Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis

Soffer, Shelly; Klang, Eyal; Shimon, Orit; Nachmias, Noy; Eliakim, Rami; Ben‐Horin, Shomron; Kopylov, Uri; Barash, Yiftach

doi:10.1016/j.gie.2020.04.039

Cited by 178 publications

(105 citation statements)

References 61 publications

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“…Finally, in a systematic review and meta-analysis, Soffer et al analyzed 10 studies that provided sufficient data for a quantitative meta-analysis of the CNN technique. 34 The pooled sensitivity and specificity for ulcer detection were 0.95 and 0.94, respectively, and the pooled sensitivity and specificity for bleeding or the bleeding source were 0.98 and 0.99, respectively. However, there was high heterogeneity between studies and most studies had a high risk of bias.…”

Section: Application Of Artificial Intelligence In Capsule Endoscopymentioning

confidence: 91%

“…In addition, the CNN model significantly reduced the reading time compared with conventional reading by endoscopists (5.9 min vs. 96.6 min), thus showing the outstanding effectiveness of CNN models. Finally, in a systematic review and meta-analysis, Soffer et al analyzed 10 studies that provided sufficient data for a quantitative meta-analysis of the CNN technique [ 34 ]. The pooled sensitivity and specificity for ulcer detection were 0.95 and 0.94, respectively, and the pooled sensitivity and specificity for bleeding or the bleeding source were 0.98 and 0.99, respectively.…”

Section: Artificial Intelligence In Capsule Endoscopymentioning

confidence: 99%

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The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Yang

2020

Clin Endosc

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Section: Application Of Artificial Intelligence In Capsule Endoscopymentioning

confidence: 91%

Section: Artificial Intelligence In Capsule Endoscopymentioning

confidence: 99%

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Yang

2020

Clin Endosc

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“…AI can be a perfect assistant in analyzing this high‐volume data and highlight the suspicious regions. Soffer et al 42 . investigated 19 retrospective studies and showed DL has been widely adopted for wireless capsule endoscopy applications including detection of ulcers, polyps, celiac disease, bleeding, and hookworm with detection accuracy above 90% for most studies.…”

Section: Artificial Intelligence Image Analysis In Endoscopymentioning

confidence: 99%

Potentials of AI in medical image analysis in Gastroenterology and Hepatology

Chen

Sung

2021

J of Gastro and Hepatol

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With the advancement of artificial intelligence (AI) technology, it comes in a big wave carrying possibly huge impact in the field of medicine. Gastroenterology and hepatology, being a specialty relying much on diagnostic imaging, endoscopy, and histopathology, AI technology has promised improving the quality and consistency of care to the patients. In this review, we will elucidate the development of machine learning methods, especially the visual representation mechanism in deep learning on recognition tasks. Various AI‐image analysis applications in endoscopy, radiology, and pathology are covered in gastroenterology and hepatology and reveal the enormous potentials for AI in assisting diagnosis, prognosis, and treatment. We also discuss the promises as well as pitfalls for AI in medical image analysis and pointing out future research directions.

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“…Although clinical evidence of deep learning algorithms is still poor (there are few studies with low confidence), they show equivalent (or even better) performance than clinicians [9,10]. In this sense, some systematic reviews show the potential of deep learning in gastric tissue diseases [11] and wireless endoscopic capsule [12], but these studies also identify risk of bias due to gaps in the evaluation metrics and public availability of the dataset [11] that must be solved through prospective multicenter studies [12].…”

Section: Introductionmentioning

confidence: 99%

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

et al. 2020

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Colorectal cancer is one of the leading cancer death causes worldwide, but its early diagnosis highly improves the survival rates. The success of deep learning has also benefited this clinical field. When training a deep learning model, it is optimized based on the selected loss function. In this work, we consider two networks (U-Net and LinkNet) and two backbones (VGG-16 and Densnet121). We analyzed the influence of seven loss functions and used a principal component analysis (PCA) to determine whether the PCA-based decomposition allows for the defining of the coefficients of a non-redundant primal loss function that can outperform the individual loss functions and different linear combinations. The eigenloss is defined as a linear combination of the individual losses using the elements of the eigenvector as coefficients. Empirical results show that the proposed eigenloss improves the general performance of individual loss functions and outperforms other linear combinations when Linknet is used, showing potential for its application in polyp segmentation problems.

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Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis

Cited by 178 publications

References 61 publications

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements

Potentials of AI in medical image analysis in Gastroenterology and Hepatology

Eigenloss: Combined PCA-Based Loss Function for Polyp Segmentation

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