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Compare D, Sgamato C, Rocco A, Coccoli P, Donnarumma D, Marchitto SA, Cinque S, Palmieri P, Nardone G. The professional background of a referring physician predicts the diagnostic yield of small bowel capsule endoscopy in suspected small bowel bleeding. Endosc Int Open 2024; 12:E282-E290. [PMID: 38455125 PMCID: PMC10919998 DOI: 10.1055/a-2251-3285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/22/2023] [Indexed: 03/09/2024] Open
Abstract
Background and study aims The diagnostic yield of small-bowel capsule endoscopy (SBCE) in suspected small bowel bleeding (SSBB) is highly variable. Different reimbursement systems and equipment costs also limit SBCE use in clinical practice. Thus, minimizing non-diagnostic procedures is advisable. This study aimed to assess the SBCE diagnostic yield and identify factors predicting diagnostic findings in a cohort of patients with SSBB. Patients and methods In this retrospective cohort study, we analyzed the medical records of patients who consecutively underwent SBCE for SSBB over 9 years. By logistic regression, we identified covariates predicting diagnostic findings at SBCE. Finally, we performed a post-hoc cost analysis based on previous gastroenterologist or endoscopist consultations versus direct SBCE ordering by other specialists. Results The final analysis included 584 patients. Most SBCEs were ordered by a gastroenterologist or endoscopist (74%). The number of SBCEs without any finding was significantly lower in the gastroenterologist/endoscopist group P <0.001). The SBCE diagnostic yield ordered by a gastroenterologist or endoscopist was significantly higher than that by other specialists (63% vs 52%, odds ratio [OR] 1.57; 95% confidence interval [CI] 1.07-2.26, P =0.019). At multivariate analysis, older age (OR 1.7, 95%CI 1.2-2.4, P =0.005), anemia (OR 4.9, 95%CI 1.9-12, P =0.001), small bowel transit time (OR 1, 95%CI 1-1.02, P =0.039), and referring physician (OR 1.8, 95%CI 1.1-2.7, P =0.003) independently predicted diagnostic findings. Implementing prior gastroenterologist or endoscopist referral vs direct SBCE ordering would reduce medical expenditures by 16%. Conclusions The professional background of referring physicians significantly improves the diagnostic yield of SBCE and contributes to controlling public health costs.
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Affiliation(s)
- Debora Compare
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Costantino Sgamato
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Alba Rocco
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Pietro Coccoli
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Durante Donnarumma
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Stefano Andrea Marchitto
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Sofia Cinque
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Pietro Palmieri
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
| | - Gerardo Nardone
- Gastroenterology Unit, Clinical Medicine and Surgery, University of Naples Federico II, Napoli, Italy
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Zhang RY, Qiang PP, Cai LJ, Li T, Qin Y, Zhang Y, Zhao YQ, Wang JP. Automatic detection of small bowel lesions with different bleeding risks based on deep learning models. World J Gastroenterol 2024; 30:170-183. [PMID: 38312122 PMCID: PMC10835517 DOI: 10.3748/wjg.v30.i2.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/15/2023] [Accepted: 12/26/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges. AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups. METHODS The proposed model represents a two-stage method that combined image classification with object detection. First, we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images, normal SB mucosa images, and invalid images. Then, the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding, and the location of the lesion was marked. We constructed training and testing sets and compared model-assisted reading with physician reading. RESULTS The accuracy of the model constructed in this study reached 98.96%, which was higher than the accuracy of other systems using only a single module. The sensitivity, specificity, and accuracy of the model-assisted reading detection of all images were 99.17%, 99.92%, and 99.86%, which were significantly higher than those of the endoscopists' diagnoses. The image processing time of the model was 48 ms/image, and the image processing time of the physicians was 0.40 ± 0.24 s/image (P < 0.001). CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images, which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.
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Affiliation(s)
- Rui-Ya Zhang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Peng-Peng Qiang
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi Province, China
| | - Ling-Jun Cai
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Tao Li
- School of Life Sciences and Technology, Mudanjiang Normal University, Mudanjiang 157011, Heilongjiang Province, China
| | - Yan Qin
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Yu Zhang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Yi-Qing Zhao
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
| | - Jun-Ping Wang
- Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China
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Fantasia S, Cortegoso Valdivia P, Kayali S, Koulaouzidis G, Pennazio M, Koulaouzidis A. The Role of Capsule Endoscopy in the Diagnosis and Management of Small Bowel Tumors: A Narrative Review. Cancers (Basel) 2024; 16:262. [PMID: 38254753 PMCID: PMC10813471 DOI: 10.3390/cancers16020262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/21/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Small bowel tumors (SBT) are relatively rare, but have had a steadily increasing incidence in the last few decades. Small bowel capsule endoscopy (SBCE) and device-assisted enteroscopy are the main endoscopic techniques for the study of the small bowel, the latter additionally providing sampling and therapeutic options, and hence acting complementary to SBCE in the diagnostic work-up. Although a single diagnostic modality is often insufficient in the setting of SBTs, SBCE is a fundamental tool to drive further management towards a definitive diagnosis. The aim of this paper is to provide a concise narrative review of the role of SBCE in the diagnosis and management of SBTs.
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Affiliation(s)
- Stefano Fantasia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - Pablo Cortegoso Valdivia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
| | - Stefano Kayali
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy; (S.F.); (S.K.)
- Department of Medicine and Surgery, University of Parma, 43125 Parma, Italy
| | - George Koulaouzidis
- Department of Biochemical Sciences, Pomeranian Medical University, 70204 Szczecin, Poland;
| | - Marco Pennazio
- University Division of Gastroenterology, City of Health and Science University Hospital, University of Turin, 10126 Turin, Italy;
| | - Anastasios Koulaouzidis
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark;
- Department of Gastroenterology, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70204 Szczecin, Poland
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Wang X, Hu X, Xu Y, Yong J, Li X, Zhang K, Gan T, Yang J, Rao N. A systematic review on diagnosis and treatment of gastrointestinal diseases by magnetically controlled capsule endoscopy and artificial intelligence. Therap Adv Gastroenterol 2023; 16:17562848231206991. [PMID: 37900007 PMCID: PMC10612444 DOI: 10.1177/17562848231206991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/31/2023] Open
Abstract
Background Magnetically controlled capsule endoscopy (MCCE) is a non-invasive, painless, comfortable, and safe equipment to diagnose gastrointestinal diseases (GID), partially overcoming the shortcomings of conventional endoscopy and wireless capsule endoscopy (WCE). With advancements in technology, the main technical parameters of MCCE have continuously been improved, and MCCE has become more intelligent. Objectives The aim of this systematic review was to summarize the research progress of MCCE and artificial intelligence (AI) in the diagnosis and treatment of GID. Data Sources and Methods We conducted a systematic search of PubMed and EMBASE for published studies on GID detection of MCCE, physical factors related to MCCE imaging quality, the application of AI in aiding MCCE, and its additional functions. We synergistically reviewed the included studies, extracted relevant data, and made comparisons. Results MCCE was confirmed to have the same performance as conventional gastroscopy and WCE in detecting common GID, while it lacks research in detecting early gastric cancer (EGC). The body position and cleanliness of the gastrointestinal tract are the main factors affecting imaging quality. The applications of AI in screening intestinal diseases have been comprehensive, while in the detection of common gastric diseases such as ulcers, it has been developed. MCCE can perform some additional functions, such as observations of drug behavior in the stomach and drug damage to the gastric mucosa. Furthermore, it can be improved to perform a biopsy. Conclusion This comprehensive review showed that the MCCE technology has made great progress, but studies on GID detection and treatment by MCCE are in the primary stage. Further studies are required to confirm the performance of MCCE.
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Affiliation(s)
- Xiaotong Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoming Hu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yongxue Xu
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiahao Yong
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiang Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kaixuan Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tao Gan
- Digestive Endoscopic Center of West China Hospital, Sichuan University, Chengdu, China
| | - Jinlin Yang
- Digestive Endoscopic Center of West China Hospital, Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Chengdu, Sichuan Province 610017, China
| | - Nini Rao
- School of Life Science and Technology, University of Electronic Science and Technology of China, No. 4, Section Two, Jianshe North Road, Chengdu 610054, China
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O'Hara FJ, Mc Namara D. Capsule endoscopy with artificial intelligence-assisted technology: Real-world usage of a validated AI model for capsule image review. Endosc Int Open 2023; 11:E970-E975. [PMID: 37828977 PMCID: PMC10567136 DOI: 10.1055/a-2161-1816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 08/25/2023] [Indexed: 10/14/2023] Open
Abstract
Background and study aims Capsule endoscopy is a time-consuming procedure with a significance error rate. Artificial intelligence (AI) can potentially reduce reading time significantly by reducing the number of images that need human review. An OMOM Artificial Intelligence-enabled small bowel capsule has been recently trained and validated for small bowel capsule endoscopy video review. This study aimed to assess its performance in a real-world setting in comparison with standard reading methods. Patients and methods In this single-center retrospective study, 40 patient studies performed using the OMOM capsule were analyzed first with standard reading methods and later using AI-assisted reading. Reading time, pathology identified, intestinal landmark identification and bowel preparation assessment (Brotz Score) were compared. Results Overall diagnosis correlated 100% between the two reading methods. In a per-lesion analysis, 1293 images of significant lesions were identified combining standard and AI-assisted reading methods. AI-assisted reading captured 1268 (98.1%, 95% CI 97.15-98.7) of these findings while standard reading mode captured 1114 (86.2%, 95% confidence interval 84.2-87.9), P < 0.001. Mean reading time went from 29.7 minutes with standard reading to 2.3 minutes with AI-assisted reading ( P < 0.001), for an average time saving of 27.4 minutes per study. Time of first cecal image showed a wide discrepancy between AI and standard reading of 99.2 minutes (r = 0.085, P = 0.68). Bowel cleansing evaluation agreed in 97.4% (r = 0.805 P < 0.001). Conclusions AI-assisted reading has shown significant time savings without reducing sensitivity in this study. Limitations remain in the evaluation of other indicators.
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Affiliation(s)
- Fintan John O'Hara
- Gastroenterology, Tallaght University Hospital, Dublin, Ireland
- Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland
| | - Deirdre Mc Namara
- Gastroenterology, Tallaght University Hospital, Dublin, Ireland
- Medicine, Trinity College Dublin School of Medicine, Dublin, Ireland
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Ribeiro T, Mascarenhas Saraiva MJ, Afonso J, Cardoso P, Mendes F, Martins M, Andrade AP, Cardoso H, Mascarenhas Saraiva M, Ferreira J, Macedo G. Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040810. [PMID: 37109768 PMCID: PMC10145655 DOI: 10.3390/medicina59040810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023]
Abstract
Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50-90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel José Mascarenhas Saraiva
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Afonso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Pedro Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Francisco Mendes
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel Martins
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Ana Patrícia Andrade
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Hélder Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | | | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
| | - Guilherme Macedo
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Mascarenhas M, Afonso J, Ribeiro T, Andrade P, Cardoso H, Macedo G. The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040790. [PMID: 37109748 PMCID: PMC10145124 DOI: 10.3390/medicina59040790] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/27/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023]
Abstract
With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available.
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Affiliation(s)
- Miguel Mascarenhas
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Patrícia Andrade
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Hélder Cardoso
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
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Nakada A, Niikura R, Otani K, Kurose Y, Hayashi Y, Kitamura K, Nakanishi H, Kawano S, Honda T, Hasatani K, Sumiyoshi T, Nishida T, Yamada A, Aoki T, Harada T, Kawai T, Fujishiro M. Improved Object Detection Artificial Intelligence Using the Revised RetinaNet Model for the Automatic Detection of Ulcerations, Vascular Lesions, and Tumors in Wireless Capsule Endoscopy. Biomedicines 2023; 11:biomedicines11030942. [PMID: 36979921 PMCID: PMC10046454 DOI: 10.3390/biomedicines11030942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/10/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023] Open
Abstract
The use of computer-aided detection models to diagnose lesions in images from wireless capsule endoscopy (WCE) is a topical endoscopic diagnostic solution. We revised our artificial intelligence (AI) model, RetinaNet, to better diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. RetinaNet was trained using the data of 1234 patients, consisting of images of 6476 erosions and ulcers, 1916 vascular lesions, 7127 tumors, and 14,014,149 normal tissues. The mean area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for each lesion were evaluated using five-fold stratified cross-validation. Each cross-validation set consisted of between 6,647,148 and 7,267,813 images from 217 patients. The mean AUC values were 0.997 for erosions and ulcers, 0.998 for vascular lesions, and 0.998 for tumors. The mean sensitivities were 0.919, 0.878, and 0.876, respectively. The mean specificities were 0.936, 0.969, and 0.937, and the mean accuracies were 0.930, 0.962, and 0.924, respectively. We developed a new version of an AI-based diagnostic model for the multiclass identification of small bowel lesions in WCE images to help endoscopists appropriately diagnose small intestine diseases in daily clinical practice.
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Affiliation(s)
- Ayako Nakada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
| | - Ryota Niikura
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
- Department of Gastroenterological Endoscopy, Tokyo Medical University Hospital, Tokyo 1600023, Japan
| | - Keita Otani
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 1538904, Japan
| | - Yusuke Kurose
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 1538904, Japan
| | - Yoshito Hayashi
- Department of Gastroenterology and Hepatology, Osaka University Graduate School of Medicine, Osaka 5650871, Japan
| | - Kazuya Kitamura
- Department of Gastroenterology, Kanazawa University Hospital, 9208641 Kanazawa, Japan
| | - Hiroyoshi Nakanishi
- Department of Gastroenterology, Ishikawa Prefectural Central Hospital, Ishikawa 9208530, Japan
| | - Seiji Kawano
- Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama 7008558, Japan
| | - Testuya Honda
- Department of Gastroenterology, Nagasaki Harbor Medical Center, 8508555 Nagasaki, Japan
| | - Kenkei Hasatani
- Department of Gastroenterology, Fukui Prefectural Hospital, Fukui 9108526, Japan
| | - Tetsuya Sumiyoshi
- The Center for Digestive Disease, Tonan Hospital, Sapporo 0600004, Japan
| | - Tsutomu Nishida
- Department of Gastroenterology, Toyonaka Municipal Hospital, 5608565 Toyonaka, Japan
| | - Atsuo Yamada
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
| | - Tomonori Aoki
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
| | - Tatsuya Harada
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 1538904, Japan
| | - Takashi Kawai
- Department of Gastroenterological Endoscopy, Tokyo Medical University Hospital, Tokyo 1600023, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo 1138655, Japan
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Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple. Diagnostics (Basel) 2023; 13:diagnostics13061038. [PMID: 36980347 PMCID: PMC10047552 DOI: 10.3390/diagnostics13061038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 02/07/2023] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.
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10
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Cortegoso Valdivia P, Deding U, Bjørsum-Meyer T, Baatrup G, Fernández-Urién I, Dray X, Boal-Carvalho P, Ellul P, Toth E, Rondonotti E, Kaalby L, Pennazio M, Koulaouzidis A. Inter/Intra-Observer Agreement in Video-Capsule Endoscopy: Are We Getting It All Wrong? A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12102400. [PMID: 36292089 PMCID: PMC9600122 DOI: 10.3390/diagnostics12102400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 12/09/2022] Open
Abstract
Video-capsule endoscopy (VCE) reading is a time- and energy-consuming task. Agreement on findings between readers (either different or the same) is a crucial point for increasing performance and providing valid reports. The aim of this systematic review with meta-analysis is to provide an evaluation of inter/intra-observer agreement in VCE reading. A systematic literature search in PubMed, Embase and Web of Science was performed throughout September 2022. The degree of observer agreement, expressed with different test statistics, was extracted. As different statistics are not directly comparable, our analyses were stratified by type of test statistics, dividing them in groups of “None/Poor/Minimal”, “Moderate/Weak/Fair”, “Good/Excellent/Strong” and “Perfect/Almost perfect” to report the proportions of each. In total, 60 studies were included in the analysis, with a total of 579 comparisons. The quality of included studies, assessed with the MINORS score, was sufficient in 52/60 studies. The most common test statistics were the Kappa statistics for categorical outcomes (424 comparisons) and the intra-class correlation coefficient (ICC) for continuous outcomes (73 comparisons). In the overall comparison of inter-observer agreement, only 23% were evaluated as “good” or “perfect”; for intra-observer agreement, this was the case in 36%. Sources of heterogeneity (high, I2 81.8–98.1%) were investigated with meta-regressions, showing a possible role of country, capsule type and year of publication in Kappa inter-observer agreement. VCE reading suffers from substantial heterogeneity and sub-optimal agreement in both inter- and intra-observer evaluation. Artificial-intelligence-based tools and the adoption of a unified terminology may progressively enhance levels of agreement in VCE reading.
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Affiliation(s)
- Pablo Cortegoso Valdivia
- Gastroenterology and Endoscopy Unit, University Hospital of Parma, University of Parma, 43126 Parma, Italy
- Correspondence:
| | - Ulrik Deding
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Surgery, Odense University Hospital, 5000 Odense, Denmark
| | - Thomas Bjørsum-Meyer
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Surgery, Odense University Hospital, 5000 Odense, Denmark
| | - Gunnar Baatrup
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Surgery, Odense University Hospital, 5000 Odense, Denmark
| | | | - Xavier Dray
- Center for Digestive Endoscopy, Sorbonne University, Saint Antoine Hospital, APHP, 75012 Paris, France
| | - Pedro Boal-Carvalho
- Gastroenterology Department, Hospital da Senhora da Oliveira, Creixomil, 4835 Guimarães, Portugal
| | - Pierre Ellul
- Division of Gastroenterology, Mater Dei Hospital, 2090 Msida, Malta
| | - Ervin Toth
- Department of Gastroenterology, Skåne University Hospital, Lund University, 20502 Malmö, Sweden
| | | | - Lasse Kaalby
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Surgery, Odense University Hospital, 5000 Odense, Denmark
| | - Marco Pennazio
- University Division of Gastroenterology, City of Health and Science University Hospital, University of Turin, 10126 Turin, Italy
| | - Anastasios Koulaouzidis
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Medicine, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Surgical Research Unit, OUH, 5000 Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70-204 Szczecin, Poland
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11
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Son G, Eo T, An J, Oh DJ, Shin Y, Rha H, Kim YJ, Lim YJ, Hwang D. Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering. Diagnostics (Basel) 2022; 12:diagnostics12081858. [PMID: 36010210 PMCID: PMC9406835 DOI: 10.3390/diagnostics12081858] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/28/2022] [Accepted: 07/28/2022] [Indexed: 12/22/2022] Open
Abstract
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to apply automated small bowel lesion detection algorithms based on deep learning. The purpose of the study was to develop an automated small bowel detection method from long untrimmed videos captured from WCE. Through this, the stomach and colon can also be distinguished. The proposed method is based on a convolutional neural network (CNN) with a temporal filtering on the predicted probabilities from the CNN. For CNN, we use a ResNet50 model to classify three organs including stomach, small bowel, and colon. The hybrid temporal filter consisting of a Savitzky–Golay filter and a median filter is applied to the temporal probabilities for the “small bowel” class. After filtering, the small bowel and the other two organs are differentiated with thresholding. The study was conducted on dataset of 200 patients (100 normal and 100 abnormal WCE cases), which was divided into a training set of 140 cases, a validation set of 20 cases, and a test set of 40 cases. For the test set of 40 patients (20 normal and 20 abnormal WCE cases), the proposed method showed accuracy of 99.8% in binary classification for the small bowel. Transition time errors for gastrointestinal tracts were only 38.8 ± 25.8 seconds for the transition between stomach and small bowel and 32.0 ± 19.1 seconds for the transition between small bowel and colon, compared to the ground truth organ transition points marked by two experienced gastroenterologists.
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Affiliation(s)
- Geonhui Son
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Taejoon Eo
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Jiwoong An
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Dong Jun Oh
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
| | - Yejee Shin
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - Hyenogseop Rha
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
| | - You Jin Kim
- IntroMedic, Capsule Endoscopy Medical Device Manufacturer, Seoul 08375, Korea;
| | - Yun Jeong Lim
- Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang 10326, Korea;
- Correspondence: (Y.J.L.); (D.H.)
| | - Dosik Hwang
- School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea; (G.S.); (T.E.); (J.A.); (Y.S.); (H.R.)
- Center for Healthcare Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Korea
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul 03722, Korea
- Department of Radiology and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, Seoul 03722, Korea
- Correspondence: (Y.J.L.); (D.H.)
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12
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Piccirelli S, Mussetto A, Bellumat A, Cannizzaro R, Pennazio M, Pezzoli A, Bizzotto A, Fusetti N, Valiante F, Hassan C, Pecere S, Koulaouzidis A, Spada C. New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times. Diagnostics (Basel) 2022; 12:diagnostics12081783. [PMID: 35892494 PMCID: PMC9332221 DOI: 10.3390/diagnostics12081783] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/15/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND: Reading capsule endoscopy (CE) is time-consuming. The Express View (EV) (IntroMedic, Seoul, Korea) software was designed to shorten CE video reading. Our primary aim was to evaluate the diagnostic accuracy of EV in detecting significant small-bowel (SB) lesions. We also compared the reading times with EV mode and standard reading (SR). METHODS: 126 patients with suspected SB bleeding and/or suspected neoplasia were prospectively enrolled and underwent SB CE (MiroCam®1200, IntroMedic, Seoul, Korea). CE evaluation was performed in standard and EV mode. In case of discrepancies between SR and EV readings, a consensus was reached after reviewing the video segments and the findings were re-classified. RESULTS: The completion rate of SB CE in our cohort was 86.5% and no retention occurred. The per-patient analysis of sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of EV compared to SR were 86%, 86%, 90%, 81%, and 86%, respectively, before consensus. After consensus, they increased to 97%, 100%, 100%, 96%, and 98%, respectively. The median reading time with SR and EV was 71 min (range 26−340) and 13 min (range 3−85), respectively (p < 0.001). CONCLUSIONS: The new-generation EV shows high diagnostic accuracy and significantly reduces CE reading times.
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Affiliation(s)
- Stefania Piccirelli
- Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (S.P.); (A.B.); (C.S.)
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | | | | | | | - Marco Pennazio
- Division of Gastroenterology, University City of Health and Science University Hospital, 10121 Turin, Italy;
| | - Alessandro Pezzoli
- Endoscopy Unit, Department of Gastroenterology, Sant’Anna University Hospital, 44121 Ferrara, Italy; (A.P.); (N.F.)
| | - Alessandra Bizzotto
- Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (S.P.); (A.B.); (C.S.)
| | - Nadia Fusetti
- Endoscopy Unit, Department of Gastroenterology, Sant’Anna University Hospital, 44121 Ferrara, Italy; (A.P.); (N.F.)
| | | | - Cesare Hassan
- Endoscopy Unit, Department of Gastroenterology, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Silvia Pecere
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
- Digestive Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
- Correspondence:
| | - Anastasios Koulaouzidis
- Department of Medicine, Odense University Hospital Svendborg Sygehus, 5700 Svendborg, Denmark;
- Department of Clinical Research, University of Southern Denmark (SDU), 5230 Odense, Denmark
- Surgical Research Unit, Odense University Hospital, 5000 Odense, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70-204 Szczecin, Poland
| | - Cristiano Spada
- Fondazione Poliambulanza Istituto Ospedaliero, 25124 Brescia, Italy; (S.P.); (A.B.); (C.S.)
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
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Alemanni LV, Fabbri S, Rondonotti E, Mussetto A. Recent developments in small bowel endoscopy: the "black box" is now open! Clin Endosc 2022; 55:473-479. [PMID: 35831981 PMCID: PMC9329645 DOI: 10.5946/ce.2022.113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/11/2022] [Indexed: 12/09/2022] Open
Abstract
Over the last few years, capsule endoscopy has been established as a fundamental device in the practicing gastroenterologist’s toolbox. Its utilization in diagnostic algorithms for suspected small bowel bleeding, Crohn’s disease, and small bowel tumors has been approved by several guidelines. The advent of double-balloon enteroscopy has significantly increased the therapeutic possibilities and release of multiple devices (single-balloon enteroscopy and spiral enteroscopy) aimed at improving the performance of small bowel enteroscopy. Recently, some important innovations have appeared in the small bowel endoscopy scene, providing further improvement to its evolution. Artificial intelligence in capsule endoscopy should increase diagnostic accuracy and reading efficiency, and the introduction of motorized spiral enteroscopy into clinical practice could also improve the therapeutic yield. This review focuses on the most recent studies on artificial-intelligence-assisted capsule endoscopy and motorized spiral enteroscopy.
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Affiliation(s)
- Luigina Vanessa Alemanni
- Gastroenterology Unit, Santa Maria delle Croci Hospital, Ravenna, Italy.,Department of Medical and Surgical Sciences, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Stefano Fabbri
- Gastroenterology Unit, Santa Maria delle Croci Hospital, Ravenna, Italy.,Department of Medical and Surgical Sciences, S. Orsola-Malpighi Hospital, Bologna, Italy
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Chetcuti Zammit S, Sidhu R. Artificial intelligence within the small bowel: are we lagging behind? Curr Opin Gastroenterol 2022; 38:307-317. [PMID: 35645023 DOI: 10.1097/mog.0000000000000827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
PURPOSE OF REVIEW The use of artificial intelligence in small bowel capsule endoscopy is expanding. This review focusses on the use of artificial intelligence for small bowel pathology compared with human data and developments to date. RECENT FINDINGS The diagnosis and management of small bowel disease has been revolutionized with the advent of capsule endoscopy. Reading of capsule endoscopy videos however is time consuming with an average reading time of 40 min. Furthermore, the fatigued human eye may miss subtle lesions including indiscreet mucosal bulges. In recent years, artificial intelligence has made significant progress in the field of medicine including gastroenterology. Machine learning has enabled feature extraction and in combination with deep neural networks, image classification has now materialized for routine endoscopy for the clinician. SUMMARY Artificial intelligence is in built within the Navicam-Ankon capsule endoscopy reading system. This development will no doubt expand to other capsule endoscopy platforms and capsule endoscopies that are used to visualize other parts of the gastrointestinal tract as a standard. This wireless and patient friendly technique combined with rapid reading platforms with the help of artificial intelligence will become an attractive and viable choice to alter how patients are investigated in the future.
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Affiliation(s)
| | - Reena Sidhu
- Academic Department of Gastroenterology, Royal Hallamshire Hospital
- Academic Unit of Gastroenterology, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
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15
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Koulaouzidis A, Bjørsum-Meyer T, Toth E. Real-life practice data on colon capsule endoscopy: We need them fast! Endosc Int Open 2022; 10:E230-E231. [PMID: 35295244 PMCID: PMC8920598 DOI: 10.1055/a-1728-9371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/28/2022] Open
Affiliation(s)
- Anastasios Koulaouzidis
- Department of Medicine, OUH Svendborg, Sygehus, Denmark,Department of Clinical Research, University, of Southern Denmark (SDU), Denmark,Department of Social Medicine & Public Health, Pomeranian Medical University, Poland
| | | | - Ervin Toth
- Department of Gastroenterology, Skåne University Hospital, Malmö, Lund University, Sweden
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16
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Leenhardt R, Fernandez-Urien Sainz I, Rondonotti E, Toth E, Van de Bruaene C, Baltes P, Rosa BJ, Triantafyllou K, Histace A, Koulaouzidis A, Dray X. PEACE: Perception and Expectations toward Artificial Intelligence in Capsule Endoscopy. J Clin Med 2021; 10:jcm10235708. [PMID: 34884410 PMCID: PMC8658716 DOI: 10.3390/jcm10235708] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 11/22/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022] Open
Abstract
Artificial intelligence (AI) has shown promising results in digestive endoscopy, especially in capsule endoscopy (CE). However, some physicians still have some difficulties and fear the advent of this technology. We aimed to evaluate the perceptions and current sentiments toward the use of AI in CE. An online survey questionnaire was sent to an audience of gastroenterologists. In addition, several European national leaders of the International CApsule endoscopy REsearch (I CARE) Group were asked to disseminate an online survey among their national communities of CE readers (CER). The survey included 32 questions regarding general information, perceptions of AI, and its use in daily life, medicine, endoscopy, and CE. Among 380 European gastroenterologists who answered this survey, 333 (88%) were CERs. The mean average time length of experience in CE reading was 9.9 years (0.5–22). A majority of CERs agreed that AI would positively impact CE, shorten CE reading time, and help standardize reporting in CE and characterize lesions seen in CE. Nevertheless, in the foreseeable future, a majority of CERs disagreed with the complete replacement all CE reading by AI. Most CERs believed in the high potential of AI for becoming a valuable tool for automated diagnosis and for shortening the reading time. Currently, the perception is that AI will not replace CE reading.
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Affiliation(s)
- Romain Leenhardt
- Endoscopy Unit, Saint Antoine Hospital, Sorbonne University, APHP, 75012 Paris, France;
- ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, 95000 Cergy-Pontoise, France;
| | | | | | - Ervin Toth
- Department of Gastroenterology, Skane University Hospital, Lund University, 214 28 Malmo, Sweden;
| | | | - Peter Baltes
- Klinik für Innere Medizin, Agaplesion Bethesda Krankenhaus Bergedorf, 21029 Hamburg, Germany;
| | - Bruno Joel Rosa
- Department of Gastroenterology, Hospital da Senhora da Oliveira, 4835-044 Guimarães, Portugal;
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, 4704-553 Braga, Portugal
| | - Konstantinos Triantafyllou
- Hepatogastroenterology Unit, Second Department of Internal Propaedeutic Medicine, Attikon University General Hospital, Medical School, National and Kapodistrian University of Athens, 10679 Athens, Greece;
| | - Aymeric Histace
- ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, 95000 Cergy-Pontoise, France;
| | - Anastasios Koulaouzidis
- Department of Social Medicine & Public Health, Faculty of Health Sciences, Pomeranian Medical University, 70-204 Szczecin, Poland;
| | - Xavier Dray
- Endoscopy Unit, Saint Antoine Hospital, Sorbonne University, APHP, 75012 Paris, France;
- ETIS UMR 8051, CY Paris Cergy University, ENSEA, CNRS, 95000 Cergy-Pontoise, France;
- Correspondence: ; Tel.: +33-149282000
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Mascarenhas Saraiva MJ, Afonso J, Ribeiro T, Ferreira J, Cardoso H, Andrade AP, Parente M, Natal R, Mascarenhas Saraiva M, Macedo G. Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network. BMJ Open Gastroenterol 2021; 8:bmjgast-2021-000753. [PMID: 34580155 PMCID: PMC8477239 DOI: 10.1136/bmjgast-2021-000753] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 09/15/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images. DESIGN We developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin's classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS A total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential. CONCLUSION We developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.
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Affiliation(s)
- Miguel José Mascarenhas Saraiva
- Department of Gastroenterology, Hospital São João, Porto, Portugal .,Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal.,University of Porto Faculty of Medicine, Porto, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, Hospital São João, Porto, Portugal.,Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal
| | - Tiago Ribeiro
- Department of Gastroenterology, Hospital São João, Porto, Portugal.,Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Helder Cardoso
- Department of Gastroenterology, Hospital São João, Porto, Portugal.,Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal.,University of Porto Faculty of Medicine, Porto, Porto, Portugal
| | - Ana Patricia Andrade
- Department of Gastroenterology, Hospital São João, Porto, Portugal.,Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal.,University of Porto Faculty of Medicine, Porto, Porto, Portugal
| | - Marco Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Renato Natal
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.,INEGI - Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | | | - Guilherme Macedo
- Department of Gastroenterology, Hospital São João, Porto, Portugal.,Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, Porto, Portugal.,University of Porto Faculty of Medicine, Porto, Porto, Portugal
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