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Cited by 30 publications
(23 citation statements)
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“…A previous work reported the automatic detection of hookworms based on an edge extraction network and classification network in 2016; however, although the preliminary results confirmed the ability of CNN to detect hookworms, consistent with other literature findings [ 2 ], fewer images of hookworms were used, limiting the ability further clinically validate the findings [ 25 ]. Since 2015, some studies have reported the effectiveness of the deep learning-based analysis of CE images for identifying intestinal lesions such as angioectasia, ulceration, erosion, polyps, hemorrhages, and protruding masses [ 9 , 11 ]. However, there have been no clinical studies or reports on intestinal parasites such as hookworms, roundworms, and tapeworms.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…A previous work reported the automatic detection of hookworms based on an edge extraction network and classification network in 2016; however, although the preliminary results confirmed the ability of CNN to detect hookworms, consistent with other literature findings [ 2 ], fewer images of hookworms were used, limiting the ability further clinically validate the findings [ 25 ]. Since 2015, some studies have reported the effectiveness of the deep learning-based analysis of CE images for identifying intestinal lesions such as angioectasia, ulceration, erosion, polyps, hemorrhages, and protruding masses [ 9 , 11 ]. However, there have been no clinical studies or reports on intestinal parasites such as hookworms, roundworms, and tapeworms.…”
Section: Discussionmentioning
confidence: 99%
“…In order to reduce the burden on physicians and improve the efficiency and accuracy of endoscopic diagnosis, computer software technology has begun to be applied to this field. With the continuous development of the combination of computer software technology and endoscopic diagnosis [ 5 ], many computer-aided methods have been formed, and such methods are promising for the detection of many small intestinal abnormalities [ 6 8 ], such as bleeding [ 9 ], erosions [ 1 ], ulcerations [ 1 ], angioectasias [ 10 ], and protruding lesions [ 11 ], such as polyps, nodules, epithelial tumors, stromal tumors, and venous structures.…”
Section: Introductionmentioning
confidence: 99%
“…35 To overcome these limitations, an increasing number of studies have already addressed the potential of AI in the field of CE. Recent advances in deep learning algorithms in CE have been summarized in a 2021 systematic review with meta-analysis by Mohan et al, 36 As regards the specific IBD focus of our systematic review, a full application of an AI system based on a hybrid adaptive filtering and differential lacunarity (HFA DLac) architecture was explored in 2016 by Charisis and Hadjileontiadis for describing and detecting CD-associated lesions in CE. 37 The system was trained with a CE image database containing 400 images depicting CD-related lesions with the lowest possible similarity and 400 lesion-free frames which were judged by two independent endoscopists.…”
Section: Capsule Endoscopymentioning
confidence: 99%
“…Enlargement of application ranges: ANNs have been used for GI diagnosis, prognosis and risk prediction, and therapeutic guidance, achieving relatively high accuracies. Significance is limited to constructing more models that have been proposed in the same fields[ 184 , 185 ]. Future studies should focus on expanding ANN application ranges.…”
Section: Features Limitations and Future Perspectivesmentioning
confidence: 99%