 2021; 27:8103-8122. [PMID: 35068857 PMCID: PMC8704267 DOI: 10.3748/wjg.v27.i47.8103] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/22/2021] [Accepted: 12/08/2021] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy is an effective screening procedure in colorectal cancer prevention programs; however, colonoscopy practice can vary in terms of lesion detection, classification, and removal. Artificial intelligence (AI)-assisted decision support systems for endoscopy is an area of rapid research and development. The systems promise improved detection, classification, screening, and surveillance for colorectal polyps and cancer. Several recently developed applications for AI-assisted colonoscopy have shown promising results for the detection and classification of colorectal polyps and adenomas. However, their value for real-time application in clinical practice has yet to be determined owing to limitations in the design, validation, and testing of AI models under real-life clinical conditions. Despite these current limitations, ambitious attempts to expand the technology further by developing more complex systems capable of assisting and supporting the endoscopist throughout the entire colonoscopy examination, including polypectomy procedures, are at the concept stage. However, further work is required to address the barriers and challenges of AI integration into broader colonoscopy practice, to navigate the approval process from regulatory organizations and societies, and to support physicians and patients on their journey to accepting the technology by providing strong evidence of its accuracy and safety. This article takes a closer look at the current state of AI integration into the field of colonoscopy and offers suggestions for future research.
Collapse
Affiliation(s)
- Mahsa Taghiakbari
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo 0450, Norway
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama 224-8503, Japan
| | - Daniel von Renteln
- Department of Gastroenterology, CRCHUM, Montreal H2X 0A9, Quebec, Canada
| |
Collapse
|
57
|
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.
Collapse
|
58
|
Guo L, Gong H, Wang Q, Zhang Q, Tong H, Li J, Lei X, Xiao X, Li C, Jiang J, Hu B, Song J, Tang C, Huang Z. Detection of multiple lesions of gastrointestinal tract for endoscopy using artificial intelligence model: a pilot study. Surg Endosc 2021; 35:6532-6538. [PMID: 33185766 DOI: 10.1007/s00464-020-08150-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023]
Abstract
BACKGROUND This study was aimed to develop a computer-aided diagnosis (CAD) system with deep-learning technique and to validate its efficiency on detecting the four categories of lesions such as polyps, advanced cancer, erosion/ulcer and varices at endoscopy. METHODS A deep convolutional neural network (CNN) that consists of more than 50 layers were trained with a big dataset containing 327,121 white light images (WLI) of endoscopy from 117,005 cases collected from 2012 to 2017. Two CAD models were developed using images with or without annotation of the training dataset. The efficiency of the CAD system detecting the four categories of lesions was validated by another dataset containing consecutive cases from 2018 to 2019. RESULTS A total of 1734 cases with 33,959 images were included in the validation datasets which containing lesions of polyps 1265, advanced cancer 500, erosion/ulcer 486, and varices 248. The CAD system developed in this study may detect polyps, advanced cancer, erosion/ulcer and varices as abnormality with the sensitivity of 88.3% and specificity of 90.3%, respectively, in 0.05 s. The training datasets with annotation may enhance either sensitivity or specificity about 20%, p = 0.000. The sensitivities and specificities for polyps, advanced cancer, erosion/ulcer and varices reached about 90%, respectively. The detect efficiency for the four categories of lesions reached to 89.7%. CONCLUSION The CAD model for detection of multiple lesions in gastrointestinal lumen would be potentially developed into a double check along with real-time assessment and interpretation of the findings encountered by the endoscopists and may be a benefit to reduce the events of missing lesions.
Collapse
Affiliation(s)
- Linjie Guo
- West China Hospital, Sichuan University, Laboratory of Gastroenterology and Hepatology, State Key Laboratory of Biotherapy, Chengdu, China
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Hui Gong
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | | | - Qiongying Zhang
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Huan Tong
- West China Hospital, Sichuan University, Laboratory of Gastroenterology and Hepatology, State Key Laboratory of Biotherapy, Chengdu, China
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Jing Li
- West China Hospital, Sichuan University, Laboratory of Gastroenterology and Hepatology, State Key Laboratory of Biotherapy, Chengdu, China
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Xiang Lei
- SeedsMed Technology Inc, Sichuan, China
| | - Xue Xiao
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Chuanhui Li
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Jinsun Jiang
- West China Hospital, Sichuan University, Management Department of Clinical Study, Chengdu, China
| | - Bing Hu
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China
| | - Jie Song
- SeedsMed Technology Inc, Sichuan, China
| | - Chengwei Tang
- West China Hospital, Sichuan University, Laboratory of Gastroenterology and Hepatology, State Key Laboratory of Biotherapy, Chengdu, China.
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China.
| | - Zhiyin Huang
- West China Hospital, Sichuan University, Department of Gastroenterology, Chengdu, China.
| |
Collapse
|
59
|
Fuhrman JD, Gorre N, Hu Q, Li H, El Naqa I, Giger ML. A review of explainable and interpretable AI with applications in COVID-19 imaging. Med Phys 2021; 49:1-14. [PMID: 34796530 PMCID: PMC8646613 DOI: 10.1002/mp.15359] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/14/2021] [Accepted: 10/25/2021] [Indexed: 12/24/2022] Open
Abstract
The development of medical imaging artificial intelligence (AI) systems for evaluating COVID‐19 patients has demonstrated potential for improving clinical decision making and assessing patient outcomes during the recent COVID‐19 pandemic. These have been applied to many medical imaging tasks, including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life‐or‐death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to COVID‐19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID‐19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID‐19 to quickly understand the basics of several explainable AI techniques and assist in the selection of an approach that is both appropriate and effective for a given scenario.
Collapse
Affiliation(s)
- Jordan D Fuhrman
- Medical Imaging and Data Resource Center (MIDRC), The University of Chicago, Chicago, Illinois, USA.,Department of Radiology, The University of Chicago, Chicago, Illinois, USA
| | - Naveena Gorre
- Medical Imaging and Data Resource Center (MIDRC), The University of Chicago, Chicago, Illinois, USA.,Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida, USA
| | - Qiyuan Hu
- Medical Imaging and Data Resource Center (MIDRC), The University of Chicago, Chicago, Illinois, USA.,Department of Radiology, The University of Chicago, Chicago, Illinois, USA
| | - Hui Li
- Medical Imaging and Data Resource Center (MIDRC), The University of Chicago, Chicago, Illinois, USA.,Department of Radiology, The University of Chicago, Chicago, Illinois, USA
| | - Issam El Naqa
- Medical Imaging and Data Resource Center (MIDRC), The University of Chicago, Chicago, Illinois, USA.,Department of Machine Learning, Moffitt Cancer Center, Tampa, Florida, USA
| | - Maryellen L Giger
- Medical Imaging and Data Resource Center (MIDRC), The University of Chicago, Chicago, Illinois, USA.,Department of Radiology, The University of Chicago, Chicago, Illinois, USA
| |
Collapse
|
60
|
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.
Collapse
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
| |
Collapse
|
61
|
Haak HE, Gao X, Maas M, Waktola S, Benson S, Beets-Tan RGH, Beets GL, van Leerdam M, Melenhorst J. The use of deep learning on endoscopic images to assess the response of rectal cancer after chemoradiation. Surg Endosc 2021; 36:3592-3600. [PMID: 34642794 PMCID: PMC9001548 DOI: 10.1007/s00464-021-08685-7] [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: 06/17/2020] [Accepted: 08/07/2021] [Indexed: 12/30/2022]
Abstract
Background Accurate response evaluation is necessary to select complete responders (CRs) for a watch-and-wait approach. Deep learning may aid in this process, but so far has never been evaluated for this purpose. The aim was to evaluate the accuracy to assess response with deep learning methods based on endoscopic images in rectal cancer patients after neoadjuvant therapy. Methods Rectal cancer patients diagnosed between January 2012 and December 2015 and treated with neoadjuvant (chemo)radiotherapy were retrospectively selected from a single institute. All patients underwent flexible endoscopy for response evaluation. Diagnostic performance (accuracy, area under the receiver operator characteristics curve (AUC), positive- and negative predictive values, sensitivities and specificities) of different open accessible deep learning networks was calculated. Reference standard was histology after surgery, or long-term outcome (>2 years of follow-up) in a watch-and-wait policy. Results 226 patients were included for the study (117(52%) were non-CRs; 109(48%) were CRs). The accuracy, AUC, positive- and negative predictive values, sensitivity and specificity of the different models varied from 0.67–0.75%, 0.76–0.83%, 67–74%, 70–78%, 68–79% to 66–75%, respectively. Overall, EfficientNet-B2 was the most successful model with the highest diagnostic performance. Conclusions This pilot study shows that deep learning has a modest accuracy (AUCs 0.76-0.83). This is not accurate enough for clinical decision making, and lower than what is generally reported by experienced endoscopists. Deep learning models can however be further improved and may become useful to assist endoscopists in evaluating the response. More well-designed prospective studies are required. Supplementary Information The online version contains supplementary material available at 10.1007/s00464-021-08685-7.
Collapse
Affiliation(s)
- Hester E Haak
- Department of Surgery, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology-Maastricht University, Maastricht, The Netherlands
| | - Xinpei Gao
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Selam Waktola
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Sean Benson
- Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- GROW School for Oncology and Developmental Biology-Maastricht University, Maastricht, The Netherlands.,Department of Radiology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Geerard L Beets
- Department of Surgery, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands.,GROW School for Oncology and Developmental Biology-Maastricht University, Maastricht, The Netherlands
| | - Monique van Leerdam
- Department of Gastroenterology, Netherlands Cancer Institute-Antoni Van Leeuwenhoek, Amsterdam, The Netherlands
| | - Jarno Melenhorst
- GROW School for Oncology and Developmental Biology-Maastricht University, Maastricht, The Netherlands. .,Department of Surgery, Maastricht University Medical Centre, Postbox 5800, 6202 AZ, Maastricht, The Netherlands.
| |
Collapse
|
62
|
Ahmad OF, Mori Y, Misawa M, Kudo SE, Anderson JT, Bernal J, Berzin TM, Bisschops R, Byrne MF, Chen PJ, East JE, Eelbode T, Elson DS, Gurudu SR, Histace A, Karnes WE, Repici A, Singh R, Valdastri P, Wallace MB, Wang P, Stoyanov D, Lovat LB. Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method. Endoscopy 2021; 53:893-901. [PMID: 33167043 PMCID: PMC8390295 DOI: 10.1055/a-1306-7590] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND : Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. METHODS : An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. RESULTS : The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. CONCLUSIONS : This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
Collapse
Affiliation(s)
- Omer F. Ahmad
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan,Clinical Effectiveness Research Group, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Masashi Misawa
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Shin-ei Kudo
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - John T. Anderson
- Department of Gastroenterology, Gloucestershire Hospitals NHS Foundation Trust, Gloucester, UK
| | - Jorge Bernal
- Computer Science Department, Universitat Autonoma de Barcelona and Computer Vision Center, Barcelona, Spain
| | - Tyler M. Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Raf Bisschops
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, TARGID KU Leuven, Leuven, Belgium
| | - Michael F. Byrne
- Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, British Columbia, Canada
| | - Peng-Jen Chen
- Division of Gastroenterology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - James E. East
- Translational Gastroenterology Unit, John Radcliffe Hospital, Oxford, UK,Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Tom Eelbode
- Medical Imaging Research Center, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Daniel S. Elson
- Hamlyn Centre for Robotic Surgery, Institute of Global Health Innovation, Imperial College London, London, UK,Department of Surgery and Cancer, Imperial College London, London, UK
| | - Suryakanth R. Gurudu
- Division of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona, USA
| | - Aymeric Histace
- ETIS, Universite de Cergy-Pointoise, ENSEA, CNRS, Cergy-Pointoise Cedex, France
| | - William E. Karnes
- H. H. Chao Comprehensive Digestive Disease Center, Division of Gastroenterology & Hepatology, Department of Medicine, University of California, Irvine, California, USA
| | - Alessandro Repici
- Department of Gastroenterology, Humanitas Clinical and Research Center, IRCCS, Rozzano, Milan, Italy,Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Milan, Italy
| | - Rajvinder Singh
- Department of Gastroenterology and Hepatology, Lyell McEwan Hospital, Adelaide, South Australia, Australia
| | - Pietro Valdastri
- School of Electronics and Electrical Engineering, University of Leeds, Leeds, UK
| | - Michael B. Wallace
- Division of Gastroenterology & Hepatology, Mayo Clinic, Jacksonville, Florida, USA
| | - Pu Wang
- Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China
| | - Danail Stoyanov
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK
| | - Laurence B. Lovat
- Wellcome/EPSRC Centre for Interventional & Surgical Sciences, University College London, London, UK,Gastrointestinal Services, University College London Hospital, London, UK
| |
Collapse
|
63
|
Yang H, Hu B. Early gastrointestinal cancer: The application of artificial intelligence. Artif Intell Gastrointest Endosc 2021; 2:185-197. [DOI: 10.37126/aige.v2.i4.185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 06/25/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.
Collapse
Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| |
Collapse
|
64
|
Zhao SB, Yang W, Wang SL, Pan P, Wang RD, Chang X, Sun ZQ, Fu XH, Shang H, Wu JR, Chen LZ, Chang J, Song P, Miao YL, He SX, Miao L, Jiang HQ, Wang W, Yang X, Dong YH, Lin H, Chen Y, Gao J, Meng QQ, Jin ZD, Li ZS, Bai Y. Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning. World J Gastroenterol 2021; 27:5232-5246. [PMID: 34497447 PMCID: PMC8384745 DOI: 10.3748/wjg.v27.i31.5232] [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: 02/19/2021] [Revised: 04/10/2021] [Accepted: 07/20/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Artificial intelligence in colonoscopy is an emerging field, and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas. Several deep learning-based computer-assisted detection (CADe) techniques were established from small single-center datasets, and unrepresentative learning materials might confine their application and generalization in wide practice. Although CADes have been reported to identify polyps in colonoscopic images and videos in real time, their diagnostic performance deserves to be further validated in clinical practice.
AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.
METHODS With high-quality screening and labeling from 55 qualified colonoscopists, a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe. In addition, the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps. Finally, we conducted a self-controlled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.
RESULTS The CADe was able to identify polyps in the test dataset with 95.0% sensitivity and 99.1% specificity. For colonoscopy videos, all 86 polyps were detected with 92.2% sensitivity and 93.6% specificity in frame-by-frame analysis. In the prospective validation, the sensitivity of CAD in identifying polyps was 98.4% (185/188). Folds, reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies. Colonoscopists can detect more polyps (0.90 vs 0.82, P < 0.001) and adenomas (0.32 vs 0.30, P = 0.045) with the aid of CADe, particularly polyps < 5 mm and flat polyps (0.65 vs 0.57, P < 0.001; 0.74 vs 0.67, P = 0.001, respectively). However, high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time (P = 0.32; P = 0.16, respectively).
CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas, and further confirmation is warranted.
Collapse
Affiliation(s)
- Sheng-Bing Zhao
- Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Wei Yang
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Shu-Ling Wang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Peng Pan
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Run-Dong Wang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Xin Chang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Zhong-Qian Sun
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Xing-Hui Fu
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Hong Shang
- Tencent AI Lab, National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging, Shenzhen 518063, Guangdong Province, China
| | - Jian-Rong Wu
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Li-Zhu Chen
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Jia Chang
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Pu Song
- Tencent Healthcare (Shenzhen) Co. LTD., Shenzhen 518063, Guangdong Province, China
| | - Ying-Lei Miao
- Department of Gastroenterology, The First Affiliated Hospital of Kunming Medical University, Kunming 650000, Yunnan Province, China
| | - Shui-Xiang He
- Department of Gastroenterology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, Shaanxi Province, China
| | - Lin Miao
- Institute of Digestive Endoscopy and Medical Center for Digestive Disease, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, Jiangsu Province, China
| | - Hui-Qing Jiang
- Department of Gastroenterology, The Second Hospital of Hebei Medical University, Hebei Key Laboratory of Gastroenterology, Hebei Institute of Gastroenterology, Shijiazhuang 050000, Hebei Province, China
| | - Wen Wang
- Department of Gastroenterology, 900th Hospital of Joint Logistics Support Force, Fuzhou 350025, Fujian Province, China
| | - Xia Yang
- Department of Gastroenterology, No. 905 Hospital of The Chinese People's Liberation Army, Shanghai 200050, China
| | - Yuan-Hang Dong
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Han Lin
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Yan Chen
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Jie Gao
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Qian-Qian Meng
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Zhen-Dong Jin
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Zhao-Shen Li
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| | - Yu Bai
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University/Naval Medical University, Shanghai 200433, China
| |
Collapse
|
65
|
Majtner T, Brodersen JB, Herp J, Kjeldsen J, Halling ML, Jensen MD. A deep learning framework for autonomous detection and classification of Crohn's disease lesions in the small bowel and colon with capsule endoscopy. Endosc Int Open 2021; 9:E1361-E1370. [PMID: 34466360 PMCID: PMC8367448 DOI: 10.1055/a-1507-4980] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 05/03/2021] [Indexed: 12/13/2022] Open
Abstract
Background and study aims Small bowel ulcerations are efficiently detected with deep learning techniques, whereas the ability to diagnose Crohn's disease (CD) in the colon with it is unknown. This study examined the ability of a deep learning framework to detect CD lesions with pan-enteric capsule endoscopy (CE) and classify lesions of different severity. Patients and methods CEs from patients with suspected or known CD were included in the analysis. Two experienced gastroenterologists classified anonymized images into normal mucosa, non-ulcerated inflammation, aphthous ulceration, ulcer, or fissure/extensive ulceration. An automated framework incorporating multiple ResNet-50 architectures was trained. To improve its robustness and ability to characterize lesions, image processing methods focused on texture enhancement were employed. Results A total of 7744 images from 38 patients with CD were collected (small bowel 4972, colon 2772) of which 2748 contained at least one ulceration (small bowel 1857, colon 891). With a patient-dependent split of images for training, validation, and testing, ulcerations were diagnosed with a sensitivity, specificity, and diagnostic accuracy of 95.7 % (CI 93.4-97.4), 99.8 % (CI 99.2-100), and 98.4 % (CI 97.6-99.0), respectively. The diagnostic accuracy was 98.5 % (CI 97.5-99.2) for the small bowel and 98.1 % (CI 96.3-99.2) for the colon. Ulcerations of different severities were classified with substantial agreement (κ = 0.72). Conclusions Our proposed framework is in excellent agreement with the clinical standard, and diagnostic accuracies are equally high for the small bowel and colon. Deep learning approaches have a great potential to help clinicians detect, localize, and determine the severity of CD with pan-enteric CE.
Collapse
Affiliation(s)
- Tomáš Majtner
- Applied Artificial Intelligence and Data Science, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Jacob Broder Brodersen
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
| | - Jürgen Herp
- Applied Artificial Intelligence and Data Science, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | - Jens Kjeldsen
- Department of Medical Gastroenterology, Odense University Hospital, Odense, Denmark
| | - Morten Lee Halling
- Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
| | - Michael Dam Jensen
- Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
| |
Collapse
|
66
|
Development of an AI system for accurately diagnose hepatocellular carcinoma from computed tomography imaging data. Br J Cancer 2021; 125:1111-1121. [PMID: 34365472 DOI: 10.1038/s41416-021-01511-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 07/05/2021] [Accepted: 07/22/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND AND AIMS Computed tomography (CT) scan is frequently used to detect hepatocellular carcinoma (HCC) in routine clinical practice. The aim of this study is to develop a deep-learning AI system to improve the diagnostic accuracy of HCC by analysing liver CT imaging data. METHODS We developed a deep-learning AI system by training on CT images from 7512 patients at Henan Provincial Peoples' Hospital. Its performance was validated on one internal test set (Henan Provincial Peoples' Hospital, n = 385) and one external test set (Henan Provincial Cancer Hospital, n = 556). The area under the receiver-operating characteristic curve (AUROC) was used as the primary classification metric. Accuracy, sensitivity, specificity, precision, negative predictive value and F1 metric were used to measure the performance of AI systems and radiologists. RESULTS AI system achieved high performance in identifying HCC patients, with AUROC of 0.887 (95% CI 0.855-0.919) on the internal test set and 0.883 (95% CI 0.855-0.911) on the external test set. For internal test set, accuracy was 81.0% (76.8-84.8%), sensitivity was 78.4% (72.4-83.7%), specificity was 84.4% (78.0-89.6%) and F1 (harmonic average of precision and recall rate) was 0.824. For external test set, accuracy was 81.3% (77.8-84.5%), sensitivity was 89.4% (85.0-92.8%), specificity was 74.0% (68.5-78.9%) and F1 was 0.819. Compared with radiologists, AI system achieved comparable accuracy and F1 metric on internal test set (0.853 versus 0.818, P = 0.107; 0.863 vs. 0.824, P = 0.082) and external test set (0.805 vs. 0.793, P = 0.663; 0.810 vs. 0.814, P = 0.866). The predicted HCC risk scores by AI system in HCC patients with multiple tumours and high fibrosis stage were higher than those with solitary tumour and low fibrosis stage (tumour number: 0.197 vs. 0.138, P = 0.006; fibrosis stage: 0.183 vs. 0.127, P < 0.001). Radiologists' review showed that the accuracy of saliency heatmaps predicted by algorithms was 92.1% (95% CI: 89.2-95.0%). CONCLUSIONS AI system achieved high performance in the detection of HCC compared with a group of specialised radiologists. Further investigation by prospective clinical trials was necessitated to verify this model.
Collapse
|
67
|
Computerized Tomography Image Feature under Convolutional Neural Network Algorithm Evaluated for Therapeutic Effect of Clarithromycin Combined with Salmeterol/Fluticasone on Chronic Obstructive Pulmonary Disease. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:8563181. [PMID: 34381586 PMCID: PMC8352704 DOI: 10.1155/2021/8563181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/13/2021] [Accepted: 07/22/2021] [Indexed: 11/30/2022]
Abstract
This study was to explore the use of convolutional neural network (CNN) for the classification and recognition of computerized tomography (CT) images of chronic obstructive pulmonary disease (COPD) and the therapeutic effect of clarithromycin combined with salmeterol/fluticasone. First, the clinical data of COPD patients treated in hospital from September 2018 to December 2020 were collected, and CT and X-ray images were also collected. CT-CNN and X ray-CNN single modal models were constructed based on the LeNet-5 model. The randomized fusion algorithm was introduced to construct a fused CNN model for the diagnosis of COPD patients, and the recognition effect of the model was verified. Subsequently, the three-dimensional reconstruction of the patient's bronchus was performed using the classified CT images, and the changes of CT quantitative parameters in COPD patients were compared and analyzed. Finally, COPD patients were treated with salmeterol/fluticasone (COPD-C) and combined with clarithromycin (COPD-T). In addition, the differences between patients' lung function indexes, blood gas indexes, St. George respiratory questionnaire (SGRQ) scores, and the number of acute exacerbations (AECOPD) before and after treatment were evaluated. The results showed that the randomized fusion model under different iteration times and batch sizes always had the highest recognition rate, sensitivity, and specificity compared to the two single modal CNN models, but it also had longer training time. After CT images were used to quantitatively evaluate the changes of the patient's bronchus, it was found that the area of the upper and lower lung lobes of the affected side of COPD patients and the ratio of the area of the tube wall to the bronchus were significantly changed. The lung function, blood gas index, and SGRQ score of COPD-T patients were significantly improved compared with the COPD-C group (P < 0.05), but there was no considerable difference in AECOPD (P > 0.05). In summary, the randomized fusion-based CNN model can improve the recognition rate of COPD, and salmeterol/fluticasone combined with clarithromycin therapy can significantly improve the clinical treatment effect of COPD patients.
Collapse
|
68
|
Artificial intelligence can increase the detection rate of colorectal polyps and adenomas: a systematic review and meta-analysis. Eur J Gastroenterol Hepatol 2021; 33:1041-1048. [PMID: 32804846 DOI: 10.1097/meg.0000000000001906] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Colonoscopy is an important method to diagnose polyps, especially adenomatous polyps. However, the rate of missed diagnoses is relatively high. In this study, we aimed to determine whether artificial intelligence (AI) improves the polyp detection rate (PDR) and adenoma detection rate (ADR) with colonoscopy. We performed a systematic search in PubMed, Cochrane Library, Embase, and Web of Science databases; the search included entries in the databases up to and including 29 February 2020. Five articles that involved a total of 4311 patients fulfilled the selection criteria. The results of these studies showed that both PDR and ADR increased with the assistance of AI compared with those in control groups {pooled odds ratio (OR) = 1.91 [95% confidence interval (CI) 1.68-2.16] and 1.75 (95% CI 1.52-2.01), respectively}. Good bowel preparation reduced the impact of AI, but significant differences were still apparent in PDR and ADR [pooled OR = 1.69 (95% CI 1.32-2.16) and 1.36 (95% CI 1.04-1.78), respectively]. The characteristics of polyps and adenomas also influenced the results. The average number of polyps and adenomas detected varied significantly by location, and small polyps and adenomas were more likely to be missed. However, the effect of the morphology of polyps and AI-assisted detection needs further studies. In conclusion, AI increases the detection rates of polyps and adenomas in colonoscopy. Without AI assistance, detection rates can be improved with better bowel preparation and training for small polyp and adenoma detection.
Collapse
|
69
|
Song YQ, Mao XL, Zhou XB, He SQ, Chen YH, Zhang LH, Xu SW, Yan LL, Tang SP, Ye LP, Li SW. Use of Artificial Intelligence to Improve the Quality Control of Gastrointestinal Endoscopy. Front Med (Lausanne) 2021; 8:709347. [PMID: 34368199 PMCID: PMC8339701 DOI: 10.3389/fmed.2021.709347] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 06/29/2021] [Indexed: 12/04/2022] Open
Abstract
With the rapid development of science and technology, artificial intelligence (AI) systems are becoming ubiquitous, and their utility in gastroenteroscopy is beginning to be recognized. Digestive endoscopy is a conventional and reliable method of examining and diagnosing digestive tract diseases. However, with the increase in the number and types of endoscopy, problems such as a lack of skilled endoscopists and difference in the professional skill of doctors with different degrees of experience have become increasingly apparent. Most studies thus far have focused on using computers to detect and diagnose lesions, but improving the quality of endoscopic examination process itself is the basis for improving the detection rate and correctly diagnosing diseases. In the present study, we mainly reviewed the role of AI in monitoring systems, mainly through the endoscopic examination time, reducing the blind spot rate, improving the success rate for detecting high-risk lesions, evaluating intestinal preparation, increasing the detection rate of polyps, automatically collecting maps and writing reports. AI can even perform quality control evaluations for endoscopists, improve the detection rate of endoscopic lesions and reduce the burden on endoscopists.
Collapse
Affiliation(s)
- Ya-Qi Song
- Taizhou Hospital, Zhejiang University, Linhai, China
| | - Xin-Li Mao
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Xian-Bin Zhou
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Sai-Qin He
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ya-Hong Chen
- Health Management Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Li-Hui Zhang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shi-Wen Xu
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Ling-Ling Yan
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Shen-Ping Tang
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Li-Ping Ye
- Taizhou Hospital, Zhejiang University, Linhai, China.,Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.,Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| | - Shao-Wei Li
- Key Laboratory of Minimally Invasive Techniques and Rapid Rehabilitation of Digestive System Tumor of Zhejiang Province, Taizhou Hospital Affiliated to Wenzhou Medical University, Linhai, China.,Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China.,Institute of Digestive Disease, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China
| |
Collapse
|
70
|
Tanabe S, Perkins EJ, Ono R, Sasaki H. Artificial intelligence in gastrointestinal diseases. Artif Intell Gastroenterol 2021; 2:69-76. [DOI: 10.35712/aig.v2.i3.69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/09/2021] [Accepted: 06/04/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) applications are growing in medicine. It is important to understand the current state of the AI applications prior to utilizing in disease research and treatment. In this review, AI application in the diagnosis and treatment of gastrointestinal diseases are studied and summarized. In most cases, AI studies had large amounts of data, including images, to learn to distinguish disease characteristics according to a human’s perspectives. The detailed pros and cons of utilizing AI approaches should be investigated in advance to ensure the safe application of AI in medicine. Evidence suggests that the collaborative usage of AI in both diagnosis and treatment of diseases will increase the precision and effectiveness of medicine. Recent progress in genome technology such as genome editing provides a specific example where AI has revealed the diagnostic and therapeutic possibilities of RNA detection and targeting.
Collapse
Affiliation(s)
- Shihori Tanabe
- Division of Risk Assessment, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Edward J Perkins
- Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS 3180, United States
| | - Ryuichi Ono
- Division of Cellular and Molecular Toxicology, Center for Biological Safety and Research, National Institute of Health Sciences, Kawasaki 210-9501, Japan
| | - Hiroki Sasaki
- Department of Clinical Genomics, Fundamental Innovative Oncology Core, National Cancer Center Research Institute, Tokyo 104-0045, Japan
| |
Collapse
|
71
|
Yalchin M, Baker AM, Graham TA, Hart A. Predicting Colorectal Cancer Occurrence in IBD. Cancers (Basel) 2021; 13:2908. [PMID: 34200768 PMCID: PMC8230430 DOI: 10.3390/cancers13122908] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 05/27/2021] [Accepted: 06/01/2021] [Indexed: 12/13/2022] Open
Abstract
Patients with colonic inflammatory bowel disease (IBD) are at an increased risk of developing colorectal cancer (CRC), and are therefore enrolled into a surveillance programme aimed at detecting dysplasia or early cancer. Current surveillance programmes are guided by clinical, endoscopic or histological predictors of colitis-associated CRC (CA-CRC). We have seen great progress in our understanding of these predictors of disease progression, and advances in endoscopic technique and management, along with improved medical care, has been mirrored by the falling incidence of CA-CRC over the last 50 years. However, more could be done to improve our molecular understanding of CA-CRC progression and enable better risk stratification for patients with IBD. This review summarises the known risk factors associated with CA-CRC and explores the molecular landscape that has the potential to complement and optimise the existing IBD surveillance programme.
Collapse
Affiliation(s)
- Mehmet Yalchin
- Inflammatory Bowel Disease Department, St. Mark’s Hospital, Watford R.d., Harrow HA1 3UJ, UK
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse S.q., London EC1M 6BQ, UK; (A.-M.B.); (T.A.G.)
| | - Ann-Marie Baker
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse S.q., London EC1M 6BQ, UK; (A.-M.B.); (T.A.G.)
| | - Trevor A. Graham
- Centre for Genomics and Computational Biology, Barts Cancer Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse S.q., London EC1M 6BQ, UK; (A.-M.B.); (T.A.G.)
| | - Ailsa Hart
- Inflammatory Bowel Disease Department, St. Mark’s Hospital, Watford R.d., Harrow HA1 3UJ, UK
| |
Collapse
|
72
|
Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. MED 2021; 2:642-665. [DOI: 10.1016/j.medj.2021.04.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 03/22/2021] [Accepted: 04/06/2021] [Indexed: 12/24/2022]
|
73
|
Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11114761] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In the screening and identifying of colon and rectum malignancy, computed tomography colonography (CTC) is a highly effective imaging technique, albeit patients receiving a significant effective dose. Accordingly, patient dose evaluation is an important need, seeking to ensure benefits outweigh the projected cancer risk. Objective: For CTC procedures carried out in the Radiology Department, Medical Imaging Operation Services, King Fahad Medical City (KFMC), evaluation is done using the current American College of Radiology (ACR) imaging protocol and concomitant patient-effective doses. Study is carried out on a sample size of 55 CTC procedures, involving 25 males (45%) and 30 females (55%). The patients were classified as follows: two groups based on CT machine; four groups based on the applied protocol; and three groups based on the procedure results. All procedures were carried out using two machines, the products of two different vendors (a GE Healthcare DISCOVERY CT 750 HD 64 slices dual-energy scanner and a Philips Brilliance CT 64 slices scanner). The overall mean, standard deviation (SD), median, and range of the effective dose (in mSv) were 11.57 ± 7.75, 9.25 (2.17–31.93). Automatic tube current modulation (ATCM) shows a significant increase in CTDIvol up to 69% and effective dose (mSv) up to 95% than the manual tube current (mA) compared to the standard protocol. The CT protocol variation results in a three-fold variation in patient-effective dose. The technologist role is crucial in selecting a noise reference based on patient weight and adjusting tube current per slice to avoid overexposure during ATCM protocol.
Collapse
|
74
|
Kim KO, Kim EY. Application of Artificial Intelligence in the Detection and Characterization of Colorectal Neoplasm. Gut Liver 2021; 15:346-353. [PMID: 32773386 PMCID: PMC8129657 DOI: 10.5009/gnl20186] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 06/28/2020] [Indexed: 12/19/2022] Open
Abstract
Endoscpists always have tried to pursue a perfect colonoscopy, and application of artificial intelligence (AI) using deep-learning algorithms is one of the promising supportive options for detection and characterization of colorectal polyps during colonoscopy. Many retrospective studies conducted with real-time application of AI using convolutional neural networks have shown improved colorectal polyp detection. Moreover, a recent randomized clinical trial reported additional polyp detection with shorter analysis time. Studies conducted regarding polyp characterization provided additional promising results. Application of AI with narrow band imaging in real-time prediction of the pathology of diminutive polyps resulted in high diagnostic accuracy. In addition, application of AI with endocytoscopy or confocal laser endomicroscopy was investigated for real-time cellular diagnosis, and the diagnostic accuracy of some studies was comparable to that of pathologists. With AI technology, we can expect a higher polyp detection rate with reduced time and cost by avoiding unnecessary procedures, resulting in enhanced colonoscopy efficiency. However, for AI application in actual daily clinical practice, more prospective studies with minimized selection bias, consensus on standardized utilization, and regulatory approval are needed. (Gut Liver 2021;15:-353)
Collapse
Affiliation(s)
- Kyeong Ok Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Yeungnam University College of Medicine, Daegu, Korea
| | - Eun Young Kim
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea
| |
Collapse
|
75
|
Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L, Qiu Y. Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J Int Med Res 2021; 49:3000605211000157. [PMID: 33771068 PMCID: PMC8165857 DOI: 10.1177/03000605211000157] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Recent advancements in the field of artificial intelligence have demonstrated
success in a variety of clinical tasks secondary to the development and
application of big data, supercomputing, sensor networks, brain science, and
other technologies. However, no projects can yet be used on a large scale in
real clinical practice because of the lack of standardized processes, lack of
ethical and legal supervision, and other issues. We analyzed the existing
problems in the field of artificial intelligence and herein propose possible
solutions. We call for the establishment of a process framework to ensure the
safety and orderly development of artificial intelligence in the medical
industry. This will facilitate the design and implementation of artificial
intelligence products, promote better management via regulatory authorities, and
ensure that reliable and safe artificial intelligence products are selected for
application.
Collapse
Affiliation(s)
- Lushun Jiang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Zhe Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Xiaolan Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yaqiong Zhan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Xuehang Jin
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Li Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.,Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Hangzhou, Zhejiang, People's Republic of China
| |
Collapse
|
76
|
Yang H, Hu B. Application of artificial intelligence to endoscopy on common gastrointestinal benign diseases. Artif Intell Gastrointest Endosc 2021; 2:25-35. [DOI: 10.37126/aige.v2.i2.25] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 03/17/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) has been widely involved in every aspect of healthcare in the preclinical stage. In the digestive system, AI has been trained to assist auxiliary examinations including histopathology, endoscopy, ultrasonography, computerized tomography, and magnetic resonance imaging in detection, diagnosis, classification, differentiation, prognosis, and quality control. In the field of endoscopy, the application of AI, such as automatic detection, diagnosis, classification, and invasion depth, in early gastrointestinal (GI) cancers has received wide attention. There is a paucity of studies of AI application on common GI benign diseases based on endoscopy. In the review, we provide an overview of AI applications to endoscopy on common GI benign diseases including in the esophagus, stomach, intestine, and colon. It indicates that AI will gradually become an indispensable part of normal endoscopic detection and diagnosis of common GI benign diseases as clinical data, algorithms, and other related work are constantly repeated and improved.
Collapse
Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| |
Collapse
|
77
|
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.
Collapse
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;
| |
Collapse
|
78
|
Aniwan S, Vanduangden K, Kerr SJ, Wisedopas N, Kongtab N, Kullavanijaya P, Rerknimitr R. Usefulness of mean number of adenomas per positive screenee for identifying meticulous endoscopists among those who achieve acceptable adenoma detection rates. Endoscopy 2021; 53:394-401. [PMID: 32544957 DOI: 10.1055/a-1201-0226] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND Adenoma detection rate (ADR) is a quality indicator for colonoscopy. However, many missed adenomas have subsequently been identified after colonoscopies performed by endoscopists with ADR ≥ 25 %. Adenomas per positive participant (APP; mean number of adenomas detected by an endoscopist among screenees with positive findings) correlates well inversely with adenoma miss rate. This study aimed to evaluate whether APP added additional information on the detection rate for advanced adenomas (AADR) and proximal adenomas (pADR) and among endoscopists with acceptable ADRs (≥ 25 %). METHODS A total of 47 endoscopists performed 7339 screening colonoscopies that were retrospectively reviewed. Using a cutoff APP value of 2.0, endoscopist performance was classified as high or low APP. Endoscopist ADRs were also classified as acceptable (25 % - 29 %), high standard (30 % - 39 %) and aspirational (≥ 40 %). Generalized linear models were used to assess the relationship between AADR or pADR, and ADR and APP, after adjusting for potential confounders. RESULTS After adjusting for endoscopist performance and patient characteristics, endoscopists with high APP had a significant 2.1 percentage point increase in AADR (95 %CI 0.3 to 3.9; P = 0.02) and a 2.1 percentage point increase in pADR (95 %CI - 0.8 to 5.1; P = 0.15) compared to endoscopists with low APP. In total, 11 (24 %), 18 (38 %), and 18 (38 %) endoscopists were classified as having acceptable, high standard, and aspirational ADRs, respectively. APP values higher than the cutoff were found in 18 %, 44 %, and 72 % of endoscopists with acceptable, high standard, and aspirational ADRs, respectively (P = 0.02). CONCLUSION APP is helpful for identifying more meticulous endoscopists who can detect a greater number of advanced adenomas. Endoscopists who achieved an only acceptable ADR had the lowest APP.
Collapse
Affiliation(s)
- Satimai Aniwan
- Centre of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Kunvadee Vanduangden
- Centre of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Stephen J Kerr
- Biostatistics Excellence Centre, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Naruemon Wisedopas
- Department of Pathology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Natanong Kongtab
- Centre of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Pinit Kullavanijaya
- Centre of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Rungsun Rerknimitr
- Centre of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology, Division of Gastroenterology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| |
Collapse
|
79
|
Cao W, Liang Z, Gao Y, Pomeroy MJ, Han F, Abbasi A, Pickhardt PJ. A dynamic lesion model for differentiation of malignant and benign pathologies. Sci Rep 2021; 11:3485. [PMID: 33568762 PMCID: PMC7875978 DOI: 10.1038/s41598-021-83095-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/20/2021] [Indexed: 11/21/2022] Open
Abstract
Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.
Collapse
Affiliation(s)
- Weiguo Cao
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA
| | - Zhengrong Liang
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA.
- Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, USA.
| | - Yongfeng Gao
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA
| | - Marc J Pomeroy
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA
- Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY, USA
| | - Fangfang Han
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China
| | - Almas Abbasi
- Department of Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA
| | - Perry J Pickhardt
- Department of Radiology, School of Medicine, University of Wisconsin, Madison, WI, USA
| |
Collapse
|
80
|
van der Laan JJH, van der Waaij AM, Gabriëls RY, Festen EAM, Dijkstra G, Nagengast WB. Endoscopic imaging in inflammatory bowel disease: current developments and emerging strategies. Expert Rev Gastroenterol Hepatol 2021; 15:115-126. [PMID: 33094654 DOI: 10.1080/17474124.2021.1840352] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Developments in enhanced and magnified endoscopy have signified major advances in endoscopic imaging of ileocolonic pathology in inflammatory bowel disease (IBD). Artificial intelligence is increasingly being used to augment the benefits of these advanced techniques. Nevertheless, treatment of IBD patients is frustrated by high rates of non-response to therapy, while delayed detection and failures to detect neoplastic lesions impede successful surveillance. A possible solution is offered by molecular imaging, which adds functional imaging data to mucosal morphology assessment through visualizing biological parameters. Other label-free modalities enable visualization beyond the mucosal surface without the need of tracers. AREAS COVERED A literature search up to May 2020 was conducted in PubMed/MEDLINE in order to find relevant articles that involve the (pre-)clinical application of high-definition white light endoscopy, chromoendoscopy, artificial intelligence, confocal laser endomicroscopy, endocytoscopy, molecular imaging, optical coherence tomography, and Raman spectroscopy in IBD. EXPERT OPINION Enhanced and magnified endoscopy have enabled an improved assessment of the ileocolonic mucosa. Implementing molecular imaging in endoscopy could overcome the remaining clinical challenges by giving practitioners a real-time in vivo view of targeted biomarkers. Label-free modalities could help optimize the endoscopic assessment of mucosal healing and dysplasia detection in IBD patients.
Collapse
Affiliation(s)
- Jouke J H van der Laan
- Department of Gastroenterology and Hepatology, University Medical Centre Groningen , Groningen, The Netherlands
| | - Anne M van der Waaij
- Department of Gastroenterology and Hepatology, University Medical Centre Groningen , Groningen, The Netherlands
| | - Ruben Y Gabriëls
- Department of Gastroenterology and Hepatology, University Medical Centre Groningen , Groningen, The Netherlands
| | - Eleonora A M Festen
- Department of Gastroenterology and Hepatology, University Medical Centre Groningen , Groningen, The Netherlands
| | - Gerard Dijkstra
- Department of Gastroenterology and Hepatology, University Medical Centre Groningen , Groningen, The Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, University Medical Centre Groningen , Groningen, The Netherlands
| |
Collapse
|
81
|
Application of Artificial Intelligence in Gastrointestinal Endoscopy. J Clin Gastroenterol 2021; 55:110-120. [PMID: 32925304 DOI: 10.1097/mcg.0000000000001423] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, the research on medical image recognition usually adopts the deep-learning algorithm based on the convolutional neural network. AI has been used in gastrointestinal endoscopy including esophagogastroduodenoscopy, capsule endoscopy, colonoscopy, etc. AI can help endoscopic physicians improve the diagnosis rate of various lesions, reduce the rate of missed diagnosis, improve the quality of endoscopy, assess the severity of the disease, and improve the efficiency of endoscopy. The diversity, susceptibility, and imaging specificity of gastrointestinal endoscopic images are all difficulties and challenges on the road to intelligence. We need more large-scale, high-quality, multicenter prospective studies to explore the clinical applicability of AI, and ethical issues need to be taken into account.
Collapse
|
82
|
Comparison of deep learning and conventional machine learning methods for classification of colon polyp types. EUROBIOTECH JOURNAL 2021. [DOI: 10.2478/ebtj-2021-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Abstract
Determination of polyp types requires tissue biopsy during colonoscopy and then histopathological examination of the microscopic images which tremendously time-consuming and costly. The first aim of this study was to design a computer-aided diagnosis system to classify polyp types using colonoscopy images (optical biopsy) without the need for tissue biopsy. For this purpose, two different approaches were designed based on conventional machine learning (ML) and deep learning. Firstly, classification was performed using random forest approach by means of the features obtained from the histogram of gradients descriptor. Secondly, simple convolutional neural networks (CNN) based architecture was built to train with the colonoscopy images containing colon polyps. The performances of these approaches on two (adenoma & serrated vs. hyperplastic) or three (adenoma vs. hyperplastic vs. serrated) category classifications were investigated. Furthermore, the effect of imaging modality on the classification was also examined using white-light and narrow band imaging systems. The performance of these approaches was compared with the results obtained by 3 novice and 4 expert doctors. Two-category classification results showed that conventional ML approach achieved significantly better than the simple CNN based approach did in both narrow band and white-light imaging modalities. The accuracy reached almost 95% for white-light imaging. This performance surpassed the correct classification rate of all 7 doctors. Additionally, the second task (three-category) results indicated that the simple CNN architecture outperformed both conventional ML based approaches and the doctors. This study shows the feasibility of using conventional machine learning or deep learning based approaches in automatic classification of colon types on colonoscopy images.
Collapse
|
83
|
Milluzzo SM, Cesaro P, Grazioli LM, Olivari N, Spada C. Artificial Intelligence in Lower Gastrointestinal Endoscopy: The Current Status and Future Perspective. Clin Endosc 2021; 54:329-339. [PMID: 33434961 PMCID: PMC8182250 DOI: 10.5946/ce.2020.082] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 12/24/2022] Open
Abstract
The present manuscript aims to review the history, recent advances, evidence, and challenges of artificial intelligence (AI) in colonoscopy. Although it is mainly focused on polyp detection and characterization, it also considers other potential applications (i.e., inflammatory bowel disease) and future perspectives. Some of the most recent algorithms show promising results that are similar to human expert performance. The integration of AI in routine clinical practice will be challenging, with significant issues to overcome (i.e., regulatory, reimbursement). Medico-legal issues will also need to be addressed. With the exception of an AI system that is already available in selected countries (GI Genius; Medtronic, Minneapolis, MN, USA), the majority of the technology is still in its infancy and has not yet been proven to reach a sufficient diagnostic performance to be adopted in the clinical practice. However, larger players will enter the arena of AI in the next few months.
Collapse
Affiliation(s)
- Sebastian Manuel Milluzzo
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Department of Gastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS -Università Cattolica del Sacro Cuore, Roma, Italy
| | - Paola Cesaro
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | | | - Nicola Olivari
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy
| | - Cristiano Spada
- Digestive Endoscopy Unit and Gastroenterology, Fondazione Poliambulanza, Brescia, Italy.,Department of Gastroenterology, Fondazione Policlinico Universitario A. Gemelli IRCCS -Università Cattolica del Sacro Cuore, Roma, Italy
| |
Collapse
|
84
|
Tan X, Li K, Zhang J, Wang W, Wu B, Wu J, Li X, Huang X. Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study. Cancer Cell Int 2021; 21:35. [PMID: 33413391 PMCID: PMC7791865 DOI: 10.1186/s12935-020-01742-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 12/14/2020] [Accepted: 12/25/2020] [Indexed: 12/21/2022] Open
Abstract
Background The incidence rates of cervical cancer in developing countries have been steeply increasing while the medical resources for prevention, detection, and treatment are still quite limited. Computer-based deep learning methods can achieve high-accuracy fast cancer screening. Such methods can lead to early diagnosis, effective treatment, and hopefully successful prevention of cervical cancer. In this work, we seek to construct a robust deep convolutional neural network (DCNN) model that can assist pathologists in screening cervical cancer. Methods ThinPrep cytologic test (TCT) images diagnosed by pathologists from many collaborating hospitals in different regions were collected. The images were divided into a training dataset (13,775 images), validation dataset (2301 images), and test dataset (408,030 images from 290 scanned copies) for training and effect evaluation of a faster region convolutional neural network (Faster R-CNN) system. Results The sensitivity and specificity of the proposed cervical cancer screening system was 99.4 and 34.8%, respectively, with an area under the curve (AUC) of 0.67. The model could also distinguish between negative and positive cells. The sensitivity values of the atypical squamous cells of undetermined significance (ASCUS), the low-grade squamous intraepithelial lesion (LSIL), and the high-grade squamous intraepithelial lesions (HSIL) were 89.3, 71.5, and 73.9%, respectively. This system could quickly classify the images and generate a test report in about 3 minutes. Hence, the system can reduce the burden on the pathologists and saves them valuable time to analyze more complex cases. Conclusions In our study, a CNN-based TCT cervical-cancer screening model was established through a retrospective study of multicenter TCT images. This model shows improved speed and accuracy for cervical cancer screening, and helps overcome the shortage of medical resources required for cervical cancer screening.
Collapse
Affiliation(s)
- Xiangyu Tan
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China
| | - Kexin Li
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China
| | - Jiucheng Zhang
- College of Computer Science & Technology, Zhejiang University, 310027, Hangzhou, China
| | - Wenzhe Wang
- College of Computer Science & Technology, Zhejiang University, 310027, Hangzhou, China
| | - Bian Wu
- Data Science and AI Lab, WeDoctor Group Limited, 311200, Hangzhou, China
| | - Jian Wu
- School of Public Health, Zhejiang University, 310027, Hangzhou, China
| | - Xiaoping Li
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China.
| | - Xiaoyuan Huang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030, Wuhan, Hubei, China.
| |
Collapse
|
85
|
Kou W, Carlson DA, Baumann AJ, Donnan E, Luo Y, Pandolfino JE, Etemadi M. A deep-learning-based unsupervised model on esophageal manometry using variational autoencoder. Artif Intell Med 2021; 112:102006. [PMID: 33581826 DOI: 10.1016/j.artmed.2020.102006] [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: 06/12/2020] [Revised: 10/19/2020] [Accepted: 12/28/2020] [Indexed: 12/27/2022]
Abstract
High-resolution manometry (HRM) is the primary method for diagnosing esophageal motility disorders and its interpretation and classification are based on variables (features) from data of each swallow. Modeling and learning the semantics directly from raw swallow data could not only help automate the feature extraction, but also alleviate the bias from pre-defined features. With more than 32-thousand raw swallow data, a generative model using the approach of variational auto-encoder (VAE) was developed, which, to our knowledge, is the first deep-learning-based unsupervised model on raw esophageal manometry data. The VAE model was reformulated to include different types of loss motivated by domain knowledge and tuned with different hyper-parameters. Training of the VAE model was found sensitive on the learning rate and hence the evidence lower bound objective (ELBO) was further scaled by the data dimension. Case studies showed that the dimensionality of latent space have a big impact on the learned semantics. In particular, cases with 4-dimensional latent variables were found to encode various physiologically meaningful contraction patterns, including strength, propagation pattern as well as sphincter relaxation. Cases with so-called hybrid L2 loss seemed to better capture the coherence of contraction/relaxation transition. Discriminating capability was further evaluated using simple linear discriminative analysis (LDA) on predicting swallow type and swallow pressurization, which yields clustering patterns consistent with clinical impression. The current work on modeling and understanding swallow-level data will guide the development of study-level models for automatic diagnosis as the next stage.
Collapse
Affiliation(s)
- Wenjun Kou
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA.
| | - Dustin A Carlson
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Alexandra J Baumann
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Erica Donnan
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 North Lake Shore Drive, 11th Floor, Chicago, IL 60611, USA
| | - John E Pandolfino
- Department of Medicine, Feinberg School of Medicine, Northwestern University, 676 North Saint Clair Street, 14th Floor, Chicago, IL 60611, USA
| | - Mozziyar Etemadi
- Department of Anesthesiology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA; Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60201, USA
| |
Collapse
|
86
|
Golhar M, Bobrow TL, Khoshknab MP, Jit S, Ngamruengphong S, Durr NJ. Improving Colonoscopy Lesion Classification Using Semi-Supervised Deep Learning. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:631-640. [PMID: 33747680 PMCID: PMC7978231 DOI: 10.1109/access.2020.3047544] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of unlabeled data, and that these representations can improve the performance of supervised tasks. Here, we demonstrate that an unsupervised jigsaw learning task, in combination with supervised training, results in up to a 9.8% improvement in correctly classifying lesions in colonoscopy images when compared to a fully-supervised baseline. We additionally benchmark improvements in domain adaptation and out-of-distribution detection, and demonstrate that semi-supervised learning outperforms supervised learning in both cases. In colonoscopy applications, these metrics are important given the skill required for endoscopic assessment of lesions, the wide variety of endoscopy systems in use, and the homogeneity that is typical of labeled datasets.
Collapse
Affiliation(s)
- Mayank Golhar
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Taylor L Bobrow
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Simran Jit
- Division of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Saowanee Ngamruengphong
- Division of Gastroenterology and Hepatology, Johns Hopkins Hospital, Baltimore, MD 21287, USA
| | - Nicholas J Durr
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| |
Collapse
|
87
|
|
88
|
Ahmad OF, Stassen P, Webster GJ. Artificial intelligence in biliopancreatic endoscopy: Is there any role? Best Pract Res Clin Gastroenterol 2020; 52-53:101724. [PMID: 34172251 DOI: 10.1016/j.bpg.2020.101724] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/22/2020] [Indexed: 01/31/2023]
Abstract
Artificial intelligence (AI) research in endoscopy is being translated at rapid pace with a number of approved devices now available for use in luminal endoscopy. However, the published literature for AI in biliopancreatic endoscopy is predominantly limited to early pre-clinical studies including applications for diagnostic EUS and patient risk stratification. Potential future use cases are highlighted in this manuscript including optical characterisation of strictures during cholangioscopy, prediction of post-ERCP acute pancreatitis and selective biliary duct cannulation difficulty, automated report generation and novel AI-based quality key performance metrics. To realise the full potential of AI and accelerate innovation, it is crucial that robust inter-disciplinary collaborations are formed between biliopancreatic endoscopists and AI researchers.
Collapse
Affiliation(s)
- Omer F Ahmad
- Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, NW1 2BU, United Kingdom; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TS, United Kingdom.
| | - Pauline Stassen
- Erasmus MC University Medical Center, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands
| | - George J Webster
- Department of Gastroenterology, University College London Hospitals NHS Foundation Trust, 250 Euston Road, London, NW1 2BU, United Kingdom
| |
Collapse
|
89
|
Lei S, Wang Z, Tu M, Liu P, Lei L, Xiao X, Zhou G, Liu X, Li L, Wang P. Adenoma detection rate is not influenced by the time of day in computer-aided detection colonoscopy. Medicine (Baltimore) 2020; 99:e23685. [PMID: 33371110 PMCID: PMC7748207 DOI: 10.1097/md.0000000000023685] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Accepted: 11/13/2020] [Indexed: 12/29/2022] Open
Abstract
Because of endoscopist fatigue, the time of colonoscopy have been shown to influence adenoma detection rate (ADR). Computer-aided detection (CADe) provides simultaneous visual alerts on polyps during colonoscopy and thus to increase adenoma detection rate. This is attributable to the strengthening of endoscopists diagnostic level and alleviation of fatigue. The aim of the study was to investigate whether CADe colonoscopy could eliminate the influence of the afternoon fatigue on ADR.We retrospectively analyzed the recorded data of patients who were performed CADe colonoscopy from September 2017 to February 2019 in Endoscopy Center of Sichuan Provincial People's Hospital. Patients demographic as well as baseline data recorded during colonoscopy were used for the analysis. Morning colonoscopy was defined as colonoscopic procedures starting between 8:00 am and 12:00 noon. Afternoon colonoscopy was defined as procedures starting at 2:00 pm and thereafter. The primary outcome was ADR. Univariate analysis and multivariate regression analysis were also performed.A total of 484 CADe colonoscopies were performed by 4 endoscopists in the study. The overall polyp detection rate was 52% and overall ADR was 35.5%. The mean number of adenomas detected per colonoscopy (0.62 vs 0.61, P > .05) and ADR (0.36 vs 0.35, P > .05) were similar in the am and pm group. Multivariable analysis shows that the ADR of CADe colonoscopy was influenced by the age (P < .001), gender (P = .004) and withdrawal time (P < .001), no correlation was found regarding bowel preparation (P = .993) and endoscopist experience (P = .804).CADe colonoscopy could eliminate the influence of the afternoon fatigue on ADR. The ADR during CADe colonoscopy is significantly affected by age, gender and withdrawal time.
Collapse
Affiliation(s)
| | | | - Mengtian Tu
- Department of Internal Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Lei Lei
- Department of Gastroenterology
| | | | | | | | | | - Pu Wang
- Department of Gastroenterology
| |
Collapse
|
90
|
Meher D, Gogoi M, Bharali P, Anirvan P, Singh SP. Artificial Intelligence in Small Bowel Endoscopy: Current Perspectives and Future Directions. JOURNAL OF DIGESTIVE ENDOSCOPY 2020. [DOI: 10.1055/s-0040-1717824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
AbstractArtificial intelligence (AI) is a computer system that is able to perform tasks which normally require human intelligence. The role of AI in the field of gastroenterology has been gradually evolving since its inception in the 1950s. Discovery of wireless capsule endoscopy (WCE) and balloon enteroscopy (BE) has revolutionized small gut imaging. While WCE is a relatively patient-friendly and noninvasive mode to examine the nonobstructed small gut, it is limited by a lengthy examination time and the need for expertise in reading images acquired by the capsule. Similarly, BE, despite having the advantage of therapeutic intervention, is costly, invasive, and requires general sedation. Incorporation of concepts like machine learning and deep learning has been used to handle large amounts of data and images in gastroenterology. Interestingly, in small gut imaging, the application of AI has been limited to WCE only. This review was planned to examine and summarize available published data on various AI-based approaches applied to small bowel disease.
We conducted an extensive literature search using Google search engine, Google Scholar, and PubMed database for published literature in English on the application of different AI techniques in small bowel endoscopy, and have summarized the outcome and benefits of these applications of AI in small bowel endoscopy. Incorporation of AI in WCE has resulted in significant advancements in the detection of various lesions starting from dysplastic mucosa, inflammatory and nonmalignant lesions to the detection of bleeding with increasing accuracy and has shortened the lengthy review time in image analysis. As most of the studies to evaluate AI are retrospective, the presence of inherent selection bias cannot be excluded. Besides, the interpretability (black-box nature) of AI models remains a cause for concern. Finally, issues related to medical ethics and AI need to be judiciously addressed to enable its seamless use in future.
Collapse
Affiliation(s)
- Dinesh Meher
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | - Mrinal Gogoi
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | - Pankaj Bharali
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | - Prajna Anirvan
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | | |
Collapse
|
91
|
Mohan BP, Facciorusso A, Khan SR, Chandan S, Kassab LL, Gkolfakis P, Tziatzios G, Triantafyllou K, Adler DG. Real-time computer aided colonoscopy versus standard colonoscopy for improving adenoma detection rate: A meta-analysis of randomized-controlled trials. EClinicalMedicine 2020; 29-30:100622. [PMID: 33294821 PMCID: PMC7691740 DOI: 10.1016/j.eclinm.2020.100622] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/04/2020] [Accepted: 09/28/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Recent prospective randomized controlled trials have evaluated deep convolutional neural network (CNN) based computer aided detection (CADe) of lesions in real-time colonoscopy. We conducted this meta-analysis to compare the adenoma detection rate (ADR) of deep CNN based CADe assisted colonoscopy to standard colonoscopy (SC) from randomized controlled trials (RCTs). METHODS Multiple databases were searched (from inception to May 2020) and parallel RCTs that compared deep CNN based CADe assisted colonoscopy to SC were included for this analysis. Using Mantel-Haenzel (M-H) random effects model, pooled risk ratios (RR) and mean difference (MD) were calculated. In between study heterogeneity was assessed by I2% values. Outcomes assessed included other per patient adenoma parameters. FINDINGS Six RCTs were included in our final analysis that utilized deep CNN based CADe system in real-time colonoscopy. Total numbers of patients assessed were 4962 (2480 in CADe and 2482 in SC group). CADe based colonoscopy demonstrated statistically higher pooled ADR, RR=1.5 (95% CI 1.3-1.72), p<0.0001, I2=56%; and pooled PDR, RR=1.42 (95% CI 1.33-1.51), p<0.00001, I2=9%; when compared to SC. Per patient adenoma detection parameters were significantly better with CADe colonoscopy when compared to SC, with increased scope withdrawal time (mean difference = 0.38, 95% CI 0.05-0.72, p = 0.02). INTERPRETATION Based on our meta-analysis, deep CNN based CADe colonoscopy achieved significantly higher ADR metrics, albeit with increased scope withdrawal time when compared to SC.
Collapse
Affiliation(s)
- Babu P. Mohan
- Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Antonio Facciorusso
- Gastroenterology Unit, University of Foggia, Foggia, Italy
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Shahab R. Khan
- Gastroenterology, Rush University Medical Center, Chicago, IL, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Saurabh Chandan
- Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Lena L. Kassab
- Internal Medicine, Mayo Clinic, Rochester, MIN, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Paraskevas Gkolfakis
- Hepatogastroenterology Unit, 2nd Dep of Internal Medicine – Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Georgios Tziatzios
- Hepatogastroenterology Unit, 2nd Dep of Internal Medicine – Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, 2nd Dep of Internal Medicine – Propaedeutic Medical School, National and Kapodistrian University of Athens, Athens, Greece
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| | - Douglas G. Adler
- Gastroenterology and Hepatology, University of Utah Health, Salt Lake City, UT, USA
- Gastroenterology and Hepatology, University of Colorado Anshchutz Medical Campus, Aurora, CO, USA
| |
Collapse
|
92
|
Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1:71-85. [DOI: 10.35712/aig.v1.i4.71] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 10/28/2020] [Accepted: 11/12/2020] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) using machine or deep learning algorithms is attracting increasing attention because of its more accurate image recognition ability and prediction performance than human-aid analyses. The application of AI models to gastrointestinal (GI) clinical oncology has been investigated for the past decade. AI has the capacity to automatically detect and diagnose GI tumors with similar diagnostic accuracy to expert clinicians. AI may also predict malignant potential, such as tumor histology, metastasis, patient survival, resistance to cancer treatments and the molecular biology of tumors, through image analyses of radiological or pathological imaging data using complex deep learning models beyond human cognition. The introduction of AI-assisted diagnostic systems into clinical settings is expected in the near future. However, limitations associated with the evaluation of GI tumors by AI models have yet to be resolved. Recent studies on AI-assisted diagnostic models of gastric and colorectal cancers in the endoscopic, pathological, and radiological fields were herein reviewed. The limitations and future perspectives for the application of AI systems in clinical settings have also been discussed. With the establishment of a multidisciplinary team containing AI experts in each medical institution and prospective studies, AI-assisted medical systems will become a promising tool for GI cancer.
Collapse
Affiliation(s)
- Michihiro Kudou
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Kyoto Okamoto Memorial Hospital, Kyoto 613-0034, Japan
| | - Toshiyuki Kosuga
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
- Department of Surgery, Saiseikai Shiga Hospital, Ritto 520-3046, Japan
| | - Eigo Otsuji
- Division of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine, Kyoto 602-8566, Japan
| |
Collapse
|
93
|
van der Sommen F, de Groof J, Struyvenberg M, van der Putten J, Boers T, Fockens K, Schoon EJ, Curvers W, de With P, Mori Y, Byrne M, Bergman JJGHM. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field. Gut 2020; 69:2035-2045. [PMID: 32393540 PMCID: PMC7569393 DOI: 10.1136/gutjnl-2019-320466] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 04/13/2020] [Accepted: 04/22/2020] [Indexed: 02/07/2023]
Abstract
There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.
Collapse
Affiliation(s)
- Fons van der Sommen
- Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, Eindhoven, Noord-Brabant, The Netherlands
| | - Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC—Locatie AMC, Amsterdam, North Holland, The Netherlands
| | - Maarten Struyvenberg
- Department of Gastroenterology and Hepatology, Amsterdam UMC—Locatie AMC, Amsterdam, North Holland, The Netherlands
| | - Joost van der Putten
- Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, Eindhoven, Noord-Brabant, The Netherlands
| | - Tim Boers
- Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, Eindhoven, Noord-Brabant, The Netherlands
| | - Kiki Fockens
- Department of Gastroenterology and Hepatology, Amsterdam UMC—Locatie AMC, Amsterdam, North Holland, The Netherlands
| | - Erik J Schoon
- Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, The Netherlands
| | - Wouter Curvers
- Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, The Netherlands
| | - Peter de With
- Department of Electrical Engineering, VCA Group, University of Technology Eindhoven, Eindhoven, Noord-Brabant, The Netherlands
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Kanagawa, Japan
| | - Michael Byrne
- Division of Gastroenterology, Vancouver General Hospital, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Jacques J G H M Bergman
- Department of Gastroenterology and Hepatology, Amsterdam UMC-Locatie AMC, Amsterdam, North Holland, The Netherlands
| |
Collapse
|
94
|
Sinagra E, Badalamenti M, Maida M, Spadaccini M, Maselli R, Rossi F, Conoscenti G, Raimondo D, Pallio S, Repici A, Anderloni A. Use of artificial intelligence in improving adenoma detection rate during colonoscopy: Might both endoscopists and pathologists be further helped. World J Gastroenterol 2020; 26:5911-5918. [PMID: 33132644 PMCID: PMC7584058 DOI: 10.3748/wjg.v26.i39.5911] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/18/2020] [Accepted: 09/22/2020] [Indexed: 02/06/2023] Open
Abstract
Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.
Collapse
Affiliation(s)
- Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Matteo Badalamenti
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Marcello Maida
- Gastroenterology and Endoscopy Unit, S. Elia-Raimondi Hospital, Caltanissetta 93100, Italy
| | - Marco Spadaccini
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Roberta Maselli
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Francesca Rossi
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Giuseppe Conoscenti
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Dario Raimondo
- Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy
| | - Socrate Pallio
- Endoscopy Unit, AOUP Policlinico G. Martino, Messina 98125, Italy
| | - Alessandro Repici
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| | - Andrea Anderloni
- Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy
| |
Collapse
|
95
|
Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer. J Clin Med 2020; 9:jcm9103313. [PMID: 33076511 PMCID: PMC7602532 DOI: 10.3390/jcm9103313] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 10/09/2020] [Accepted: 10/12/2020] [Indexed: 12/15/2022] Open
Abstract
Globally, colorectal cancer is the third most diagnosed malignancy. It causes significant mortality and morbidity, which can be reduced by early diagnosis with an effective screening test. Integrating artificial intelligence (AI) and computer-aided detection (CAD) with screening methods has shown promising colorectal cancer screening results. AI could provide a “second look” for endoscopists to decrease the rate of missed polyps during a colonoscopy. It can also improve detection and characterization of polyps by integration with colonoscopy and various advanced endoscopic modalities such as magnifying narrow-band imaging, endocytoscopy, confocal endomicroscopy, laser-induced fluorescence spectroscopy, and magnifying chromoendoscopy. This descriptive review discusses various AI and CAD applications in colorectal cancer screening, polyp detection, and characterization.
Collapse
|
96
|
Chadebecq F, Vasconcelos F, Mazomenos E, Stoyanov D. Computer Vision in the Surgical Operating Room. Visc Med 2020; 36:456-462. [PMID: 33447601 DOI: 10.1159/000511934] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 09/30/2020] [Indexed: 12/20/2022] Open
Abstract
Background Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages With the increasing availability of surgical video sources and the convergence of technologies around video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic.
Collapse
Affiliation(s)
- François Chadebecq
- Department of Computer Science, Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
| | - Francisco Vasconcelos
- Department of Computer Science, Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
| | - Evangelos Mazomenos
- Department of Computer Science, Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
| | - Danail Stoyanov
- Department of Computer Science, Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, United Kingdom
| |
Collapse
|
97
|
Attardo S, Chandrasekar VT, Spadaccini M, Maselli R, Patel HK, Desai M, Capogreco A, Badalamenti M, Galtieri PA, Pellegatta G, Fugazza A, Carrara S, Anderloni A, Occhipinti P, Hassan C, Sharma P, Repici A. Artificial intelligence technologies for the detection of colorectal lesions: The future is now. World J Gastroenterol 2020; 26:5606-5616. [PMID: 33088155 PMCID: PMC7545398 DOI: 10.3748/wjg.v26.i37.5606] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 06/30/2020] [Accepted: 09/15/2020] [Indexed: 02/06/2023] Open
Abstract
Several studies have shown a significant adenoma miss rate up to 35% during screening colonoscopy, especially in patients with diminutive adenomas. The use of artificial intelligence (AI) in colonoscopy has been gaining popularity by helping endoscopists in polyp detection, with the aim to increase their adenoma detection rate (ADR) and polyp detection rate (PDR) in order to reduce the incidence of interval cancers. The efficacy of deep convolutional neural network (DCNN)-based AI system for polyp detection has been trained and tested in ex vivo settings such as colonoscopy still images or videos. Recent trials have evaluated the real-time efficacy of DCNN-based systems showing promising results in term of improved ADR and PDR. In this review we reported data from the preliminary ex vivo experiences and summarized the results of the initial randomized controlled trials.
Collapse
Affiliation(s)
- Simona Attardo
- Department of Endoscopy and Digestive Disease, AOU Maggiore della Carità, Novara 28100, Italy
| | | | - Marco Spadaccini
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Roberta Maselli
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Harsh K Patel
- Department of Internal Medicine, Ochsner Clinic Foundation, New Orleans, LA 70124, United States
| | - Madhav Desai
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Antonio Capogreco
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| | - Matteo Badalamenti
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | | | - Gaia Pellegatta
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Alessandro Fugazza
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Silvia Carrara
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Andrea Anderloni
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
| | - Pietro Occhipinti
- Department of Endoscopy and Digestive Disease, AOU Maggiore della Carità, Novara 28100, Italy
| | - Cesare Hassan
- Endoscopy Unit, Nuovo Regina Margherita Hospital, Roma 00153, Italy
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, Kansas City VA Medical Center, Kansas City, MO 66045, United States
| | - Alessandro Repici
- Department of Endoscopy, Humanitas Research Hospital, Rozzano 20089, Italy
- Department of Biomedical Sciences, Humanitas University, Rozzano 20089, Italy
| |
Collapse
|
98
|
Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92:831-839.e8. [PMID: 32334015 DOI: 10.1016/j.gie.2020.04.039] [Citation(s) in RCA: 95] [Impact Index Per Article: 23.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 04/13/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Deep learning is an innovative algorithm based on neural networks. Wireless capsule endoscopy (WCE) is considered the criterion standard for detecting small-bowel diseases. Manual examination of WCE is time-consuming and can benefit from automatic detection using artificial intelligence (AI). We aimed to perform a systematic review of the current literature pertaining to deep learning implementation in WCE. METHODS We conducted a search in PubMed for all original publications on the subject of deep learning applications in WCE published between January 1, 2016 and December 15, 2019. Evaluation of the risk of bias was performed using tailored Quality Assessment of Diagnostic Accuracy Studies-2. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. RESULTS Of the 45 studies retrieved, 19 studies were included. All studies were retrospective. Deep learning applications for WCE included detection of ulcers, polyps, celiac disease, bleeding, and hookworm. Detection accuracy was above 90% for most studies and diseases. Pooled sensitivity and specificity for ulcer detection were .95 (95% confidence interval [CI], .89-.98) and .94 (95% CI, .90-.96), respectively. Pooled sensitivity and specificity for bleeding or bleeding source were .98 (95% CI, .96-.99) and .99 (95% CI, .97-.99), respectively. CONCLUSIONS Deep learning has achieved excellent performance for the detection of a range of diseases in WCE. Notwithstanding, current research is based on retrospective studies with a high risk of bias. Thus, future prospective, multicenter studies are necessary for this technology to be implemented in the clinical use of WCE.
Collapse
|
99
|
Lui TKL, Tsui VWM, Leung WK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest Endosc 2020; 92:821-830.e9. [PMID: 32562608 DOI: 10.1016/j.gie.2020.06.034] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Accepted: 06/07/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-assisted detection is increasingly used in upper endoscopy. We performed a meta-analysis to determine the diagnostic accuracy of AI on detection of gastric and esophageal neoplastic lesions and Helicobacter pylori (HP) status. METHODS We searched Embase, PubMed, Medline, Web of Science, and Cochrane databases for studies on AI detection of gastric or esophageal neoplastic lesions and HP status. After assessing study quality using the Quality Assessment of Diagnostic Accuracy Studies tool, a bivariate meta-analysis following a random-effects model was used to summarize the data and plot hierarchical summary receiver-operating characteristic curves. The diagnostic accuracy was determined by the area under the hierarchical summary receiver-operating characteristic curve (AUC). RESULTS Twenty-three studies including 969,318 images were included. The AUC of AI detection of neoplastic lesions in the stomach, Barrett's esophagus, and squamous esophagus and HP status were .96 (95% confidence interval [CI], .94-.99), .96 (95% CI, .93-.99), .88 (95% CI, .82-.96), and .92 (95% CI, .88-.97), respectively. AI using narrow-band imaging was superior to white-light imaging on detection of neoplastic lesions in squamous esophagus (.92 vs .83, P < .001). The performance of AI was superior to endoscopists in the detection of neoplastic lesions in the stomach (AUC, .98 vs .87; P < .001), Barrett's esophagus (AUC, .96 vs .82; P < .001), and HP status (AUC, .90 vs .82; P < .001). CONCLUSIONS AI is accurate in the detection of upper GI neoplastic lesions and HP infection status. However, most studies were based on retrospective reviews of selected images, which requires further validation in prospective trials.
Collapse
Affiliation(s)
- Thomas K L Lui
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| | - Vivien W M Tsui
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| | - Wai K Leung
- Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong
| |
Collapse
|
100
|
Affiliation(s)
- Steffi E.M. van de Ven
- Department of Gastroenterology and Hepatology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Arjun D. Koch
- Department of Gastroenterology and Hepatology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| |
Collapse
|