Regulatory Science on AI-based Medical Devices and Systems

Chinzei, Kiyoyuki; Shimizu, A.; Mori, Kensaku; Harada, Kanako; Takeda, Hideaki; Hashizume, Makoto; Ishizuka, Mayumi; Kato, Nobuo; Kawamori, Ryuzo; Kyo, Shunei; Nagata, Kyosuke; Yamada, Takashi; Sakuma, Ichiro; Mitsuishi, Mamoru

doi:10.14326/abe.7.118

Cited by 30 publications

(12 citation statements)

References 2 publications

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“…This is partly driven by the anticipation that thresholds for clinical implementation of many CAD systems will not be high, due to the fact that most of these systems rely on a 'low risk, high impact' principle, although currently regulatory entities do not necessarily consider AI to be low risk. 32 A spurious algorithm prediction will at worst lead to an additional biopsy, yet the algorithm may detect a cancer that might otherwise have been missed. Furthermore, nearly all CAD systems are now considered as second-readers, merely assisting endoscopists.…”

Section: Recent Advances In Clinical Practicementioning

confidence: 99%

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Sommen

Groof

Struyvenberg

et al. 2020

Gut

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There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challenge for reviewers and readers of GI journals. To appreciate scientific quality and novelty of machine learning studies, understanding of the technical basis and commonly used techniques is required. Clinicians often lack this technical background, while machine learning experts may be unfamiliar with clinical relevance and implications for daily practice. Therefore, there is an increasing need for a multidisciplinary, international evaluation on how to perform high-quality machine learning research in endoscopy. This review aims to provide guidance for readers and reviewers of peer-reviewed GI journals to allow critical appraisal of the most relevant quality requirements of machine learning studies. The paper provides an overview of common trends and their potential pitfalls and proposes comprehensive quality requirements in six overarching themes: terminology, data, algorithm description, experimental setup, interpretation of results and machine learning in clinical practice.

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Section: Recent Advances In Clinical Practicementioning

confidence: 99%

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Sommen

Groof

Struyvenberg

et al. 2020

Gut

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“…In Japan, the Pharmaceuticals and Medical Devices Agency (PMDA), a regulatory body, issued a statement on the science of AI‐assisted medical devices and systems in 2018. In this document, the PMDA classified the risks of using the CAD system into five categories, in which the fifth harbors a risk of treatment failure as a result of the misdiagnosis provided by CAD . Considering this risk stratification and other factors, PMDA requests the applicants to carry out a retrospective or prospective evaluation of the CAD system.…”

Section: Future Directionsmentioning

confidence: 99%

“…In this document, the PMDA classified the risks of using the CAD system into five categories, in which the fifth harbors a risk of treatment failure as a result of the misdiagnosis provided by CAD. 67 Considering this risk stratification and other factors, PMDA requests the applicants to carry out a retrospective or prospective evaluation of the CAD system. Currently, only two CAD systems designed for use with laser-induced fluorescence spectroscopy 54,62 and endocytoscopy [42][43][44][45] are approved by regulatory bodies (WavSTAT4; Pentax Corp., Tokyo, Japan, EndoBRAIN; Cybernet System Corp., Tokyo, Japan), but we expect that much more CAD systems designed for colonoscopy will be approved by regulatory bodies in a few years.…”

Section: Requirement For Regulatory Approval and The Strategy To Addrmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

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117

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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.

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“…The hurdle of obtaining approval differs according to countries and role of AI in clinical practice. 80 East et al 81 proposed three roles of CADx for colonoscopy. A second observer, a concurrent observer, or an independent decision maker.…”

Section: Regulatory Approval and Legal Issues A) Regulatory Approvalmentioning

confidence: 99%

Quality assurance of computer-aided detection and diagnosis in colonoscopy

Vinsard

Mori

Misawa

et al. 2019

Gastrointestinal Endoscopy

108

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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.

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Regulatory Science on AI-based Medical Devices and Systems

Cited by 30 publications

References 2 publications

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field

Artificial intelligence and colonoscopy: Current status and future perspectives

Quality assurance of computer-aided detection and diagnosis in colonoscopy

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