Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence

Kuiper, Teaco; Alderlieste, Yasser A.; Tytgat, Kristien M. A. J.; Vlug, Malaika S.; Nabuurs, Joyce A.; Bastiaansen, Barbara A.; Löwenberg, Mark; Fockens, Paul; Dekker, Evelien

doi:10.1055/s-0034-1378112

Cited by 37 publications

(44 citation statements)

References 18 publications

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“…113 Another endoscopic method based on laser-induced AF spectroscopy has been considered as a complimentary technique for the real-time prediction of polyp histology. [114][115][116] …”

Section: Crcmentioning

confidence: 99%

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

2017

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Gastrointestinal cancer is a leading contributor to cancer-related morbidity and mortality worldwide. Early diagnosis currently plays a key role in the prognosis of patients with gastrointestinal cancer. Despite the advances in endoscopy over the last decades, missing lesions, undersampling and incorrect sampling in biopsies, as well as invasion still result in a poor diagnostic rate of early gastrointestinal cancers. Accordingly, there is a pressing need to develop noninvasive methods for the early detection of gastrointestinal cancers. Biomedical optical spectroscopy, including infrared spectroscopy, Raman spectroscopy, diffuse scattering spectroscopy and autofluorescence, is capable of providing structural and chemical information about biological specimens with the advantages of non-destruction, non-invasion and reagent-free and waste-free analysis and has thus been widely investigated for the diagnosis of oesophageal, gastric and colorectal cancers. This review will introduce the advances of biomedical optical spectroscopy techniques, highlight their applications for the early detection of gastrointestinal cancers and discuss their limitations.

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“…113 Another endoscopic method based on laser-induced AF spectroscopy has been considered as a complimentary technique for the real-time prediction of polyp histology. [114][115][116] …”

Section: Crcmentioning

confidence: 99%

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

2017

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“…evaluated the performance of CAD with laser‐induced fluorescence spectroscopy, which showed a high NPV for adenomatous polyp histology of 96.1% in a single‐center prospective trial that included 27 patients with 137 diminutive polyps that met the PIVI‐2 criteria. Conversely, in another prospective study with the same device, Kuiper et al . reported an NPV limited to 73.5% for identifying small adenomas.…”

Section: Automated Polyp Characterizationmentioning

confidence: 93%

“…Although many retrospective/experimental studies were conducted in this field, only five prospective studies evaluated real‐time use of AI during colonoscopy . Kominami et al .…”

Section: Automated Polyp Characterizationmentioning

confidence: 99%

Artificial intelligence and colonoscopy: Current status and future perspectives

Kudo

Mori

Misawa

et al. 2019

Digestive Endoscopy

119

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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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Section: Future Directionsmentioning

confidence: 99%

“…A couple of CAD systems for colonoscopy have already been prospectively validated and data describing both good (efficacy) and bad (limitations) aspects of using CAD have been revealed. [46][47][48][49][50] For example, Mori et al conducted a large-scale prospective study including 791 patients for validation of their developed CAD model designed for endocytoscopy. They found their model provided 93.7% NPV for diminutive rectosigmoid adenomas even under the worst-case scenario.…”

Section: Difference Of the Research Phase Between Cad For Colonoscopymentioning

confidence: 99%

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

Mori

Kudo

Mohmed

et al. 2019

Digestive Endoscopy

112

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With recent breakthroughs in artificial intelligence, computer‐aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early‐stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false‐negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real‐time gastroscopy to provide on‐site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.

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Automatic optical diagnosis of small colorectal lesions by laser-induced autofluorescence

Cited by 37 publications

References 18 publications

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Biomedical optical spectroscopy for the early diagnosis of gastrointestinal neoplasms

Artificial intelligence and colonoscopy: Current status and future perspectives

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective

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