Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy

Takeda, Kazuya; Kudo, Shin‐ei; Mori, Yuichi; Misawa, Masashi; Kudo, Toyoki; Wakamura, Kunihiko; Katagiri, Atsushi; Baba, Toshiyuki; Hidaka, Eiji; Ishida, Fumio; Inoue, Haruhiro; Oda, Masahiro; Mori, Kensaku

doi:10.1055/s-0043-105486

Cited by 113 publications

(86 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…5). 48 According to that pilot study, a newly developed algorithm could differentiate invasive cancers with sensitivity, specificity, accuracy, PPV, and NPV values of 89.4%, 98.9%, 94.1%, 98.8%, and 90.1%, respectively.…”

Section: Endocytoscopymentioning

confidence: 98%

“…Apart from differentiating between neoplastic and non‐neoplastic lesions, they explored the possibility of using CAD to identify invasive cancer (Fig. ) . According to that pilot study, a newly developed algorithm could differentiate invasive cancers with sensitivity, specificity, accuracy, PPV, and NPV values of 89.4%, 98.9%, 94.1%, 98.8%, and 90.1%, respectively.…”

Section: Automated Polyp Characterizationmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

Self Cite

117

View full text Add to dashboard Cite

Background and Aim Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps. Methods and Results Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy. Conclusion We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.

show abstract

Section: Endocytoscopymentioning

confidence: 98%

Section: Automated Polyp Characterizationmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

Self Cite

117

View full text Add to dashboard Cite

show abstract

“…Compared with CADe in which white-light endoscopy is used as the target of the image analysis, several optical technologies can be used for CADx: white light endoscopy, 25,26 magnifying narrow-band imaging (NBI), [27][28][29][30][31][32] magnifying chromoendoscopy, 33 endocytoscopy, 13,[34][35][36][37] confocal laser endomicroscopy, 38,39 spectroscopy, 40,41 and autofluorescence endoscopy. 42,43,44 Among these, the most extensively studied has been magnifying NBI; 29 probably because it may have better diagnostic performance than nonmagnified NBI and does not require staining like dye-based chromoendoscopy that can be time consuming in routine clinical use.…”

Section: B) Cadxmentioning

confidence: 99%

Quality assurance of computer-aided detection and diagnosis in colonoscopy

Vinsard

Mori

Misawa

et al. 2019

Gastrointestinal Endoscopy

Self Cite

107

View full text Add to dashboard Cite

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "Deep Learning," have direct implications for computer-aided detection and diagnosis (CADe/CADx) for colonoscopy. AI is expected to have at least 2 major roles in colonoscopy practice; polyp detection (CADe) and polyp characterization (CADx). CADe has the potential to decrease polyp miss rate, contributing to improving adenoma detection, whereas CADx can improve the accuracy of colorectal polyp optical diagnosis, leading to reduction of unnecessary polypectomy of non-neoplastic lesions, potential implementation of a resect and discard paradigm, and proper application of advanced resection techniques. A growing number of medical-engineering researchers are developing both, CADe and CADx systems, some of which allow real-time recognition of polyps or in vivo identification of adenomas with over 90% accuracy. However, the quality of the developed AI systems as well as that of the study designs vary significantly, hence raising some concerns regarding the generalization of the proposed AI systems. Initial studies were conducted in an exploratory or retrospective fashion using stored images and likely overestimating the results. These drawbacks potentially hinder smooth implementation of this novel technology into colonoscopy practice. The aim of this article is to review both contributions and limitations in recent machine learning based CADe/CADx colonoscopy studies and propose some principles that should underlie system development and clinical testing.

show abstract

“…As assistant observers or clinical decision‐makers, AI‐aided systems undoubtedly have a promising future in increasing the effective training of junior endoscopists . Furthermore, it has emerged that, given the advantages of computer vision, the depth of tumor invasion can be predicted …”

Section: Future Challenges Of Ai In Gi Endoscopymentioning

confidence: 99%

Application of artificial intelligence in gastrointestinal endoscopy

et al. 2019

J of Digest Diseases

View full text Add to dashboard Cite

With recent significant improvements in artificial intelligence (AI), especially in the field of deep learning, an increasing number of studies have evaluated the use of AI in endoscopy to detect and diagnose gastrointestinal (GI) lesions. The present review summarizes current publications on the use of AI in GI endoscopy and focuses on the challenges and future of AI-aided systems. We expect AI to provide an effective and practical method for endoscopists in lesion detection and characterization as well as in quality control in endoscopy. However, so far, most studies have remained at the preclinical stage. More attention should be paid in the future to the use of AI in reallife clinical applications.Artificial intelligence (AI), first proposed in the 1950s, 1 uses computers to simulate certain thought processes and forms of intelligent behavior. In recent years, one way to achieve AI, namely machine learning, has been used in a variety of medical fields, such as radiology, pathology, ophthalmology, orthopedics, obstetrics, and gynecology, in an attempt to improve physicians' diagnoses. 2 In the field of gastrointestinal (GI) endoscopy endoscopists face heavy workloads, and different kinds of endoscopic images, such as those of esophagogastroduodenoscopy, colonoscopy and capsule endoscopy, has led specialty gastroenterology societies to call for practical and effective methods in order to improve the efficiency and quality of endoscopy in daily clinical practice.With the advent of data-driven deep learning (DL), the inefficient and incomplete feature extraction that results from manual extraction by using traditional machine learning has been thoroughly addressed, and this has revolutionized the research and development of AI. 3,4 After having been trained by a large number of labeled images, the DL algorithm can classify enormous quantities of images. 3 A variety of DL algorithms have thus been created, of which convolutional neural networks (CNN) are the most widely used for medical image recognition.However, DL has some defects, including the requirement of highquality databases and its black-box nature, which have been summarized in Table 1. AI technology used in GI endoscopy falls mainly in two groups:computer-aided detection (CADe) system and computer-aided diagnosis (CADx) system. 5 CADe system is designed to detect and track GI lesions, while CADx system focuses mainly on identifying and characterizing them. Moreover, computer-aided monitoring (CADm) systems are designed for evaluating the examination procedure and † These authors contributed equally to this article.

show abstract

Accuracy of diagnosing invasive colorectal cancer using computer-aided endocytoscopy

Cited by 113 publications

References 15 publications

Artificial intelligence and colonoscopy: Current status and future perspectives

Artificial intelligence and colonoscopy: Current status and future perspectives

Quality assurance of computer-aided detection and diagnosis in colonoscopy

Application of artificial intelligence in gastrointestinal endoscopy

Contact Info

Product

Resources

About