Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Ueyama, Hiroya; Kato, Yusuke; Akazawa, Yoichi; Yatagai, Noboru; Kohno, Hiroyuki; Takeda, Tsutomu; Matsumoto, Keīichi; Ueda, Kazuki; Matsumoto, Kenshi; Hojo, Mariko; Yao, Takashi; Nagahara, Akihito; Tada, Takeshi

doi:10.1111/jgh.15190

Cited by 102 publications

(72 citation statements)

References 33 publications

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“…In other studies, a microendoscope with narrow-band imaging (ME-NBI) and EC were used in the classification of ESCC [180,181]. FICE and ME-NBI were used in the diagnosis of GC [182,183]. BLI and LCI were employed in the prediction of H. pylori infections.…”

Section: Applicationmentioning

confidence: 99%

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

Jin

Zhang

et al. 2021

IEEE Access

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Minimally invasive medicine has become mainstream because of its crucial clinical significance in providing a low risk of postoperative complications, limited blood loss, short postoperative recovery time, and small sizes of associated physiological tissue wounds. Endoscopic navigation systems comprise a research hot spot in medical science and technology and are an essential means to achieve precision medicine and improve surgical operation safety. As a core component in endoscopic navigation during minimally invasive surgery, endoscopes play a critical role in disease diagnosis and treatment. The development of endoscopic vision technologies has resulted in a renewed drive to further develop endoscopic navigation systems. Multiple endoscopic optical imaging modalities provide data sources for endoscopic vision technology, including white-light endoscopy, contrast-enhanced imaging and technologies involving magnified observation. Endoscopic vision is a specific application of computer vision involving the use of endoscopes that include instrument tracking, endoscopic view expansion, and suspicious lesion tracking in the application of endoscopic navigation. These techniques help surgeons or surgical robots locate instruments and lesions and expand the field of view of the endoscope. Although these technologies have been applied to various clinical and pre-clinical diagnoses and treatments, the use and combination of these advanced technologies in endoscopic navigation system for specific clinical requirements remains challenging. This review performs a broad survey of advanced endoscopic vision technologies and their application in endoscopic navigation systems. Finally, we discuss the challenges and future directions in implementing and developing endoscopic navigation systems.

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Section: Applicationmentioning

confidence: 99%

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

Jin

Zhang

et al. 2021

IEEE Access

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“…The integration of NBI and ANN has made amazing progress in early screening. Three studies respectively reported their models for the diagnosis of early GC[ 81 , 86 , 87 ]. For instance, Horiuchi et al [ 87 ] trained a CNN system that could differentiate between early GC and gastritis.…”

Section: Achievements Of Ann Research In Gi Diseasesmentioning

confidence: 99%

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Cao¹,

Zhang²,

Wei³

et al. 2021

WJG

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Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

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“…An array of CADx models has been developed to perform binary classification tasks for various qualitative properties of GI diseases, mostly for neoplastic lesions (Tables 3 4,10,22–34 and 4 16,35–41 ). The models determine and predict the group of qualitative property to which each lesion belongs.…”

Section: Cadxmentioning

confidence: 99%

Artificial intelligence in endoscopy: Present and future perspectives

Sumiyama

Futakuchi

Kamba

et al. 2020

Digestive Endoscopy

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Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deepleaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of information within short periods. The amount of research applying DL-based models to real-time computer-aided diagnosis (CAD) systems has been increasing steadily in the GI endoscopy field. An array of published data has already demonstrated the advantages of DL-based CAD models in the detection and characterization of various neoplastic lesions, regardless of the level of the GI tract. Although the diagnostic performances and study designs vary widely, owing to a lack of academic standards to assess the capability of AI for GI endoscopic diagnosis fairly, the superiority of CAD models has been demonstrated for almost all applications studied so far. Most of the challenges associated with AI in the endoscopy field are general problems for AI models used in the real world outside of medical fields. Solutions have been explored seriously and some solutions have been tested in the endoscopy field. Given that AI has become the basic technology to make machines react to the environment, AI would be a major technological paradigm shift, for not only diagnosis but also treatment. In the near future, autonomous endoscopic diagnosis might no longer be just a dream, as we are witnessing with the advent of autonomously driven electric vehicles.

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Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Cited by 102 publications

References 33 publications

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial intelligence in endoscopy: Present and future perspectives

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