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Nagao S, Tani Y, Shibata J, Tsuji Y, Tada T, Ishihara R, Fujishiro M. Implementation of artificial intelligence in upper gastrointestinal endoscopy. DEN OPEN 2022; 2:e72. [PMID: 35873509 PMCID: PMC9302271 DOI: 10.1002/deo2.72] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/11/2021] [Accepted: 10/16/2021] [Indexed: 12/24/2022]
Abstract
The application of artificial intelligence (AI) using deep learning has significantly expanded in the field of esophagogastric endoscopy. Recent studies have shown promising results in detecting and differentiating early gastric cancer using AI tools built using white light, magnified, or image-enhanced endoscopic images. Some studies have reported the use of AI tools to predict the depth of early gastric cancer based on endoscopic images. Similarly, studies based on using AI for detecting early esophageal cancer have also been reported, with an accuracy comparable to that of endoscopy specialists. Moreover, an AI system, developed to diagnose pharyngeal cancer, has shown promising performance with high sensitivity. These reports suggest that, if introduced for regular use in clinical settings, AI systems can significantly reduce the burden on physicians. This review summarizes the current status of AI applications in the upper gastrointestinal tract and presents directions for clinical practice implementation and future research.
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Affiliation(s)
- Sayaka Nagao
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
- Department of Endoscopy and Endoscopic SurgeryGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Yasuhiro Tani
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Junichi Shibata
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
| | - Yosuke Tsuji
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Tomohiro Tada
- Tada Tomohiro Institute of Gastroenterology and ProctologySaitamaJapan
- AI Medical Service Inc.TokyoJapan
- Department of Surgical OncologyGraduate School of Medicinethe University of TokyoTokyoJapan
| | - Ryu Ishihara
- Department of Gastrointestinal OncologyOsaka International Cancer InstituteOsakaJapan
| | - Mitsuhiro Fujishiro
- Department of GastroenterologyGraduate School of Medicinethe University of TokyoTokyoJapan
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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Paderno A, Piazza C, Del Bon F, Lancini D, Tanagli S, Deganello A, Peretti G, De Momi E, Patrini I, Ruperti M, Mattos LS, Moccia S. Deep Learning for Automatic Segmentation of Oral and Oropharyngeal Cancer Using Narrow Band Imaging: Preliminary Experience in a Clinical Perspective. Front Oncol 2021; 11:626602. [PMID: 33842330 PMCID: PMC8024583 DOI: 10.3389/fonc.2021.626602] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 03/08/2021] [Indexed: 01/22/2023] Open
Abstract
Introduction Fully convoluted neural networks (FCNN) applied to video-analysis are of particular interest in the field of head and neck oncology, given that endoscopic examination is a crucial step in diagnosis, staging, and follow-up of patients affected by upper aero-digestive tract cancers. The aim of this study was to test FCNN-based methods for semantic segmentation of squamous cell carcinoma (SCC) of the oral cavity (OC) and oropharynx (OP). Materials and Methods Two datasets were retrieved from the institutional registry of a tertiary academic hospital analyzing 34 and 45 NBI endoscopic videos of OC and OP lesions, respectively. The dataset referring to the OC was composed of 110 frames, while 116 frames composed the OP dataset. Three FCNNs (U-Net, U-Net 3, and ResNet) were investigated to segment the neoplastic images. FCNNs performance was evaluated for each tested network and compared to the gold standard, represented by the manual annotation performed by expert clinicians. Results For FCNN-based segmentation of the OC dataset, the best results in terms of Dice Similarity Coefficient (Dsc) were achieved by ResNet with 5(×2) blocks and 16 filters, with a median value of 0.6559. In FCNN-based segmentation for the OP dataset, the best results in terms of Dsc were achieved by ResNet with 4(×2) blocks and 16 filters, with a median value of 0.7603. All tested FCNNs presented very high values of variance, leading to very low values of minima for all metrics evaluated. Conclusions FCNNs have promising potential in the analysis and segmentation of OC and OP video-endoscopic images. All tested FCNN architectures demonstrated satisfying outcomes in terms of diagnostic accuracy. The inference time of the processing networks were particularly short, ranging between 14 and 115 ms, thus showing the possibility for real-time application.
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Affiliation(s)
- Alberto Paderno
- Department of Otorhinolaryngology-Head and Neck Surgery, ASST-Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Cesare Piazza
- Department of Otorhinolaryngology-Head and Neck Surgery, ASST-Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Francesca Del Bon
- Department of Otorhinolaryngology-Head and Neck Surgery, ASST-Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Davide Lancini
- Department of Otorhinolaryngology-Head and Neck Surgery, ASST-Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Stefano Tanagli
- Department of Otorhinolaryngology-Head and Neck Surgery, ASST-Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Alberto Deganello
- Department of Otorhinolaryngology-Head and Neck Surgery, ASST-Spedali Civili of Brescia, University of Brescia, Brescia, Italy
| | - Giorgio Peretti
- Department of Otorhinolaryngology-Head and Neck Surgery, IRCCS San Martino Hospital, University of Genoa, Genoa, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Ilaria Patrini
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Michela Ruperti
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Sara Moccia
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.,The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy.,Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
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