Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions

Inomata, H.; Tamai, Naoto; Aihara, Hiroyuki; Sumiyama, Kazuki; Saito, Shôichi; Kato, Tomohiro; Tajiri, Hisao

doi:10.3748/wjg.v19.i41.7146

Cited by 39 publications

(29 citation statements)

References 24 publications

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“…Inomata et al demonstrated that quantitative analysis of the AFI color using the G/R ratio was effective in distinguishing sporadic neoplastic lesions (28). The most important observation in the present study is that we found for the first time that the G/R ratio is helpful in discriminating dysplasia/cancer in UC as well.…”

Section: Discussionsupporting

confidence: 69%

“…The color of mucosa was divided into green as non-neoplastic or purple as neoplastic. due to individual variations in the AFI intensity, the AFI intensity of any identified lesion was also compared with the intensity of the non-neoplastic area (both UC-involved and -uninvolved area) in each individual, and the corrected value, termed green/red (G/R) ratio, was estimated by dividing the green color tone intensity values by the red color tone intensity values, based on the method previously reported (28). Color tone intensity analysis was carried out with still images of the colorectal Histopathology.…”

Section: Conventional Endoscopic Analysismentioning

confidence: 99%

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Advanced endoscopic features of ulcerative colitis-associated neoplasias: Quantification of autofluorescence imaging

Yoshioka

Mitsuyama

Takedatsu

et al. 2015

International Journal of Oncology

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Abstract. Ulcerative colitis (UC) patients are well known to carry a higher risk of developing colorectal dysplasia/cancer. However, it is hard to detect the lesion in the early phase during colonoscopy. This pilot study was conducted to analyze the endoscopic characteristics of neoplastic lesions associated with UC using advanced imaging techniques. This is a retrospective analysis of 15 colorectal neoplastic lesion obtained from 11 UC patients during remission who underwent whitelight-and advanced endoscopic imaging techniques, including chromoendoscopy, narrow-band imaging and autofluorescence imaging (AFI), and were treated with surgery. These lesions were analyzed for histology, location, size, shape, color and endoscopic features. The green/red ratio was also assessed to quantify the AFI intensity. All 11 patients had extensive colitis with the median disease duration of 14.0 years. A total of 15 lesions, consisting of 8 high-grade dysplasia and 7 cancer, was mostly located in the distal colon (86.7%, 13/15) with the mean size of 8.6 mm. The shape was protruding in 46.7% (7/15), flat elevated in 40.0% (6/15) and flat in 13.3% (2/15) and the color was red in 60.0% (9/15), same colored in 33.3% (5/15) and discolored in 6.7% (1/15). The lesion predominantly showed Kudo's neoplastic pit pattern in 86.7% (13/15; 5 type IIIL, 7 type IV and 1 type VI) on chromoendoscopy and Sano's neoplastic capillary pattern (type IIIa) in 63.6% (7/11) on narrow-band imaging, but were colored purple as neoplastic lesions in only 37.5% (3/8) on AFI. Of note, the AFI green/red ratio was significantly lower in the neoplastic lesions than UC-involved areas (p=0.00014) and UC-uninvolved areas (p=0.00651) irrespective of the lesion's size and histological type. In conclusion, endoscopic analysis based on advanced imaging, in particular AFI quantitation, may be helpful to detect early stage neoplastic lesions in long standing UC. Large-scale, prospective studies are needed.

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Section: Discussionsupporting

confidence: 69%

Section: Conventional Endoscopic Analysismentioning

confidence: 99%

Advanced endoscopic features of ulcerative colitis-associated neoplasias: Quantification of autofluorescence imaging

Yoshioka

Mitsuyama

Takedatsu

et al. 2015

International Journal of Oncology

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“…Amongst the device improvements, the following should be highlighted: 1) increase in image resolution and, consequently, textural information; 2) the use of wide-angle cameras showing more colon wall surface; 3) the development of zooming and magnification techniques [15] and 4) the development of new imaging methodologies such as autofluorescence imaging [16] or virtual chromoendoscopy (Olympus' Narrow Band Imaging [17], Fujinon's FICE [18] or Pentax's i-Scan [19]). This last group of techniques modify how the scene is observed by improving the contrast of endoluminal scene elements, which may help in lesion detection and also with in-vivo lesion diagnosis due to the enhanced visualization of lesion tissues [20].…”

Section: B Technical Strategies To Improve Polyp Detection Ratementioning

confidence: 99%

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Bernal

Tajkbaksh

Sánchez

et al. 2017

IEEE Trans. Med. Imaging

362

276

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Abstract-Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection subchallenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.

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“…AFI provides a green/red image that can be analyzed by a computer algorithm . Japanese medical groups conducted experimental research based on prototype software that calculates the green/red ratio of the acquired image and evaluated its performance prospectively. (See the section Prospective study with in vivo use of AI. )…”

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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Efficacy of a novel auto-fluorescence imaging system with computer-assisted color analysis for assessment of colorectal lesions

Abstract: The novel AFI system with color analysis was effective in distinguishing non-neoplastic lesions from neoplastic lesions and might allow determination of the depth of invasion.

Cited by 39 publications

References 24 publications

Advanced endoscopic features of ulcerative colitis-associated neoplasias: Quantification of autofluorescence imaging

Advanced endoscopic features of ulcerative colitis-associated neoplasias: Quantification of autofluorescence imaging

Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

Artificial intelligence and colonoscopy: Current status and future perspectives

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