Real-time computer-aided diagnosis of diminutive rectosigmoid polyps using an auto-fluorescence imaging system and novel color intensity analysis software

Horiuchi, Hiroki; Tamai, Naoto; Kamba, Shunsuke; Inomata, H.; Ohya, Tomohiko R.; Sumiyama, Kazuki

doi:10.1080/00365521.2019.1627407

Cited by 38 publications

(37 citation statements)

References 27 publications

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“…With the intent of directing the rapid endoscopic technology development, the Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) initiative (an American Society for Gastrointestinal Endoscopy (ASGE) program) suggested that a novel technology should achieve a threshold of negative predictive value (NPV) >90% for the optical diagnosis of diminutive colorectal polyps [92]. A summary of recent studies on AI systems for colorectal polyp characterization is presented in Table 2 [49,83,[93][94][95][96][97][98][99][100][101][102][103][104][105][106][107].…”

Section: Polyp Characterizationmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

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Section: Polyp Characterizationmentioning

confidence: 99%

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

et al. 2021

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“…In contrast, there has been no RCT in the CADx area. Instead, several large prospective studies have been published, two of which successfully demonstrated the validity of using AI to implement a diagnose‐and‐leave strategy 20,21 . However, there is a lack of evidence to support the broader use of CADx, including the challenge of implementing resect‐and‐discard strategies.…”

Section: Randomized Controlled Trials and Meta‐analysesmentioning

confidence: 99%

“…Instead, several large prospective studies have been published, two of which successfully demonstrated the validity of using AI to implement a diagnose-and-leave strategy. 20,21 However, there is a lack of evidence to support the broader use of CADx, including the challenge of implementing resect-and-discard strategies. Thus, more well-designed trials, preferably RCTs, are needed to establish the role of CADx in routine colonoscopy.…”

Section: Randomized Controlled Trials and Metaanalysesmentioning

confidence: 99%

Artificial intelligence in colonoscopy ‐ Now on the market. What's next?

Mori

Neumann

Misawa

et al. 2021

J of Gastro and Hepatol

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Adoption of artificial intelligence (AI) in clinical medicine is revolutionizing daily practice. In the field of colonoscopy, major endoscopy manufacturers have already launched their own AI products on the market with regulatory approval in Europe and Asia. This commercialization is strongly supported by positive evidence that has been recently established through rigorously designed prospective trials and randomized controlled trials. According to some of the trials, AI tools possibly increase the adenoma detection rate by roughly 50% and contribute to a 7–20% reduction of colonoscopy‐related costs. Given that reliable evidence is emerging, together with active commercialization, this seems to be a good time for us to review and discuss the current status of AI in colonoscopy from a clinical perspective. In this review, we introduce the advantages and possible drawbacks of AI tools and explore their future potential including the possibility of obtaining reimbursement.

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“…Aihara et al investigated the use of AI in AFI imaging on 32 patients with 102 polyps and showed 94.2% sensitivity and 88.9% specificity [33]. Additionally, Horiuchi et al conducted a prospective trial of 429 polyps in 95 patients and produced results of 80% sensitivity, 95.3% specificity, 85% PPV and 93.4% NPV [64].…”

Section: Resultsmentioning

confidence: 99%