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Shukla A, Chaudhary R, Nayyar N. Role of artificial intelligence in gastrointestinal surgery. Artif Intell Cancer 2024; 5:97317. [DOI: 10.35713/aic.v5.i2.97317] [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: 05/28/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 09/05/2024] Open
Abstract
Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.
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Affiliation(s)
- Ankit Shukla
- Department of Surgery, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
| | - Rajesh Chaudhary
- Department of Renal Transplantation, Dr Rajendra Prasad Government Medical College, Kangra 176001, India
| | - Nishant Nayyar
- Department of Radiology, Dr Rajendra Prasad Government Medical College, Kangra 176001, Himachal Pradesh, India
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Turtoi DC, Brata VD, Incze V, Ismaiel A, Dumitrascu DI, Militaru V, Munteanu MA, Botan A, Toc DA, Duse TA, Popa SL. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. J Clin Med 2024; 13:4818. [PMID: 39200959 PMCID: PMC11355427 DOI: 10.3390/jcm13164818] [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: 07/23/2024] [Revised: 08/11/2024] [Accepted: 08/13/2024] [Indexed: 09/02/2024] Open
Abstract
Background and Objective: Gastritis represents one of the most prevalent gastrointestinal diseases and has a multifactorial etiology, many forms of manifestation, and various symptoms. Diagnosis of gastritis is made based on clinical, endoscopic, and histological criteria, and although it is a thorough process, many cases are misdiagnosed or overlooked. This systematic review aims to provide an extensive overview of current artificial intelligence (AI) applications in gastritis diagnosis and evaluate the precision of these systems. This evaluation could highlight the role of AI as a helpful and useful tool in facilitating timely and accurate diagnoses, which in turn could improve patient outcomes. Methods: We have conducted an extensive and comprehensive literature search of PubMed, Scopus, and Web of Science, including studies published until July 2024. Results: Despite variations in study design, participant numbers and characteristics, and outcome measures, our observations suggest that implementing an AI automatic diagnostic tool into clinical practice is currently feasible, with the current systems achieving high levels of accuracy, sensitivity, and specificity. Our findings indicate that AI outperformed human experts in most studies, with multiple studies exhibiting an accuracy of over 90% for AI compared to human experts. These results highlight the significant potential of AI to enhance diagnostic accuracy and efficiency in gastroenterology. Conclusions: AI-based technologies can now automatically diagnose using images provided by gastroscopy, digital pathology, and radiology imaging. Deep learning models exhibited high levels of accuracy, sensitivity, and specificity while assessing the diagnosis, staging, and risk of neoplasia for different types of gastritis, results that are superior to those of human experts in most studies.
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Affiliation(s)
- Daria Claudia Turtoi
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Vlad Dumitru Brata
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Victor Incze
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Abdulrahman Ismaiel
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (A.I.); (S.L.P.)
| | - Dinu Iuliu Dumitrascu
- Department of Anatomy, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Valentin Militaru
- Department of Internal Medicine, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania;
| | - Mihai Alexandru Munteanu
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410087 Oradea, Romania;
| | - Alexandru Botan
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Dan Alexandru Toc
- Department of Microbiology, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania;
| | - Traian Adrian Duse
- Faculty of Medicine, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (D.C.T.); (V.I.); (A.B.); (T.A.D.)
| | - Stefan Lucian Popa
- 2nd Medical Department, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400000 Cluj-Napoca, Romania; (A.I.); (S.L.P.)
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Kikuchi R, Okamoto K, Ozawa T, Shibata J, Ishihara S, Tada T. Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms. Digestion 2024:1-17. [PMID: 39068926 DOI: 10.1159/000540251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or noncancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated. SUMMARY In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation. KEY MESSAGES Most analyses of endoscopic AI systems suggested that their performance was better than that of nonexpert endoscopists and equivalent to that of expert endoscopists. Thus, endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.
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Affiliation(s)
- Ryosuke Kikuchi
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazuaki Okamoto
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tsuyoshi Ozawa
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Saitama, Japan
- AI Medical Service Inc., Tokyo, Japan
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Matsubayashi CO, Cheng S, Hulchafo I, Zhang Y, Tada T, Buxbaum JL, Ochiai K. Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market. Dig Liver Dis 2024; 56:1156-1163. [PMID: 38763796 DOI: 10.1016/j.dld.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/21/2024]
Abstract
Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.
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Affiliation(s)
- Carolina Ogawa Matsubayashi
- Endoscopy Unit, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, University of São Paulo, São Paulo, Brasil; AI Medical Service Inc., Tokyo, Japan.
| | - Shuyan Cheng
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Ismael Hulchafo
- Columbia University School of Nursing, New York, NY 10032, USA
| | - Yifan Zhang
- Department of Population Health Science, Weill Cornell Medical College, New York, NY 10065, USA
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan; Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - James L Buxbaum
- Division of Gastrointestinal and Liver Diseases, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Department of Colon and Rectal Surgery, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
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Nijjar GS, Aulakh SK, Singh R, Chandi SK. Emerging Technologies in Endoscopy for Gastrointestinal Neoplasms: A Comprehensive Overview. Cureus 2024; 16:e62946. [PMID: 39044885 PMCID: PMC11265259 DOI: 10.7759/cureus.62946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2024] [Indexed: 07/25/2024] Open
Abstract
Gastrointestinal neoplasms are a growing global health concern, requiring prompt identification and treatment. Endoscopic procedures have revolutionized the detection and treatment of gastrointestinal tumors by providing accurate, minimally invasive methods. Early-stage malignancies can be treated with endoscopic excision, leading to improved outcomes and increased survival rates. Precancerous lesions, like adenomatous polyps, can be prevented by removing them, reducing cancer occurrence and death rates. Advanced techniques like chromoendoscopy, narrow-band imaging, and confocal laser endomicroscopy improve the ability to see the mucosa surface and diagnose conditions. Artificial Intelligence (AI) applications in endoscopy can enhance diagnostic accuracy and predict histology outcomes. However, challenges remain in accurately defining lesions and ensuring precise diagnosis and treatment selection. Molecular imaging approaches and therapeutic modalities like photodynamic therapy and endoscopic ultrasonography-guided therapies hold potential but require further study and clinical confirmation. This study examines the future prospects and obstacles in endoscopic procedures for the timely identification and treatment of gastrointestinal cancers. The focus is on developing technology, limits, and prospective effects on clinical practice.
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Affiliation(s)
| | - Smriti Kaur Aulakh
- Internal Medicine, Sri Guru Ram Das University of Health Science and Research, Amritsar, IND
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Shimada Y, Ojima T, Takaoka Y, Sugano A, Someya Y, Hirabayashi K, Homma T, Kitamura N, Akemoto Y, Tanabe K, Sato F, Yoshimura N, Tsuchiya T. Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning. Surg Today 2024; 54:540-550. [PMID: 37864054 DOI: 10.1007/s00595-023-02756-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 09/13/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically. METHODS Two deep learning models, one based on a convolutional neural network (CNN) and the other based on a vision transformer (ViT), were applied and trained via 463 images (VPI negative: 269 images, VPI positive: 194 images) captured from surgical videos of 81 patients. Model performances were validated via an independent test dataset containing 46 images (VPI negative: 28 images, VPI positive: 18 images) from 46 test patients. RESULTS The areas under the receiver operating characteristic curves of the CNN-based and ViT-based models were 0.77 and 0.84 (p = 0.304), respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 73.91, 83.33, 67.86, 62.50, and 86.36% for the CNN-based model and 78.26, 77.78, 78.57, 70.00, and 84.62% for the ViT-based model, respectively. These models' diagnostic abilities were comparable to those of board-certified thoracic surgeons and tended to be superior to those of non-board-certified thoracic surgeons. CONCLUSION The deep learning model systems can be utilized in clinical applications via data expansion.
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Affiliation(s)
- Yoshifumi Shimada
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Toshihiro Ojima
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yutaka Takaoka
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Aki Sugano
- Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
- Center for Clinical Research, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Yoshiaki Someya
- Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan
| | - Kenichi Hirabayashi
- Department of Diagnostic Pathology, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Takahiro Homma
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoya Kitamura
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Yushi Akemoto
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Keitaro Tanabe
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Fumitaka Sato
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Naoki Yoshimura
- Department of Cardiovascular Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan
| | - Tomoshi Tsuchiya
- Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
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Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
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Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
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Horiuchi Y, Hirasawa T, Fujisaki J. Endoscopic Features of Undifferentiated-Type Early Gastric Cancer in Patients with Helicobacter pylori-Uninfected or -Eradicated Stomachs: A Comprehensive Review. Gut Liver 2024; 18:209-217. [PMID: 37855088 PMCID: PMC10938157 DOI: 10.5009/gnl230106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 05/10/2023] [Accepted: 05/23/2023] [Indexed: 03/16/2024] Open
Abstract
Since the indications for endoscopic submucosal dissection have been expanded to include undifferentiated-type early gastric cancers, improvements in preoperative diagnostic ability have been an area of research. There are also concerns about the impact on the diagnosis of Helicobacter pylori infection. Based on our previous studies, in undifferentiated-type early gastric cancers, magnifying endoscopy with narrow-band imaging is useful for delineating the demarcation regardless of the tumor size. Additionally, inflammatory cell infiltration appears to be a cause of misdiagnosis, suggesting that the resolution of inflammation could contribute to the accurate diagnosis of demarcations. As such, the accuracy of demarcation in eradicated and uninfected cases is higher than that in non-eradicated cases. The common features of the endoscopic findings were discoloration under white-light imaging and a predominance of sites in the lower and middle regions. The uninfected group was characterized by smaller tumor size, flat type, more extended intervening parts in magnifying endoscopy with narrow-band imaging, and pure signet ring cell carcinoma. In contrast, the eradication and non-eradication groups were characterized by larger tumor size, depressed type, and wavy microvessels in magnifying endoscopy with narrow-band imaging. In this comprehensive review, as described above, we discuss the diagnosis of demarcation of undifferentiated-type early gastric cancers, undifferentiated-type early gastric cancers that developed following H. pylori eradication, and H. pylori-uninfected undifferentiated-type early gastric cancers, with a focus on studies with self-examination and endoscopic findings and describe the future direction.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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Sierra-Jerez F, Martinez F. A non-aligned translation with a neoplastic classifier regularization to include vascular NBI patterns in standard colonoscopies. Comput Biol Med 2024; 170:108008. [PMID: 38277922 DOI: 10.1016/j.compbiomed.2024.108008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/21/2023] [Accepted: 01/13/2024] [Indexed: 01/28/2024]
Abstract
Polyp vascular patterns are key to categorizing colorectal cancer malignancy. These patterns are typically observed in situ from specialized narrow-band images (NBI). Nonetheless, such vascular characterization is lost from standard colonoscopies (the primary attention mechanism). Besides, even for NBI observations, the categorization remains biased for expert observations, reporting errors in classification from 59.5% to 84.2%. This work introduces an end-to-end computational strategy to enhance in situ standard colonoscopy observations, including vascular patterns typically observed from NBI mechanisms. These retrieved synthetic images are achieved by adjusting a deep representation under a non-aligned translation task from optical colonoscopy (OC) to NBI. The introduced scheme includes an architecture to discriminate enhanced neoplastic patterns achieving a remarkable separation into the embedding representation. The proposed approach was validated in a public dataset with a total of 76 sequences, including standard optical sequences and the respective NBI observations. The enhanced optical sequences were automatically classified among adenomas and hyperplastic samples achieving an F1-score of 0.86%. To measure the sensibility capability of the proposed approach, serrated samples were projected to the trained architecture. In this experiment, statistical differences from three classes with a ρ-value <0.05 were reported, following a Mann-Whitney U test. This work showed remarkable polyp discrimination results in enhancing OC sequences regarding typical NBI patterns. This method also learns polyp class distributions under the unpaired criteria (close to real practice), with the capability to separate serrated samples from adenomas and hyperplastic ones.
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Affiliation(s)
- Franklin Sierra-Jerez
- Biomedical Imaging, Vision and Learning Laboratory (BIVL(2)ab), Universidad Industrial de Santander (UIS), Colombia
| | - Fabio Martinez
- Biomedical Imaging, Vision and Learning Laboratory (BIVL(2)ab), Universidad Industrial de Santander (UIS), Colombia.
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Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
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Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
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Shi Y, Fan H, Li L, Hou Y, Qian F, Zhuang M, Miao B, Fei S. The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis. World J Surg Oncol 2024; 22:40. [PMID: 38297303 PMCID: PMC10832162 DOI: 10.1186/s12957-024-03321-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis. METHODS We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed. RESULTS Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based models were 0.91 (95% CI: 0.87-0.94), 0.85 (95% CI: 0.81-0.89), and 0.94 (95% CI: 0.39-1.00) in the training set and 0.90 (95% CI: 0.86-0.93), 0.90 (95% CI: 0.86-0.92), and 0.96 (95% CI: 0.19-1.00) in the validation set. The SEN, SPE, and SROC of EGC diagnosis by non-specialist clinicians were 0.64 (95% CI: 0.56-0.71), 0.84 (95% CI: 0.77-0.89), and 0.80 (95% CI: 0.29-0.97), and those by specialist clinicians were 0.80 (95% CI: 0.74-0.85), 0.88 (95% CI: 0.85-0.91), and 0.91 (95% CI: 0.37-0.99). With the assistance of ML models, the SEN of non-specialist physicians in the diagnosis of EGC was significantly improved (0.76 vs 0.64). CONCLUSION ML-based diagnostic models have greater performance in the identification of EGC. The diagnostic accuracy of non-specialist clinicians can be improved to the level of the specialists with the assistance of ML models. The results suggest that ML models can better assist less experienced clinicians in diagnosing EGC under endoscopy and have broad clinical application value.
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Affiliation(s)
- Yiheng Shi
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Haohan Fan
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Li Li
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Yaqi Hou
- College of Nursing, Yangzhou University, Yangzhou, 225009, China
| | - Feifei Qian
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Mengting Zhuang
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China
- First Clinical Medical College, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China
| | - Bei Miao
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Institute of Digestive Diseases, Xuzhou Medical University, 84 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
| | - Sujuan Fei
- Department of Gastroenterology, The Affiliated Hospital of Xuzhou Medical University, 99 West Huaihai Road, Jiangsu Province, 221002, Xuzhou, China.
- Key Laboratory of Gastrointestinal Endoscopy, Xuzhou Medical University, Jiangsu Province, 221002, Xuzhou, China.
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12
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Horiuchi Y, Hirasawa T, Fujisaki J. Application of artificial intelligence for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. Clin Endosc 2024; 57:11-17. [PMID: 38178327 PMCID: PMC10834286 DOI: 10.5946/ce.2023.173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 01/06/2024] Open
Abstract
Although magnifying endoscopy with narrow-band imaging is the standard diagnostic test for gastric cancer, diagnosing gastric cancer using this technology requires considerable skill. Artificial intelligence has superior image recognition, and its usefulness in endoscopic image diagnosis has been reported in many cases. The diagnostic performance (accuracy, sensitivity, and specificity) of artificial intelligence using magnifying endoscopy with narrow band still images and videos for gastric cancer was higher than that of expert endoscopists, suggesting the usefulness of artificial intelligence in diagnosing gastric cancer. Histological diagnosis of gastric cancer using artificial intelligence is also promising. However, previous studies on the use of artificial intelligence to diagnose gastric cancer were small-scale; thus, large-scale studies are necessary to examine whether a high diagnostic performance can be achieved. In addition, the diagnosis of gastric cancer using artificial intelligence has not yet become widespread in clinical practice, and further research is necessary. Therefore, in the future, artificial intelligence must be further developed as an instrument, and its diagnostic performance is expected to improve with the accumulation of numerous cases nationwide.
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Affiliation(s)
- Yusuke Horiuchi
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of Japanese Foundation for Cancer Research, Tokyo, Japan
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13
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Xin Y, Zhang Q, Liu X, Li B, Mao T, Li X. Application of artificial intelligence in endoscopic gastrointestinal tumors. Front Oncol 2023; 13:1239788. [PMID: 38144533 PMCID: PMC10747923 DOI: 10.3389/fonc.2023.1239788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/17/2023] [Indexed: 12/26/2023] Open
Abstract
With an increasing number of patients with gastrointestinal cancer, effective and accurate early diagnostic clinical tools are required provide better health care for patients with gastrointestinal cancer. Recent studies have shown that artificial intelligence (AI) plays an important role in the diagnosis and treatment of patients with gastrointestinal tumors, which not only improves the efficiency of early tumor screening, but also significantly improves the survival rate of patients after treatment. With the aid of efficient learning and judgment abilities of AI, endoscopists can improve the accuracy of diagnosis and treatment through endoscopy and avoid incorrect descriptions or judgments of gastrointestinal lesions. The present article provides an overview of the application status of various artificial intelligence in gastric and colorectal cancers in recent years, and the direction of future research and clinical practice is clarified from a clinical perspective to provide a comprehensive theoretical basis for AI as a promising diagnostic and therapeutic tool for gastrointestinal cancer.
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Affiliation(s)
| | | | | | | | | | - Xiaoyu Li
- Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, China
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14
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Klang E, Sourosh A, Nadkarni GN, Sharif K, Lahat A. Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy. Diagnostics (Basel) 2023; 13:3613. [PMID: 38132197 PMCID: PMC10742887 DOI: 10.3390/diagnostics13243613] [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: 10/14/2023] [Revised: 11/23/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Gastric cancer (GC), a significant health burden worldwide, is typically diagnosed in the advanced stages due to its non-specific symptoms and complex morphological features. Deep learning (DL) has shown potential for improving and standardizing early GC detection. This systematic review aims to evaluate the current status of DL in pre-malignant, early-stage, and gastric neoplasia analysis. METHODS A comprehensive literature search was conducted in PubMed/MEDLINE for original studies implementing DL algorithms for gastric neoplasia detection using endoscopic images. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The focus was on studies providing quantitative diagnostic performance measures and those comparing AI performance with human endoscopists. RESULTS Our review encompasses 42 studies that utilize a variety of DL techniques. The findings demonstrate the utility of DL in GC classification, detection, tumor invasion depth assessment, cancer margin delineation, lesion segmentation, and detection of early-stage and pre-malignant lesions. Notably, DL models frequently matched or outperformed human endoscopists in diagnostic accuracy. However, heterogeneity in DL algorithms, imaging techniques, and study designs precluded a definitive conclusion about the best algorithmic approach. CONCLUSIONS The promise of artificial intelligence in improving and standardizing gastric neoplasia detection, diagnosis, and segmentation is significant. This review is limited by predominantly single-center studies and undisclosed datasets used in AI training, impacting generalizability and demographic representation. Further, retrospective algorithm training may not reflect actual clinical performance, and a lack of model details hinders replication efforts. More research is needed to substantiate these findings, including larger-scale multi-center studies, prospective clinical trials, and comprehensive technical reporting of DL algorithms and datasets, particularly regarding the heterogeneity in DL algorithms and study designs.
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Affiliation(s)
- Eyal Klang
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- ARC Innovation Center, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel
| | - Ali Sourosh
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Girish N. Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA (A.S.); (G.N.N.)
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kassem Sharif
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Center, Affiliated with Tel Aviv University Medical School, Tel Hashomer, Ramat Gan 52621, Tel Aviv, Israel;
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15
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Wang L, Yang Y, Yang A, Li T. Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images. BIOMEDICAL OPTICS EXPRESS 2023; 14:4677-4695. [PMID: 37791283 PMCID: PMC10545198 DOI: 10.1364/boe.487456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/11/2023] [Accepted: 06/29/2023] [Indexed: 10/05/2023]
Abstract
Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.
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Affiliation(s)
- Lingxiao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
| | - Yingyun Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Aiming Yang
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Ting Li
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300192, China
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16
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Cheema HI, Tharian B, Inamdar S, Garcia-Saenz-de-Sicilia M, Cengiz C. Recent advances in endoscopic management of gastric neoplasms. World J Gastrointest Endosc 2023; 15:319-337. [PMID: 37274561 PMCID: PMC10236974 DOI: 10.4253/wjge.v15.i5.319] [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: 09/20/2022] [Revised: 01/12/2023] [Accepted: 04/06/2023] [Indexed: 05/16/2023] Open
Abstract
The development and clinical application of new diagnostic endoscopic technologies such as endoscopic ultrasonography with biopsy, magnification endoscopy, and narrow-band imaging, more recently supplemented by artificial intelligence, have enabled wider recognition and detection of various gastric neoplasms including early gastric cancer (EGC) and subepithelial tumors, such as gastrointestinal stromal tumors and neuroendocrine tumors. Over the last decade, the evolution of novel advanced therapeutic endoscopic techniques, such as endoscopic mucosal resection, endoscopic submucosal dissection, endoscopic full-thickness resection, and submucosal tunneling endoscopic resection, along with the advent of a broad array of endoscopic accessories, has provided a promising and yet less invasive strategy for treating gastric neoplasms with the advantage of a reduced need for gastric surgery. Thus, the management algorithms of various gastric tumors in a defined subset of the patient population at low risk of lymph node metastasis and amenable to endoscopic resection, may require revision considering upcoming data given the high success rate of en bloc resection by experienced endoscopists. Moreover, endoscopic surveillance protocols for precancerous gastric lesions will continue to be refined by systematic reviews and meta-analyses of further research. However, the lack of familiarity with subtle endoscopic changes associated with EGC, as well as longer procedural time, evolving resection techniques and tools, a steep learning curve of such high-risk procedures, and lack of coding are issues that do not appeal to many gastroenterologists in the field. This review summarizes recent advances in the endoscopic management of gastric neoplasms, with special emphasis on diagnostic and therapeutic methods and their future prospects.
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Affiliation(s)
- Hira Imad Cheema
- Department of Internal Medicine, Baptist Health Medical Center, Little Rock, AR 72205, United States
| | - Benjamin Tharian
- Department of Interventional Endoscopy/Gastroenterology, Bayfront Health, Digestive Health Institute, St. Petersberg, FL 33701, United States
| | - Sumant Inamdar
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Mauricio Garcia-Saenz-de-Sicilia
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Cem Cengiz
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, John L. McClellan Memorial Veterans Hospital, Little Rock, AR 72205, United States
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, TOBB University of Economics and Technology, Ankara 06510, Turkey
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17
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Yin M, Liu L, Gao J, Lin J, Qu S, Xu W, Liu X, Xu C, Zhu J. Deep learning for pancreatic diseases based on endoscopic ultrasound: A systematic review. Int J Med Inform 2023; 174:105044. [PMID: 36948061 DOI: 10.1016/j.ijmedinf.2023.105044] [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: 09/30/2022] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND AND AIMS Endoscopic ultrasonography (EUS) is one of the main examinations in pancreatic diseases. A series of the studies reported the application of deep learning (DL)-assisted EUS in the diagnosis of pancreatic diseases. This systematic review is to evaluate the role of DL algorithms in assisting EUS diagnosis of pancreatic diseases. METHODS Literature search were conducted in PubMed and Semantic Scholar databases. Studies that developed DL models for pancreatic diseases based on EUS were eligible for inclusion. This review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and quality assessment of the included studies was performed according to the IJMEDI checklist. RESULTS A total of 23 studies were enrolled into this systematic review, which could be categorized into three groups according to computer vision tasks: classification, detection and segmentation. Seventeen studies focused on the classification task, among which five studies developed simple neural network (NN) models while twelve studies constructed convolutional NN (CNN) models. Three studies were concerned the detection task and five studies were the segmentation task, all based on CNN architectures. All models presented in the studies performed well based on EUS images, videos or voice. According to the IJMEDI checklist, six studies were recognized as high-grade quality, with scores beyond 35 points. CONCLUSIONS DL algorithms show great potential in EUS images/videos/voice for pancreatic diseases. However, there is room for improvement such as sample sizes, multi-center cooperation, data preprocessing, model interpretability, and code sharing.
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Affiliation(s)
- Minyue Yin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Lu Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jingwen Gao
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Jiaxi Lin
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Shuting Qu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Wei Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Xiaolin Liu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China
| | - Chunfang Xu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
| | - Jinzhou Zhu
- Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou 215000, China; Suzhou Clinical Center of Digestive Diseases, Suzhou 215000, China.
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18
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Yacob YM, Alquran H, Mustafa WA, Alsalatie M, Sakim HAM, Lola MS. H. pylori Related Atrophic Gastritis Detection Using Enhanced Convolution Neural Network (CNN) Learner. Diagnostics (Basel) 2023; 13:diagnostics13030336. [PMID: 36766441 PMCID: PMC9914156 DOI: 10.3390/diagnostics13030336] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/09/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
Atrophic gastritis (AG) is commonly caused by the infection of the Helicobacter pylori (H. pylori) bacteria. If untreated, AG may develop into a chronic condition leading to gastric cancer, which is deemed to be the third primary cause of cancer-related deaths worldwide. Precursory detection of AG is crucial to avoid such cases. This work focuses on H. pylori-associated infection located at the gastric antrum, where the classification is of binary classes of normal versus atrophic gastritis. Existing work developed the Deep Convolution Neural Network (DCNN) of GoogLeNet with 22 layers of the pre-trained model. Another study employed GoogLeNet based on the Inception Module, fast and robust fuzzy C-means (FRFCM), and simple linear iterative clustering (SLIC) superpixel algorithms to identify gastric disease. GoogLeNet with Caffe framework and ResNet-50 are machine learners that detect H. pylori infection. Nonetheless, the accuracy may become abundant as the network depth increases. An upgrade to the current standards method is highly anticipated to avoid untreated and inaccurate diagnoses that may lead to chronic AG. The proposed work incorporates improved techniques revolving within DCNN with pooling as pre-trained models and channel shuffle to assist streams of information across feature channels to ease the training of networks for deeper CNN. In addition, Canonical Correlation Analysis (CCA) feature fusion method and ReliefF feature selection approaches are intended to revamp the combined techniques. CCA models the relationship between the two data sets of significant features generated by pre-trained ShuffleNet. ReliefF reduces and selects essential features from CCA and is classified using the Generalized Additive Model (GAM). It is believed the extended work is justified with a 98.2% testing accuracy reading, thus providing an accurate diagnosis of normal versus atrophic gastritis.
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Affiliation(s)
- Yasmin Mohd Yacob
- Faculty of Electronic Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
- Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Wan Azani Mustafa
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Correspondence:
| | - Mohammed Alsalatie
- King Hussein Medical Center, Royal Jordanian Medical Service, The Institute of Biomedical Technology, Amman 11855, Jordan
| | - Harsa Amylia Mat Sakim
- School of Electrical and Electronic Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 11800, Penang, Malaysia
| | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Terengganu 21030, Terengganu, Malaysia
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19
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Panarese A, Saito Y, Zagari RM. Kyoto classification of gastritis, virtual chromoendoscopy and artificial intelligence: Where are we going? What do we need? Artif Intell Gastrointest Endosc 2023; 4:1-11. [DOI: 10.37126/aige.v4.i1.1] [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: 09/14/2022] [Revised: 10/18/2022] [Accepted: 01/04/2023] [Indexed: 01/06/2023] Open
Abstract
Chronic gastritis (CG) is a widespread and frequent disease, mainly caused by Helicobacter pylori infection, which is associated with an increased risk of gastric cancer. Virtual chromoendoscopy improves the endoscopic diagnostic efficacy, which is essential to establish the most appropriate therapy and to enable cancer prevention. Artificial intelligence provides algorithms for the diagnosis of gastritis and, in particular, early gastric cancer, but it is not yet used in practice. Thus, technological innovation, through image resolution and processing, optimizes the diagnosis and management of CG and gastric cancer. The endoscopic Kyoto classification of gastritis improves the diagnosis and management of this disease, but through the analysis of the most recent literature, new algorithms can be proposed.
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Affiliation(s)
- Alba Panarese
- Division of Gastroenterology and Digestive Endoscopy, Department of Medical Sciences, Central Hospital - Azienda Ospedaliera, Taranto 74123, Italy
| | - Yutaka Saito
- Division of Endoscopy, National Cancer Center Hospital, Tokyo 104-0045, Japan
| | - Rocco Maurizio Zagari
- Gastroenterology Unit and Department of Surgical and Medical Sciences, IRCCS Azienda Ospedaliero-Universitaria and University of Bologna, Bologna 40121, Italy
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20
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Zhang G, Song J, Feng Z, Zhao W, Huang P, Liu L, Zhang Y, Su X, Wu Y, Cao Y, Li Z, Jie Z. Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace. Front Oncol 2023; 12:1075974. [PMID: 36686778 PMCID: PMC9846739 DOI: 10.3389/fonc.2022.1075974] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/08/2022] [Indexed: 01/06/2023] Open
Abstract
Objective This study aimed to analyze and visualize the current research focus, research frontiers, evolutionary processes, and trends of artificial intelligence (AI) in the field of gastric cancer using a bibliometric analysis. Methods The Web of Science Core Collection database was selected as the data source for this study to retrieve and obtain articles and reviews related to AI in gastric cancer. All the information extracted from the articles was imported to CiteSpace to conduct the bibliometric and knowledge map analysis, allowing us to clearly visualize the research hotspots and trends in this field. Results A total of 183 articles published between 2017 and 2022 were included, contributed by 201 authors from 33 countries/regions. Among them, China (47.54%), Japan (21.86%), and the USA (13.11%) have made outstanding contributions in this field, accounting fsor 82.51% of the total publications. The primary research institutions were Wuhan University, Tokyo University, and Tada Tomohiro Inst Gastroenterol and Proctol. Tada (n = 12) and Hirasawa (n = 90) were ranked first in the top 10 authors and co-cited authors, respectively. Gastrointestinal Endoscopy (21 publications; IF 2022, 9.189; Q1) was the most published journal, while Gastric Cancer (133 citations; IF 2022, 8.171; Q1) was the most co-cited journal. Nevertheless, the cooperation between different countries and institutions should be further strengthened. The most common keywords were AI, gastric cancer, and convolutional neural network. The "deep-learning algorithm" started to burst in 2020 and continues till now, which indicated that this research topic has attracted continuous attention in recent years and would be the trend of research on AI application in GC. Conclusions Research related to AI in gastric cancer is increasing exponentially. Current research hotspots focus on the application of AI in gastric cancer, represented by convolutional neural networks and deep learning, in diagnosis and differential diagnosis and staging. Considering the great potential and clinical application prospects, the related area of AI applications in gastric cancer will remain a research hotspot in the future.
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Affiliation(s)
- Guoyang Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jingjing Song
- Jiangxi Med College of Nanchang University, Nanchang, China
| | - Zongfeng Feng
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,Medical Innovation Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wentao Zhao
- The Third Clinical Department of China Medical University, Shenyang, China
| | - Pan Huang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Li Liu
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yang Zhang
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xufeng Su
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yukang Wu
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yi Cao
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhengrong Li
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Zhigang Jie, ; Zhengrong Li,
| | - Zhigang Jie
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China,*Correspondence: Zhigang Jie, ; Zhengrong Li,
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21
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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Ochiai K, Ozawa T, Shibata J, Ishihara S, Tada T. Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy. Diagnostics (Basel) 2022; 12:diagnostics12123153. [PMID: 36553160 PMCID: PMC9777622 DOI: 10.3390/diagnostics12123153] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 12/07/2022] [Accepted: 12/10/2022] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist's skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.
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Affiliation(s)
- Kentaro Ochiai
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tsuyoshi Ozawa
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Junichi Shibata
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
| | - Soichiro Ishihara
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Tomohiro Tada
- Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
- Tomohiro Tada the Institute of Gastroenterology and Proctology, Musashi-Urawa, Saitama 336-0021, Japan
- AI Medical Service Inc. Toshima-ku, Tokyo 104-0061, Japan
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23
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Luo Q, Yang H, Hu B. Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection. J Dig Dis 2022; 23:666-674. [PMID: 36661411 DOI: 10.1111/1751-2980.13154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 01/04/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.
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Affiliation(s)
- Qi Luo
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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24
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Gong L, Wang M, Shu L, He J, Qin B, Xu J, Su W, Dong D, Hu H, Tian J, Zhou P. Automatic captioning of early gastric cancer using magnification endoscopy with narrow-band imaging. Gastrointest Endosc 2022; 96:929-942.e6. [PMID: 35917877 DOI: 10.1016/j.gie.2022.07.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/03/2022] [Accepted: 07/13/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS The detection rate for early gastric cancer (EGC) is unsatisfactory, and mastering the diagnostic skills of magnifying endoscopy with narrow-band imaging (ME-NBI) requires rich expertise and experience. We aimed to develop an EGC captioning model (EGCCap) to automatically describe the visual characteristics of ME-NBI images for endoscopists. METHODS ME-NBI images (n = 1886) from 294 cases were enrolled from multiple centers, and corresponding 5658 text data were designed following the simple EGC diagnostic algorithm. An EGCCap was developed using the multiscale meshed-memory transformer. We conducted comprehensive evaluations for EGCCap including the quantitative and quality of performance, generalization, robustness, interpretability, and assistant value analyses. The commonly used metrics were BLEUs, CIDEr, METEOR, ROUGE, SPICE, accuracy, sensitivity, and specificity. Two-sided statistical tests were conducted, and statistical significance was determined when P < .05. RESULTS EGCCap acquired satisfying captioning performance by outputting correctly and coherently clinically meaningful sentences in the internal test cohort (BLEU1 = 52.434, CIDEr = 36.734, METEOR = 27.823, ROUGE = 49.949, SPICE = 35.548) and maintained over 80% performance when applied to other centers or corrupted data. The diagnostic ability of endoscopists improved with the assistance of EGCCap, which was especially significant (P < .05) for junior endoscopists. Endoscopists gave EGCCap an average remarkable score of 7.182, showing acceptance of EGCCap. CONCLUSIONS EGCCap exhibited promising captioning performance and was proven with satisfying generalization, robustness, and interpretability. Our study showed potential value in aiding and improving the diagnosis of EGC and facilitating the development of automated reporting in the future.
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Affiliation(s)
- Lixin Gong
- College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Min Wang
- Department of Gastroenterology, Hepatology and Nutrition, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Lei Shu
- Department of Gastroenterology, No. 1 Hospital of Wuhan, Wuhan, China
| | - Jie He
- Endoscopy Center, Zhongshan Hospital (Xiamen Branch), Fudan University, Xiamen, China; Department of Gastroenterology, The Affiliated Dongnan Hospital of Xiamen University, Zhangzhou, China
| | - Bin Qin
- Department of Gastroenterology, the Second Affiliated Hospital of Xi'an Jiaotong University, Zhangzhou, China
| | - Jiacheng Xu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Wei Su
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
| | - Di Dong
- CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hao Hu
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China; Department of Gastroenterology, Shigatse People's Hospital, Shigatse, China
| | - Jie Tian
- College of Medicine and Biological Information Engineering School, Northeastern University, Shenyang, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China
| | - Pinghong Zhou
- Endoscopy Center and Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai, China
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25
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Pan J, Lei LM. Value of serum pepsinogen, gastrin, and cadherin-17 detection combined with narrowband imaging magnifying endoscopy in distinguishing early gastric cancer and precancerous lesions. Shijie Huaren Xiaohua Zazhi 2022; 30:964-970. [DOI: 10.11569/wcjd.v30.i21.964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The early screening of cancer has always been the focus of cancer research. As a common cancer in the world, gastric cancer has had a high incidence rate and mortality for many years, and canceration is difficult to detect. Current detection methods are not ideal for early detection of gastric cancer and precancerous lesions.
AIM To assess the value of detection of serum pepsinogen (PG), gastrin (G-17), and hepato-intestinal cadherin-17 (CDH-17) combined with narrowband imaging magnifying endoscopy in the identification of early gastric cancer and precancerous lesions.
METHODS The clinical data of patients with early gastric cancer (106 cases) and precancerous lesions (134 cases) at our hospital from January 2018 to January 2021 were retrospectively analyzed. All patients underwent narrow-band imaging magnifying endoscopy and detection of serum PG (including PGⅠ and PGⅡ), G-17, and CDH-17 levels. The sensitivity, specificity, and accuracy of PG, G-17, CDH-17, and narrow-band imaging magnifying endoscopy, alone and in combination, in the diagnosis of early gastric cancer and precancerous lesions were calculated.
RESULTS In the early gastric cancer group, the incidence of lesions with border limits, mucosal microvascular irregularities, irregular surface ducts, and increased glandular spacing as detected by narrow-band imaging magnifying endoscopy was higher than that in the precancerous lesion group (P < 0.05). The diagnostic coincidence rates of magnifying endoscopy for early gastric cancer and precancerous lesions were 83.02% and 85.07%, respectively, and the difference was not statistically significant (P > 0.05). Serum PGⅠ level in the early gastric cancer group was lower than that of the precancerous lesion group, but there was no significant difference in serum PGⅡ between the two groups (P > 0.05); serum G-17 and CDH-17 levels were higher than those of the precancerous lesion group (P < 0.05). There was no statistically significant difference in the diagnostic coincidence rates of serum PGI, G-17, and CDH-17 alone (P > 0.05). The sensitivity, specificity and accuracy of serum PGⅠ, G-17, and CDH-17 combined with narrow-band imaging magnifying endoscopy in the diagnosis of early gastric cancer and precancerous lesions were higher than those of any serum index alone (PGⅠ/G-17/CDH-17) or narrowband imaging magnifying endoscopy alone (P < 0.05).
CONCLUSION The detection of serum PG, G-17, and CDH-17 combined with narrow-band imaging magnifying endoscopy has high sensitivity, specificity, and accuracy in the differential diagnosis of early gastric cancer and precancerous lesions.
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Affiliation(s)
- Jie Pan
- Zhejiang Chinese Medicine University, Hangzhou 310053, Zhejiang Province, China,General Medicine Department of Lishui People's Hospital, Lishui 323000, Zhejiang Province, China
| | - Li-Mei Lei
- General Medicine Department of Lishui People's Hospital, Lishui 323000, Zhejiang Province, China
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26
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Yang H, Wu Y, Yang B, Wu M, Zhou J, Liu Q, Lin Y, Li S, Li X, Zhang J, Wang R, Xie Q, Li J, Luo Y, Tu M, Wang X, Lan H, Bai X, Wu H, Zeng F, Zhao H, Yi Z, Zeng F. Identification of upper GI diseases during screening gastroscopy using a deep convolutional neural network algorithm. Gastrointest Endosc 2022; 96:787-795.e6. [PMID: 35718070 DOI: 10.1016/j.gie.2022.06.011] [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] [Received: 01/30/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIMS The clinical application of GI endoscopy for the diagnosis of multiple diseases using artificial intelligence (AI) has been limited by its high false-positive rates. There is an unmet need to develop a GI endoscopy AI-assisted diagnosis system (GEADS) to improve diagnostic accuracy and clinical utility. METHODS In this retrospective, multicenter study, a convolutional neural network was trained to assess upper GI diseases based on 26,228 endoscopic images from Dazhou Central Hospital that were randomly assigned (3:1:1) to a training dataset, validation dataset, and test dataset, respectively. To validate the model, 6 external independent datasets comprising 51,372 images of upper GI diseases were collected. In addition, 1 prospective dataset comprising 27,975 images was collected. The performance of GEADS was compared with endoscopists with 2 professional degrees of expertise: expert and novice. Eight endoscopists were in the expert group with >5 years of experience, whereas 3 endoscopists were in the novice group with 1 to 5 years of experience. RESULTS The GEADS model achieved an accuracy of .918 (95% confidence interval [CI], .914-.922), with an F1 score of .884 (95% CI, .879-.889), recall of .873 (95% CI, .868-.878), and precision of .890 (95% CI, .885-.895) in the internal validation dataset. In the external validation datasets and 1 prospective validation dataset, the diagnostic accuracy of the GEADS ranged from .841 (95% CI, .834-.848) to .949 (95% CI, .935-.963). With the help of the GEADS, the diagnosing accuracies of novice and expert endoscopists were significantly improved (P < .001). CONCLUSIONS The AI system can assist endoscopists in improving the accuracy of diagnosing upper GI diseases.
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Affiliation(s)
- Hang Yang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Yu Wu
- Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Bo Yang
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Min Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jun Zhou
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qin Liu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Yifei Lin
- Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Shilin Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Xue Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jie Zhang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Rui Wang
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Qianrong Xie
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Jingqi Li
- College of Aulin, Northeast Forestry University, Harbin, Heilongjiang, China
| | - Yue Luo
- College of Basic Medical Sciences, North Sichuan Medical College, Nanchong, Sichuan, China
| | - Mengjie Tu
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China; Department of Surgery, Shantou University Medical College, Shantou, Guangdong, China
| | - Xiao Wang
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Haitao Lan
- Department of Sichuan, Academy of Medical Sciences, Sichuan Provincial People's Hospital, Chengdu, Sichuan, China
| | - Xuesong Bai
- Digestive Endoscopy Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Huaping Wu
- Department of Cardiac &Vascular Surgery, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Fanwei Zeng
- Department of Spinal Surgery, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China
| | - Hong Zhao
- Department of Hepatobiliary Surgery, State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Zhang Yi
- Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China; Center of Intelligent Medicine, Computer Science, Sichuan University, Chengdu, Sichuan, China
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27
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Jin J, Zhang Q, Dong B, Ma T, Mei X, Wang X, Song S, Peng J, Wu A, Dong L, Kong D. Automatic detection of early gastric cancer in endoscopy based on Mask region-based convolutional neural networks (Mask R-CNN)(with video). Front Oncol 2022; 12:927868. [PMID: 36338757 PMCID: PMC9630732 DOI: 10.3389/fonc.2022.927868] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 09/05/2022] [Indexed: 12/04/2022] Open
Abstract
The artificial intelligence (AI)-assisted endoscopic detection of early gastric cancer (EGC) has been preliminarily developed. The currently used algorithms still exhibit limitations of large calculation and low-precision expression. The present study aimed to develop an endoscopic automatic detection system in EGC based on a mask region-based convolutional neural network (Mask R-CNN) and to evaluate the performance in controlled trials. For this purpose, a total of 4,471 white light images (WLIs) and 2,662 narrow band images (NBIs) of EGC were obtained for training and testing. In total, 10 of the WLIs (videos) were obtained prospectively to examine the performance of the RCNN system. Furthermore, 400 WLIs were randomly selected for comparison between the Mask R-CNN system and doctors. The evaluation criteria included accuracy, sensitivity, specificity, positive predictive value and negative predictive value. The results revealed that there were no significant differences between the pathological diagnosis with the Mask R-CNN system in the WLI test (χ2 = 0.189, P=0.664; accuracy, 90.25%; sensitivity, 91.06%; specificity, 89.01%) and in the NBI test (χ2 = 0.063, P=0.802; accuracy, 95.12%; sensitivity, 97.59%). Among 10 WLI real-time videos, the speed of the test videos was up to 35 frames/sec, with an accuracy of 90.27%. In a controlled experiment of 400 WLIs, the sensitivity of the Mask R-CNN system was significantly higher than that of experts (χ2 = 7.059, P=0.000; 93.00% VS 80.20%), and the specificity was higher than that of the juniors (χ2 = 9.955, P=0.000, 82.67% VS 71.87%), and the overall accuracy rate was higher than that of the seniors (χ2 = 7.009, P=0.000, 85.25% VS 78.00%). On the whole, the present study demonstrates that the Mask R-CNN system exhibited an excellent performance status for the detection of EGC, particularly for the real-time analysis of WLIs. It may thus be effectively applied to clinical settings.
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Affiliation(s)
- Jing Jin
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qianqian Zhang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bill Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Tao Ma
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Xuecan Mei
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xi Wang
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shaofang Song
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Jie Peng
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Aijiu Wu
- Research and Development Department, Hefei Zhongna Medical Instrument Co. LTD, Hefei, China
| | - Lanfang Dong
- School of Computer Science and Technology, University of Science and Technology of China, Hefei, China
| | - Derun Kong
- Key Laboratory of Digestive Diseases of Anhui Province, Department of Gastroenterology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- *Correspondence: Derun Kong,
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28
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Ishioka M, Osawa H, Hirasawa T, Kawachi H, Nakano K, Fukushima N, Sakaguchi M, Tada T, Kato Y, Shibata J, Ozawa T, Tajiri H, Fujisaki J. Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: Retrospective study. Dig Endosc 2022; 35:483-491. [PMID: 36239483 DOI: 10.1111/den.14455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/12/2022] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Endoscopists' abilities to diagnose early gastric cancers (EGCs) vary, especially between specialists and nonspecialists. We developed an artificial intelligence (AI)-based diagnostic support tool "Tango" to differentiate EGCs and compared its performance with that of endoscopists. METHODS The diagnostic performances of Tango and endoscopists (34 specialists, 42 nonspecialists) were compared using still images of 150 neoplastic and 165 non-neoplastic lesions. Neoplastic lesions included EGCs and adenomas. The primary outcome was to show the noninferiority of Tango (based on sensitivity) over specialists. The secondary outcomes were the noninferiority of Tango (based on accuracy) over specialists and the superiority of Tango (based on sensitivity and accuracy) over nonspecialists. The lower limit of the 95% confidence interval (CI) of the difference between Tango and the specialists for sensitivity was calculated, with >-10% defined as noninferiority and >0% defined as superiority in the primary outcome. The comparable differences between Tango and the endoscopists for each performance were calculated, with >10% defined as superiority and >0% defined as noninferiority in the secondary outcomes. RESULTS Tango achieved superiority over the specialists based on sensitivity (84.7% vs. 65.8%, difference 18.9%, 95% CI 12.3-25.3%) and demonstrated noninferiority based on accuracy (70.8% vs. 67.4%). Tango achieved superiority over the nonspecialists based on sensitivity (84.7% vs. 51.0%) and accuracy (70.8% vs. 58.4%). CONCLUSIONS The AI-based diagnostic support tool for EGCs demonstrated a robust performance and may be useful to reduce misdiagnosis.
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Affiliation(s)
- Mitsuaki Ishioka
- Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroyuki Osawa
- Division of Gastroenterology, Department of Medicine, Jichi Medical University, Tochigi, Japan
| | - Toshiaki Hirasawa
- Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Hiroshi Kawachi
- Department of Pathology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | - Kaoru Nakano
- Department of Pathology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
| | | | - Mio Sakaguchi
- Department of Pathology, Jichi Medical University, Tochigi, Japan
| | - Tomohiro Tada
- AI Medical Service Inc., Tokyo, Japan.,Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | | | - Junichi Shibata
- AI Medical Service Inc., Tokyo, Japan.,Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan
| | - Tsuyoshi Ozawa
- AI Medical Service Inc., Tokyo, Japan.,Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hisao Tajiri
- The Jikei University School of Medicine, Tokyo, Japan
| | - Junko Fujisaki
- Department of Gastroenterology, Cancer Institute Hospital of the Japanese Foundation for Cancer Research, Tokyo, Japan
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29
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Ma M, Li Z, Yu T, Liu G, Ji R, Li G, Guo Z, Wang L, Qi Q, Yang X, Qu J, Wang X, Zuo X, Ren H, Li Y. Application of deep learning in the real-time diagnosis of gastric lesion based on magnifying optical enhancement videos. Front Oncol 2022; 12:945904. [PMID: 35992850 PMCID: PMC9389533 DOI: 10.3389/fonc.2022.945904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022] Open
Abstract
Background and aim Magnifying image-enhanced endoscopy was demonstrated to have higher diagnostic accuracy than white-light endoscopy. However, differentiating early gastric cancers (EGCs) from benign lesions is difficult for beginners. We aimed to determine whether the computer-aided model for the diagnosis of gastric lesions can be applied to videos rather than still images. Methods A total of 719 magnifying optical enhancement images of EGCs, 1,490 optical enhancement images of the benign gastric lesions, and 1,514 images of background mucosa were retrospectively collected to train and develop a computer-aided diagnostic model. Subsequently, 101 video segments and 671 independent images were used for validation, and error frames were labeled to retrain the model. Finally, a total of 117 unaltered full-length videos were utilized to test the model and compared with those diagnostic results made by independent endoscopists. Results Except for atrophy combined with intestinal metaplasia (IM) and low-grade neoplasia, the diagnostic accuracy was 0.90 (85/94). The sensitivity, specificity, PLR, NLR, and overall accuracy of the model to distinguish EGC from non-cancerous lesions were 0.91 (48/53), 0.78 (50/64), 4.14, 0.12, and 0.84 (98/117), respectively. No significant difference was observed in the overall diagnostic accuracy between the computer-aided model and experts. A good level of kappa values was found between the model and experts, which meant that the kappa value was 0.63. Conclusions The performance of the computer-aided model for the diagnosis of EGC is comparable to that of experts. Magnifying the optical enhancement model alone may not be able to deal with all lesions in the stomach, especially when near the focus on severe atrophy with IM. These results warrant further validation in prospective studies with more patients. A ClinicalTrials.gov registration was obtained (identifier number: NCT04563416). Clinical Trial Registration ClinicalTrials.gov, identifier NCT04563416.
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Affiliation(s)
- Mingjun Ma
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhen Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Tao Yu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guanqun Liu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Rui Ji
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Guangchao Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Zhuang Guo
- Department of Gastroenterology, Shengli Oilfield Central Hospital, Dongying, China
| | - Limei Wang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Qingqing Qi
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Xiaoxiao Yang
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Junyan Qu
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Xiao Wang
- Department of Pathology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xiuli Zuo
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
| | - Hongliang Ren
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
| | - Yanqing Li
- Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Laboratory of Translational Gastroenterology, Qilu Hospital of Shandong University, Jinan, China
- Robot Engineering Laboratory for Precise Diagnosis and Therapy of Gastrointestinal Tumor, Qilu Hospital of Shandong University, Jinan, China
- *Correspondence: Yanqing Li,
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Sugano K, Spechler SJ, El-Omar EM, McColl KEL, Takubo K, Gotoda T, Fujishiro M, Iijima K, Inoue H, Kawai T, Kinoshita Y, Miwa H, Mukaisho KI, Murakami K, Seto Y, Tajiri H, Bhatia S, Choi MG, Fitzgerald RC, Fock KM, Goh KL, Ho KY, Mahachai V, O'Donovan M, Odze R, Peek R, Rugge M, Sharma P, Sollano JD, Vieth M, Wu J, Wu MS, Zou D, Kaminishi M, Malfertheiner P. Kyoto international consensus report on anatomy, pathophysiology and clinical significance of the gastro-oesophageal junction. Gut 2022; 71:1488-1514. [PMID: 35725291 PMCID: PMC9279854 DOI: 10.1136/gutjnl-2022-327281] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/03/2022] [Indexed: 02/07/2023]
Abstract
OBJECTIVE An international meeting was organised to develop consensus on (1) the landmarks to define the gastro-oesophageal junction (GOJ), (2) the occurrence and pathophysiological significance of the cardiac gland, (3) the definition of the gastro-oesophageal junctional zone (GOJZ) and (4) the causes of inflammation, metaplasia and neoplasia occurring in the GOJZ. DESIGN Clinical questions relevant to the afore-mentioned major issues were drafted for which expert panels formulated relevant statements and textural explanations.A Delphi method using an anonymous system was employed to develop the consensus, the level of which was predefined as ≥80% of agreement. Two rounds of voting and amendments were completed before the meeting at which clinical questions and consensus were finalised. RESULTS Twenty eight clinical questions and statements were finalised after extensive amendments. Critical consensus was achieved: (1) definition for the GOJ, (2) definition of the GOJZ spanning 1 cm proximal and distal to the GOJ as defined by the end of palisade vessels was accepted based on the anatomical distribution of cardiac type gland, (3) chemical and bacterial (Helicobacter pylori) factors as the primary causes of inflammation, metaplasia and neoplasia occurring in the GOJZ, (4) a new definition of Barrett's oesophagus (BO). CONCLUSIONS This international consensus on the new definitions of BO, GOJ and the GOJZ will be instrumental in future studies aiming to resolve many issues on this important anatomic area and hopefully will lead to better classification and management of the diseases surrounding the GOJ.
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Affiliation(s)
- Kentaro Sugano
- Division of Gastroenterology, Department of Medicine, Jichi Medical University, Shimotsuke, Japan
| | - Stuart Jon Spechler
- Division of Gastroenterology, Center for Esophageal Diseases, Baylor University Medical Center, Dallas, Texas, USA
| | - Emad M El-Omar
- Microbiome Research Centre, St George & Sutherland Clinical Campuses, School of Clinical Medicine, Faculty of Medicine & Health, Sydney, New South Wales, Australia
| | - Kenneth E L McColl
- Division of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Kaiyo Takubo
- Research Team for Geriatric Pathology, Tokyo Metropolitan Institute of Gerontology, Tokyo, Japan
| | - Takuji Gotoda
- Division of Gastroenterology and Hepatology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan
| | - Mitsuhiro Fujishiro
- Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Katsunori Iijima
- Department of Gastroenterology, Akita University Graduate School of Medicine, Akita, Japan
| | - Haruhiro Inoue
- Digestive Disease Center, Showa University Koto Toyosu Hospital, Tokyo, Japan
| | - Takashi Kawai
- Department of Gastroenterological Endoscopy, Tokyo Medical University, Tokyo, Japan
| | | | - Hiroto Miwa
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Hyogo College of Medicine, Kobe, Japan
| | - Ken-Ichi Mukaisho
- Education Center for Medicine and Nursing, Shiga University of Medical Science, Otsu, Japan
| | - Kazunari Murakami
- Department of Gastroenterology, Oita University Faculty of Medicine, Yuhu, Japan
| | - Yasuyuki Seto
- Department of Gastrointestinal Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hisao Tajiri
- Jikei University School of Medicine, Minato-ku, Tokyo, Japan
| | | | - Myung-Gyu Choi
- Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, The Republic of Korea
| | - Rebecca C Fitzgerald
- Medical Research Council Cancer Unit, Hutchison/Medical Research Council Research Centre, University of Cambridge, Cambridge, UK
| | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Duke NUS School of Medicine, National University of Singapore, Singapore
| | | | - Khek Yu Ho
- Department of Medicine, National University of Singapore, Singapore
| | - Varocha Mahachai
- Center of Excellence in Digestive Diseases, Thammasat University and Science Resarch and Innovation, Bangkok, Thailand
| | - Maria O'Donovan
- Department of Histopathology, Cambridge University Hospital NHS Trust UK, Cambridge, UK
| | - Robert Odze
- Department of Pathology, Tuft University School of Medicine, Boston, Massachusetts, USA
| | - Richard Peek
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Massimo Rugge
- Department of Medicine DIMED, Surgical Pathology and Cytopathology Unit, University of Padova, Padova, Italy
| | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas School of Medicine, Kansas City, Kansas, USA
| | - Jose D Sollano
- Department of Medicine, University of Santo Tomas, Manila, Philippines
| | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Friedrich-Alexander University Erlangen, Nurenberg, Germany
| | - Justin Wu
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong, China
| | - Ming-Shiang Wu
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | - Peter Malfertheiner
- Medizinixhe Klinik und Poliklinik II, Ludwig Maximillian University Klinikum, Munich, Germany
- Klinik und Poliklinik für Radiologie, Ludwig Maximillian University Klinikum, Munich, Germany
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31
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Luo D, Kuang F, Du J, Zhou M, Liu X, Luo X, Tang Y, Li B, Su S. Artificial Intelligence-Assisted Endoscopic Diagnosis of Early Upper Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Front Oncol 2022; 12:855175. [PMID: 35756602 PMCID: PMC9229174 DOI: 10.3389/fonc.2022.855175] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Objective The aim of this study was to assess the diagnostic ability of artificial intelligence (AI) in the detection of early upper gastrointestinal cancer (EUGIC) using endoscopic images. Methods Databases were searched for studies on AI-assisted diagnosis of EUGIC using endoscopic images. The pooled area under the curve (AUC), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) with 95% confidence interval (CI) were calculated. Results Overall, 34 studies were included in our final analysis. Among the 17 image-based studies investigating early esophageal cancer (EEC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.95 (95% CI, 0.95–0.96), 0.95 (95% CI, 0.94–0.95), 10.76 (95% CI, 7.33–15.79), 0.07 (95% CI, 0.04–0.11), and 173.93 (95% CI, 81.79–369.83), respectively. Among the seven patient-based studies investigating EEC detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.98, 0.94 (95% CI, 0.91–0.96), 0.90 (95% CI, 0.88–0.92), 6.14 (95% CI, 2.06–18.30), 0.07 (95% CI, 0.04–0.11), and 69.13 (95% CI, 14.73–324.45), respectively. Among the 15 image-based studies investigating early gastric cancer (EGC) detection, the pooled AUC, sensitivity, specificity, PLR, NLR, and DOR were 0.94, 0.87 (95% CI, 0.87–0.88), 0.88 (95% CI, 0.87–0.88), 7.20 (95% CI, 4.32–12.00), 0.14 (95% CI, 0.09–0.23), and 48.77 (95% CI, 24.98–95.19), respectively. Conclusions On the basis of our meta-analysis, AI exhibited high accuracy in diagnosis of EUGIC. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/, identifier PROSPERO (CRD42021270443).
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Affiliation(s)
- De Luo
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Fei Kuang
- Department of General Surgery, Changhai Hospital of The Second Military Medical University, Shanghai, China
| | - Juan Du
- Department of Clinical Medicine, Southwest Medical University, Luzhou, China
| | - Mengjia Zhou
- Department of Ultrasound, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiangdong Liu
- Department of Hepatobiliary Surgery, Zigong Fourth People's Hospital, Zigong, China
| | - Xinchen Luo
- Department of Gastroenterology, Zigong Third People's Hospital, Zigong, China
| | - Yong Tang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Bo Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Song Su
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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32
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Zhou Y, Yuan X, Zhang X, Liu W, Wu Y, Yen GG, Hu B, Yi Z. Evolutionary Neural Architecture Search for Automatic Esophageal Lesion Identification and Segmentation. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2022. [DOI: 10.1109/tai.2021.3134600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Yao Zhou
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
| | - Xianglei Yuan
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaozhi Zhang
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
| | - Wei Liu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Wu
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
| | - Gary G. Yen
- School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK, USA
| | - Bing Hu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Zhang Yi
- Center of Intelligent Medicine, College of Computer Science, Sichuan University, Chengdu, China
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33
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Chen PC, Lu YR, Kang YN, Chang CC. The Accuracy of Artificial Intelligence in the Endoscopic Diagnosis of Early Gastric Cancer: Pooled Analysis Study. J Med Internet Res 2022; 24:e27694. [PMID: 35576561 PMCID: PMC9152716 DOI: 10.2196/27694] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 10/23/2021] [Accepted: 11/15/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) for gastric cancer diagnosis has been discussed in recent years. The role of AI in early gastric cancer is more important than in advanced gastric cancer since early gastric cancer is not easily identified in clinical practice. However, to our knowledge, past syntheses appear to have limited focus on the populations with early gastric cancer. OBJECTIVE The purpose of this study is to evaluate the diagnostic accuracy of AI in the diagnosis of early gastric cancer from endoscopic images. METHODS We conducted a systematic review from database inception to June 2020 of all studies assessing the performance of AI in the endoscopic diagnosis of early gastric cancer. Studies not concerning early gastric cancer were excluded. The outcome of interest was the diagnostic accuracy (comprising sensitivity, specificity, and accuracy) of AI systems. Study quality was assessed on the basis of the revised Quality Assessment of Diagnostic Accuracy Studies. Meta-analysis was primarily based on a bivariate mixed-effects model. A summary receiver operating curve and a hierarchical summary receiver operating curve were constructed, and the area under the curve was computed. RESULTS We analyzed 12 retrospective case control studies (n=11,685) in which AI identified early gastric cancer from endoscopic images. The pooled sensitivity and specificity of AI for early gastric cancer diagnosis were 0.86 (95% CI 0.75-0.92) and 0.90 (95% CI 0.84-0.93), respectively. The area under the curve was 0.94. Sensitivity analysis of studies using support vector machines and narrow-band imaging demonstrated more consistent results. CONCLUSIONS For early gastric cancer, to our knowledge, this was the first synthesis study on the use of endoscopic images in AI in diagnosis. AI may support the diagnosis of early gastric cancer. However, the collocation of imaging techniques and optimal algorithms remain unclear. Competing models of AI for the diagnosis of early gastric cancer are worthy of future investigation. TRIAL REGISTRATION PROSPERO CRD42020193223; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=193223.
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Affiliation(s)
- Pei-Chin Chen
- Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yun-Ru Lu
- Department of General Medicine, Taipei Medical University Hospital, Taipei, Taiwan.,Department of Anesthesiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yi-No Kang
- Evidence-Based Medicine Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.,Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan.,Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.,Department of Health Care Management, College of Health Technology, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
| | - Chun-Chao Chang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
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34
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Noda H, Kaise M, Higuchi K, Koizumi E, Yoshikata K, Habu T, Kirita K, Onda T, Omori J, Akimoto T, Goto O, Iwakiri K, Tada T. Convolutional neural network-based system for endocytoscopic diagnosis of early gastric cancer. BMC Gastroenterol 2022; 22:237. [PMID: 35549679 PMCID: PMC9102244 DOI: 10.1186/s12876-022-02312-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/04/2022] [Indexed: 12/24/2022] Open
Abstract
Background Endocytoscopy (ECS) aids early gastric cancer (EGC) diagnosis by visualization of cells. However, it is difficult for non-experts to accurately diagnose EGC using ECS. In this study, we developed and evaluated a convolutional neural network (CNN)-based system for ECS-aided EGC diagnosis. Methods We constructed a CNN based on a residual neural network with a training dataset comprising 906 images from 61 EGC cases and 717 images from 65 noncancerous gastric mucosa (NGM) cases. To evaluate diagnostic ability, we used an independent test dataset comprising 313 images from 39 EGC cases and 235 images from 33 NGM cases. The test dataset was further evaluated by three endoscopists, and their findings were compared with CNN-based results. Results The trained CNN required 7.0 s to analyze the test dataset. The area under the curve of the total ECS images was 0.93. The CNN produced 18 false positives from 7 NGM lesions and 74 false negatives from 28 EGC lesions. In the per-image analysis, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 83.2%, 76.4%, 92.3%, 93.0%, and 74.6%, respectively, with the CNN and 76.8%, 73.4%, 81.3%, 83.9%, and 69.6%, respectively, for the endoscopist-derived values. The CNN-based findings had significantly higher specificity than the findings determined by all endoscopists. In the per-lesion analysis, the accuracy, sensitivity, specificity, PPV, and NPV of the CNN-based findings were 86.1%, 82.1%, 90.9%, 91.4%, and 81.1%, respectively, and those of the results calculated by the endoscopists were 82.4%, 79.5%, 85.9%, 86.9%, and 78.0%, respectively. Conclusions Compared with three endoscopists, our CNN for ECS demonstrated higher specificity for EGC diagnosis. Using the CNN in ECS-based EGC diagnosis may improve the diagnostic performance of endoscopists.
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Affiliation(s)
- Hiroto Noda
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan.
| | - Mitsuru Kaise
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Kazutoshi Higuchi
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Eriko Koizumi
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Keiichiro Yoshikata
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Tsugumi Habu
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Kumiko Kirita
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Takeshi Onda
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Jun Omori
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Teppei Akimoto
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Osamu Goto
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Katsuhiko Iwakiri
- Department of Gastroenterology, Nippon Medical School Hospital, 1-1-5, Sendagi, Bunkyo-ku, Tokyo, 113-8603, Japan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan.,AI Medical Service Inc., Tokyo, Japan
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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36
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Panarese A. Usefulness of artificial intelligence in early gastric cancer. Artif Intell Cancer 2022; 3:17-26. [DOI: 10.35713/aic.v3.i2.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 03/27/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Gastric cancer (GC) is a major cancer worldwide, with high mortality and morbidity. Endoscopy, important for the early detection of GC, requires trained skills, high-quality technologies, surveillance and screening programs. Early diagnosis allows a better prognosis, through surgical or curative endoscopic therapy. Magnified endoscopy with virtual chromoendoscopy remarkably improve the detection of early gastric cancer (EGC) when endoscopy is performed by expert endoscopists. Artificial intelligence (AI) has also been introduced to GC diagnostics to increase diagnostic efficiency. AI improves the early detection of gastric lesions because it supports the non-expert and experienced endoscopist in defining the margins of the tumor and the depth of infiltration. AI increases the detection rate of EGC, reduces the rate of missing tumors, and characterizes EGCs, allowing clinicians to make the best therapeutic decision, that is, one that ensures curability. AI has had a remarkable evolution in medicine in recent years, moving from the research phase to clinical practice. In addition, the diagnosis of GC has markedly progressed. We predict that AI will allow great evolution in the diagnosis and treatment of EGC by overcoming the variability in performance that is currently a limitation of chromoendoscopy.
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Affiliation(s)
- Alba Panarese
- Department of Gastroenterology and Endoscopy, Central Hospital, Taranto 74123, Italy
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37
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Frazzoni L, Arribas J, Antonelli G, Libanio D, Ebigbo A, van der Sommen F, de Groof AJ, Fukuda H, Ohmori M, Ishihara R, Wu L, Yu H, Mori Y, Repici A, Bergman JJGHM, Sharma P, Messmann H, Hassan C, Fuccio L, Dinis-Ribeiro M. Endoscopists' diagnostic accuracy in detecting upper gastrointestinal neoplasia in the framework of artificial intelligence studies. Endoscopy 2022; 54:403-411. [PMID: 33951743 DOI: 10.1055/a-1500-3730] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Estimates on miss rates for upper gastrointestinal neoplasia (UGIN) rely on registry data or old studies. Quality assurance programs for upper GI endoscopy are not fully established owing to the lack of infrastructure to measure endoscopists' competence. We aimed to assess endoscopists' accuracy for the recognition of UGIN exploiting the framework of artificial intelligence (AI) validation studies. METHODS Literature searches of databases (PubMed/MEDLINE, EMBASE, Scopus) up to August 2020 were performed to identify articles evaluating the accuracy of individual endoscopists for the recognition of UGIN within studies validating AI against a histologically verified expert-annotated ground-truth. The main outcomes were endoscopists' pooled sensitivity, specificity, positive and negative predictive value (PPV/NPV), and area under the curve (AUC) for all UGIN, for esophageal squamous cell neoplasia (ESCN), Barrett esophagus-related neoplasia (BERN), and gastric adenocarcinoma (GAC). RESULTS Seven studies (2 ESCN, 3 BERN, 1 GAC, 1 UGIN overall) with 122 endoscopists were included. The pooled endoscopists' sensitivity and specificity for UGIN were 82 % (95 % confidence interval [CI] 80 %-84 %) and 79 % (95 %CI 76 %-81 %), respectively. Endoscopists' accuracy was higher for GAC detection (AUC 0.95 [95 %CI 0.93-0.98]) than for ESCN (AUC 0.90 [95 %CI 0.88-0.92]) and BERN detection (AUC 0.86 [95 %CI 0.84-0.88]). Sensitivity was higher for Eastern vs. Western endoscopists (87 % [95 %CI 84 %-89 %] vs. 75 % [95 %CI 72 %-78 %]), and for expert vs. non-expert endoscopists (85 % [95 %CI 83 %-87 %] vs. 71 % [95 %CI 67 %-75 %]). CONCLUSION We show suboptimal accuracy of endoscopists for the recognition of UGIN even within a framework that included a higher prevalence and disease awareness. Future AI validation studies represent a framework to assess endoscopist competence.
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Affiliation(s)
- Leonardo Frazzoni
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Julia Arribas
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Giulio Antonelli
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy.,Department of Translational and Precision Medicine, "Sapienza" University of Rome, Rome, Italy
| | - Diogo Libanio
- CIDES/CINTESIS, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Alanna Ebigbo
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Fons van der Sommen
- Department of Electrical Engineering, VCA group, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert Jeroen de Groof
- Department of Gastroenterology and Hepatology, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hiromu Fukuda
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Masayasu Ohmori
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Ryu Ishihara
- Department of Gastrointestinal Oncology, Osaka International Cancer Institute, Osaka, Japan
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
| | - Honggang Yu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Institute for Gastroenterology and Hepatology, Wuhan University, Wuhan, China
| | - Yuichi Mori
- Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway.,Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Alessandro Repici
- Digestive Endoscopy Unit, Humanitas Research Hospital - IRCCS, Milan, Italy.,Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | | | - Prateek Sharma
- Department of Gastroenterology and Hepatology, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Helmut Messmann
- III Medizinische Klinik, Universitatsklinikum Augsburg, Augsburg, Germany
| | - Cesare Hassan
- Gastroenterology Unit, Nuovo Regina Margherita Hospital, Rome, Italy
| | - Lorenzo Fuccio
- Department of Medical and Surgical Sciences (DIMEC), University of Bologna, S. Orsola-Malpighi Hospital, Bologna, Italy
| | - Mário Dinis-Ribeiro
- Gastroenterology Department, Portuguese Oncology Institute of Porto, Porto, Portugal
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Yu T, Lin N, Zhong X, Zhang X, Zhang X, Chen Y, Liu J, Hu W, Duan H, Si J. Multi-label recognition of cancer-related lesions with clinical priors on white-light endoscopy. Comput Biol Med 2022; 143:105255. [PMID: 35151153 DOI: 10.1016/j.compbiomed.2022.105255] [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: 11/16/2021] [Revised: 01/07/2022] [Accepted: 01/20/2022] [Indexed: 11/28/2022]
Abstract
Deep learning-based computer-aided diagnosis techniques have demonstrated encouraging performance in endoscopic lesion identification and detection, and have reduced the rate of missed and false detections of disease during endoscopy. However, the interpretability of the model-based results has not been adequately addressed by existing methods. This phenomenon is directly manifested by a significant bias in the representation of feature localization. Good recognition models experience severe feature localization errors, particularly for lesions with subtle morphological features, and such unsatisfactory performance hinders the clinical deployment of models. To effectively alleviate this problem, we proposed a solution to optimize the localization bias in feature representations of cancer-related recognition models that is difficult to accurately label and identify in clinical practice. Optimization was performed in the training phase of the model through the proposed data augmentation method and auxiliary loss function based on clinical priors. The data augmentation method, called partial jigsaw, can "break" the spatial structure of lesion-independent image blocks and enrich the data feature space to decouple the interference of background features on the space and focus on fine-grained lesion features. The annotation-based auxiliary loss function used class activation maps for sample distribution correction and led the model to present localization representation converging on the gold standard annotation of visualization maps. The results show that with the improvement of our method, the precision of model recognition reached an average of 92.79%, an F1-score of 92.61%, and accuracy of 95.56% based on a dataset constructed from 23 hospitals. In addition, we quantified the evaluation representation of visualization feature maps. The improved model yielded significant offset correction results for visualized feature maps compared with the baseline model. The average visualization-weighted positive coverage improved from 51.85% to 83.76%. The proposed approach did not change the deployment capability and inference speed of the original model and can be incorporated into any state-of-the-art neural network. It also shows the potential to provide more accurate localization inference results and assist in clinical examinations during endoscopies.
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Affiliation(s)
- Tao Yu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ne Lin
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - Xingwei Zhong
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China
| | - Xiaoyan Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Xinsen Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yihe Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiquan Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weiling Hu
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China; Institute of Gastroenterology, Zhejiang University, Hangzhou, China
| | - Huilong Duan
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jianmin Si
- Department of Gastroenterology, Sir Run Run Shaw Hospital, Medical School, Zhejiang University, Hangzhou, China; Institute of Gastroenterology, Zhejiang University, Hangzhou, China
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Xie F, Zhang K, Li F, Ma G, Ni Y, Zhang W, Wang J, Li Y. Diagnostic accuracy of convolutional neural network-based endoscopic image analysis in diagnosing gastric cancer and predicting its invasion depth: a systematic review and meta-analysis. Gastrointest Endosc 2022; 95:599-609.e7. [PMID: 34979114 DOI: 10.1016/j.gie.2021.12.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/25/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND AIMS This study aimed to evaluate the accuracy and effectiveness of the convolutional neural network (CNN) in diagnosing gastric cancer and predicting the invasion depth of gastric cancer and to compare the performance of the CNN with that of endoscopists. METHODS PubMed, Embase, Web of Science, and gray literature were searched until July 23, 2021 for studies that assessed the diagnostic accuracy of CNN-assisted examinations for gastric cancer or the invasion depth of gastric cancer. Studies meeting inclusion criteria were included in the systematic review and meta-analysis. RESULTS Seventeen studies comprising 51,446 images and 174 videos of 5539 patients were included. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and area under the curve (AUC) of the CNN for diagnosing gastric cancer were 89% (95% confidence interval [CI], 85-93), 93% (95% CI, 88-97), 13.4 (95% CI, 7.3-25.5), .11 (95% CI, .07-.17), and .94 (95% CI, .91-.98), respectively. The performance of the CNN in diagnosing gastric cancer was not significantly different from that of expert endoscopists (.95 vs .90, P > .05) and was better than that of overall endoscopists (experts and nonexperts) (.95 vs .87, P < .05). The pooled sensitivity, specificity, LR+, LR-, and AUC of the CNN for predicting the invasion depth of gastric cancer were 82% (95% CI, 78-85), 90% (95% CI, 82-95), 8.4 (95% CI, 4.2-16.8), .20 (95% CI, .16-.26), and .90 (95% CI, .87-.93), respectively. CONCLUSIONS The CNN is highly accurate in diagnosing gastric cancer and predicting the invasion depth of gastric cancer. The performance of the CNN in diagnosing gastric cancer is not significantly different from that of expert endoscopists. Studies of the real-time performance of the CNN for gastric cancer diagnosis are needed to confirm these findings.
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Affiliation(s)
- Fang Xie
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Keqiang Zhang
- Second Hospital of Jilin University, Changchun, Jilin, China
| | - Feng Li
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Guorong Ma
- College of Computer Science and Technology, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuanyuan Ni
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Wei Zhang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Junchao Wang
- School of Nursing, Jilin University, Changchun, Jilin, China
| | - Yuewei Li
- School of Nursing, Jilin University, Changchun, Jilin, China
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Abe S, Tomizawa Y, Saito Y. Can artificial intelligence be your angel to diagnose early gastric cancer in real clinical practice? Gastrointest Endosc 2022; 95:679-681. [PMID: 35177258 DOI: 10.1016/j.gie.2021.12.042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 12/31/2021] [Indexed: 12/11/2022]
Affiliation(s)
- Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
| | - Yutaka Tomizawa
- Division of Gastroenterology, Harborview Medical Center, University of Washington, Seattle, Washington, USA
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, Tokyo, Japan
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Sharma P, Hassan C. Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia. Gastroenterology 2022; 162:1056-1066. [PMID: 34902362 DOI: 10.1053/j.gastro.2021.11.040] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/09/2021] [Accepted: 11/19/2021] [Indexed: 12/24/2022]
Abstract
Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-related deaths every year. Despite its efficacy in preventing cancer mortality, diagnostic upper GI endoscopy is affected by a substantial miss rate of neoplastic lesions due to failure to recognize a visible lesion or imperfect navigation. This may be offset by the real-time application of artificial intelligence (AI) for detection (computer-aided detection [CADe]) and characterization (computer-aided diagnosis [CADx]) of upper GI neoplasia. Stand-alone performance of CADe for esophageal squamous cell neoplasia, Barrett's esophagus-related neoplasia, and gastric cancer showed promising accuracy, sensitivity ranging between 83% and 93%. However, incorporation of CADe/CADx in clinical practice depends on several factors, such as possible bias in the training or validation phases of these algorithms, its interaction with human endoscopists, and clinical implications of false-positive results. The aim of this review is to guide the clinician across the multiple steps of AI development in clinical practice.
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Affiliation(s)
- Prateek Sharma
- University of Kansas School of Medicine, Kansas City, Missouri; Kansas City Veterans Affairs Medical Center, Kansas City, Missouri
| | - Cesare Hassan
- Humanitas University, Department of Biomedical Sciences, Pieve Emanuele, Italy; Humanitas Clinical and Research Center-IRCCS, Endoscopy Unit, Rozzano, Italy.
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42
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Zhao Q, Chi T. Deep learning model can improve the diagnosis rate of endoscopic chronic atrophic gastritis: a prospective cohort study. BMC Gastroenterol 2022; 22:133. [PMID: 35321641 PMCID: PMC8941797 DOI: 10.1186/s12876-022-02212-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 03/15/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND AND AIMS Chronic atrophic gastritis (CAG) is a precancerous form of gastric cancer. However, with pathological diagnosis as the gold standard, the sensitivity of endoscopic diagnosis of atrophy is only 42%. We developed a deep learning (DL)-based real-time video monitoring diagnostic model for endoscopic CAG and conducted a prospective cohort study to verify whether this diagnostic model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists. METHODS A U-NET network was used to build a real-time video monitoring diagnostic model for endoscopic CAG based on DL. We enrolled 431 patients who underwent gastroscopy from October 1, 2020, to December 1, 2020. To keep the baseline data of enrolled patient uniform and control for confounding factors, we applied a paired design and included the same patients in both the DL and the endoscopist group. RESULTS The DL model improved the diagnosis rate of endoscopic CAG compared with that of endoscopists. Compared with diagnoses by endoscopists, the proportions of moderate and severe CAG in the atrophy patients diagnosed by the DL model were significantly larger, the proportion of "type O" CAG was significantly larger, the number of atrophy sites found was significantly increased, and the number of biopsies was significantly decreased. Compared with diagnoses by endoscopists, in the atrophic lesions diagnosed by the DL model, the proportions of severe atrophy and severe intestinal metaplasia were significantly increased. CONCLUSIONS Our study suggested the DL model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists. TRIAL REGISTRATION ChiCTR2100044458, 18/03/2020.
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Affiliation(s)
- Quchuan Zhao
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China
| | - Tianyu Chi
- Department of Gastroenterology, Xuanwu Hospital of Capital Medical University, 45 Chang-chun Street, Beijing, 100053, China.
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43
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Zhao Y, Hu B, Wang Y, Yin X, Jiang Y, Zhu X. Identification of gastric cancer with convolutional neural networks: a systematic review. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:11717-11736. [PMID: 35221775 PMCID: PMC8856868 DOI: 10.1007/s11042-022-12258-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 06/20/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
The identification of diseases is inseparable from artificial intelligence. As an important branch of artificial intelligence, convolutional neural networks play an important role in the identification of gastric cancer. We conducted a systematic review to summarize the current applications of convolutional neural networks in the gastric cancer identification. The original articles published in Embase, Cochrane Library, PubMed and Web of Science database were systematically retrieved according to relevant keywords. Data were extracted from published papers. A total of 27 articles were retrieved for the identification of gastric cancer using medical images. Among them, 19 articles were applied in endoscopic images and 8 articles were applied in pathological images. 16 studies explored the performance of gastric cancer detection, 7 studies explored the performance of gastric cancer classification, 2 studies reported the performance of gastric cancer segmentation and 2 studies analyzed the performance of gastric cancer delineating margins. The convolutional neural network structures involved in the research included AlexNet, ResNet, VGG, Inception, DenseNet and Deeplab, etc. The accuracy of studies was 77.3 - 98.7%. Good performances of the systems based on convolutional neural networks have been showed in the identification of gastric cancer. Artificial intelligence is expected to provide more accurate information and efficient judgments for doctors to diagnose diseases in clinical work.
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Affiliation(s)
- Yuxue Zhao
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
| | - Bo Hu
- Department of Thoracic Surgery, Qingdao Municipal Hospital, Qingdao, China
| | - Ying Wang
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
| | - Xiaomeng Yin
- Pediatrics Intensive Care Unit, Qingdao Municipal Hospital, Qingdao, China
| | - Yuanyuan Jiang
- International Medical Services, Qilu Hospital of Shandong University, Jinan, China
| | - Xiuli Zhu
- School of Nursing, Department of Medicine, Qingdao University, No. 15, Ningde Road, Shinan District, Qingdao, 266073 China
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Yang H, Yang WJ, Hu B. Gastric epithelial histology and precancerous conditions. World J Gastrointest Oncol 2022; 14:396-412. [PMID: 35317321 PMCID: PMC8919001 DOI: 10.4251/wjgo.v14.i2.396] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/08/2021] [Accepted: 01/05/2022] [Indexed: 02/06/2023] Open
Abstract
The most common histological type of gastric cancer (GC) is gastric adenocarcinoma arising from the gastric epithelium. Less common variants include mesenchymal, lymphoproliferative and neuroendocrine neoplasms. The Lauren scheme classifies GC into intestinal type, diffuse type and mixed type. The WHO classification includes papillary, tubular, mucinous, poorly cohesive and mixed GC. Chronic atrophic gastritis (CAG) and intestinal metaplasia are recommended as common precancerous conditions. No definite precancerous condition of diffuse/poorly/undifferentiated type is recommended. Chronic superficial inflammation and hyperplasia of foveolar cells may be the focus. Presently, the management of early GC and precancerous conditions mainly relies on endoscopy including diagnosis, treatment and surveillance. Management of precancerous conditions promotes the early detection and treatment of early GC, and even prevent the occurrence of GC. In the review, precancerous conditions including CAG, metaplasia, foveolar hyperplasia and gastric hyperplastic polyps derived from the gastric epithelium have been concluded, based on the overview of gastric epithelial histological organization and its renewal.
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Affiliation(s)
- Hang Yang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Wen-Juan 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
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Jin Z, Gan T, Wang P, Fu Z, Zhang C, Yan Q, Zheng X, Liang X, Ye X. Deep learning for gastroscopic images: computer-aided techniques for clinicians. Biomed Eng Online 2022; 21:12. [PMID: 35148764 PMCID: PMC8832738 DOI: 10.1186/s12938-022-00979-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 01/21/2022] [Indexed: 12/13/2022] Open
Abstract
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
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Affiliation(s)
- Ziyi Jin
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Tianyuan Gan
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Peng Wang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Zuoming Fu
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Chongan Zhang
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China
| | - Qinglai Yan
- Hangzhou Center for Medical Device Quality Supervision and Testing, CFDA, Hangzhou, 310000, People's Republic of China
| | - Xueyong Zheng
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xiao Liang
- Department of General Surgery, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, People's Republic of China
| | - Xuesong Ye
- Biosensor National Special Laboratory, Key Laboratory of Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, People's Republic of China.
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Real-time artificial intelligence for detecting focal lesions and diagnosing neoplasms of the stomach by white-light endoscopy (with videos). Gastrointest Endosc 2022; 95:269-280.e6. [PMID: 34547254 DOI: 10.1016/j.gie.2021.09.017] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 09/04/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND AIMS White-light endoscopy (WLE) is the most pivotal tool to detect gastric cancer in an early stage. However, the skill among endoscopists varies greatly. Here, we aim to develop a deep learning-based system named ENDOANGEL-LD (lesion detection) to assist in detecting all focal gastric lesions and predicting neoplasms by WLE. METHODS Endoscopic images were retrospectively obtained from Renmin Hospital of Wuhan University (RHWU) for the development, validation, and internal test of the system. Additional external tests were conducted in 5 other hospitals to evaluate the robustness. Stored videos from RHWU were used for assessing and comparing the performance of ENDOANGEL-LD with that of experts. Prospective consecutive patients undergoing upper endoscopy were enrolled from May 6, 2021 to August 2, 2021 in RHWU to assess clinical practice applicability. RESULTS Over 10,000 patients undergoing upper endoscopy were enrolled in this study. The sensitivities were 96.9% and 95.6% for detecting gastric lesions and 92.9% and 91.7% for diagnosing neoplasms in internal and external patients, respectively. In 100 videos, ENDOANGEL-LD achieved superior sensitivity and negative predictive value for detecting gastric neoplasms from that of experts (100% vs 85.5% ± 3.4% [P = .003] and 100% vs 86.4% ± 2.8% [P = .002], respectively). In 2010 prospective consecutive patients, ENDOANGEL-LD achieved a sensitivity of 92.8% for detecting gastric lesions with 3.04 ± 3.04 false positives per gastroscopy and a sensitivity of 91.8% and specificity of 92.4% for diagnosing neoplasms. CONCLUSIONS Our results show that ENDOANGEL-LD has great potential for assisting endoscopists in screening gastric lesions and suspicious neoplasms in clinical work. (Clinical trial registration number: ChiCTR2100045963.).
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A deep representation to fully characterize hyperplastic, adenoma, and serrated polyps on narrow band imaging sequences. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-021-00633-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Xiao Z, Ji D, Li F, Li Z, Bao Z. Application of Artificial Intelligence in Early Gastric Cancer Diagnosis. Digestion 2022; 103:69-75. [PMID: 34666330 DOI: 10.1159/000519601] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/13/2021] [Indexed: 02/04/2023]
Abstract
BACKGROUND With the development of new technologies such as magnifying endoscopy with narrow band imaging, endoscopists achieved better accuracy for diagnosis of gastric cancer (GC) in various aspects. However, to master such skill takes substantial effort and could be difficult for inexperienced doctors. Therefore, a novel diagnostic method based on artificial intelligence (AI) was developed and its effectiveness was confirmed in many studies. AI system using convolutional neural network has showed marvelous results in the ongoing trials of computer-aided detection of colorectal polyps. SUMMARY With AI's efficient computational power and learning capacities, endoscopists could improve their diagnostic accuracy and avoid the overlooking or over-diagnosis of gastric neoplasm. Several systems have been reported to achieved decent accuracy. Thus, AI-assisted endoscopy showed great potential on more accurate and sensitive ways for early detection, differentiation, and invasion depth prediction of gastric lesions. However, the feasibility, effectiveness, and safety in daily practice remain to be tested. Key messages: This review summarizes the current status of different AI applications in early GC diagnosis. More randomized controlled trails will be needed before AI could be widely put into clinical practice.
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Affiliation(s)
- Zili Xiao
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China,
| | - Danian Ji
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Feng Li
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhengliang Li
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
| | - Zhijun Bao
- Department of Gastroenterology, Huadong Hospital Affiliated to Fudan University, Shanghai, China
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Visaggi P, de Bortoli N, Barberio B, Savarino V, Oleas R, Rosi EM, Marchi S, Ribolsi M, Savarino E. Artificial Intelligence in the Diagnosis of Upper Gastrointestinal Diseases. J Clin Gastroenterol 2022; 56:23-35. [PMID: 34739406 PMCID: PMC9988236 DOI: 10.1097/mcg.0000000000001629] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Artificial intelligence (AI) has enormous potential to support clinical routine workflows and therefore is gaining increasing popularity among medical professionals. In the field of gastroenterology, investigations on AI and computer-aided diagnosis (CAD) systems have mainly focused on the lower gastrointestinal (GI) tract. However, numerous CAD tools have been tested also in upper GI disorders showing encouraging results. The main application of AI in the upper GI tract is endoscopy; however, the need to analyze increasing loads of numerical and categorical data in short times has pushed researchers to investigate applications of AI systems in other upper GI settings, including gastroesophageal reflux disease, eosinophilic esophagitis, and motility disorders. AI and CAD systems will be increasingly incorporated into daily clinical practice in the coming years, thus at least basic notions will be soon required among physicians. For noninsiders, the working principles and potential of AI may be as fascinating as obscure. Accordingly, we reviewed systematic reviews, meta-analyses, randomized controlled trials, and original research articles regarding the performance of AI in the diagnosis of both malignant and benign esophageal and gastric diseases, also discussing essential characteristics of AI.
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Affiliation(s)
- Pierfrancesco Visaggi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Nicola de Bortoli
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Brigida Barberio
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
| | - Vincenzo Savarino
- Gastroenterology Unit, Department of Internal Medicine, University of Genoa, Genoa
| | - Roberto Oleas
- Ecuadorean Institute of Digestive Diseases, Guayaquil, Ecuador
| | - Emma M. Rosi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Santino Marchi
- Gastroenterology Unit, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa
| | - Mentore Ribolsi
- Department of Digestive Diseases, Campus Bio Medico University of Rome, Roma, Italy
| | - Edoardo Savarino
- Department of Surgery, Oncology, and Gastroenterology, Division of Gastroenterology, University of Padua, Padua
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Goyal H, Sherazi SAA, Mann R, Gandhi Z, Perisetti A, Aziz M, Chandan S, Kopel J, Tharian B, Sharma N, Thosani N. Scope of Artificial Intelligence in Gastrointestinal Oncology. Cancers (Basel) 2021; 13:5494. [PMID: 34771658 PMCID: PMC8582733 DOI: 10.3390/cancers13215494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
Gastrointestinal cancers are among the leading causes of death worldwide, with over 2.8 million deaths annually. Over the last few decades, advancements in artificial intelligence technologies have led to their application in medicine. The use of artificial intelligence in endoscopic procedures is a significant breakthrough in modern medicine. Currently, the diagnosis of various gastrointestinal cancer relies on the manual interpretation of radiographic images by radiologists and various endoscopic images by endoscopists. This can lead to diagnostic variabilities as it requires concentration and clinical experience in the field. Artificial intelligence using machine or deep learning algorithms can provide automatic and accurate image analysis and thus assist in diagnosis. In the field of gastroenterology, the application of artificial intelligence can be vast from diagnosis, predicting tumor histology, polyp characterization, metastatic potential, prognosis, and treatment response. It can also provide accurate prediction models to determine the need for intervention with computer-aided diagnosis. The number of research studies on artificial intelligence in gastrointestinal cancer has been increasing rapidly over the last decade due to immense interest in the field. This review aims to review the impact, limitations, and future potentials of artificial intelligence in screening, diagnosis, tumor staging, treatment modalities, and prediction models for the prognosis of various gastrointestinal cancers.
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Affiliation(s)
- Hemant Goyal
- Department of Internal Medicine, The Wright Center for Graduate Medical Education, 501 S. Washington Avenue, Scranton, PA 18505, USA
| | - Syed A. A. Sherazi
- Department of Medicine, John H Stroger Jr Hospital of Cook County, 1950 W Polk St, Chicago, IL 60612, USA;
| | - Rupinder Mann
- Department of Medicine, Saint Agnes Medical Center, 1303 E. Herndon Ave, Fresno, CA 93720, USA;
| | - Zainab Gandhi
- Department of Medicine, Geisinger Wyoming Valley Medical Center, 1000 E Mountain Dr, Wilkes-Barre, PA 18711, USA;
| | - Abhilash Perisetti
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Muhammad Aziz
- Department of Gastroenterology and Hepatology, University of Toledo Medical Center, 3000 Arlington Avenue, Toledo, OH 43614, USA;
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Health Creighton University Medical Center, 7500 Mercy Rd, Omaha, NE 68124, USA;
| | - Jonathan Kopel
- Department of Medicine, Texas Tech University Health Sciences Center, 3601 4th St, Lubbock, TX 79430, USA;
| | - Benjamin Tharian
- Department of Gastroenterology and Hepatology, The University of Arkansas for Medical Sciences, 4301 W Markham St, Little Rock, AR 72205, USA;
| | - Neil Sharma
- Division of Interventional Oncology & Surgical Endoscopy (IOSE), Parkview Cancer Institute, 11050 Parkview Circle, Fort Wayne, IN 46845, USA; (A.P.); (N.S.)
| | - Nirav Thosani
- Division of Gastroenterology, Hepatology & Nutrition, McGovern Medical School, UTHealth, 6410 Fannin, St #1014, Houston, TX 77030, USA;
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