Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks

Ozawa, Tomohiro; Ishihara, Soichiro; Fujishiro, Mitsuhiro; Kumagai, Youichi; Shichijo, Satoki; Tada, Takeshi

doi:10.1177/1756284820910659

Cited by 100 publications

(76 citation statements)

References 37 publications

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“…All 4245 studies were screened and 106 full-length articles and/or abstracts were assessed. Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia.…”

Section: Resultsmentioning

confidence: 99%

“…Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia. Seven studies 5 11 12 14 19 20 25 used standard WLE, eight used NBI (magnifying and/ or non-magnifying) 2 6 13 15 18 22 23 26 and four 16 17 21 24 used a combination of standard WLE and/or NBI and/or chromo-endoscopy images ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

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Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

et al. 2020

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Background and study aims Recently, a growing body of evidence has been amassed on evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis of gastrointestinal lesions by means of convolutional neural networks (CNN). We conducted this meta-analysis to study pooled rates of performance for CNN-based AI in diagnosis of gastrointestinal neoplasia from endoscopic images. Methods Multiple databases were searched (from inception to November 2019) and studies that reported on the performance of AI by means of CNN in the diagnosis of gastrointestinal tumors were selected. A random effects model was used and pooled accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Pooled rates were categorized based on the gastrointestinal location of lesion (esophagus, stomach and colorectum). Results Nineteen studies were included in our final analysis. The pooled accuracy of CNN in esophageal neoplasia was 87.2 % (76–93.6) and NPV was 92.1 % (85.9–95.7); the accuracy in lesions of stomach was 85.8 % (79.8–90.3) and NPV was 92.1 % (85.9–95.7); and in colorectal neoplasia the accuracy was 89.9 % (82–94.7) and NPV was 94.3 % (86.4–97.7). Conclusions Based on our meta-analysis, CNN-based AI achieved high accuracy in diagnosis of lesions in esophagus, stomach, and colorectum.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

et al. 2020

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“…[ 17 ], and Ozawa et al . [ 18 ] have the data of diminutive CPs. We thought it was not appropriate to add theme to the general analysis for the potential risk of duplication of data.…”

Section: Methodsmentioning

confidence: 99%

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis

Ding

Wang

et al. 2021

PLoS ONE

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Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp (CP) significantly improve the prevention of colorectal cancer (CRC). The current effectiveness of the diagnostic performance of colonoscopy remains unsatisfactory with unstable accuracy. The convolutional neural networks (CNN) system based on artificial intelligence (AI) technology has demonstrated its potential to help endoscopists in increasing diagnostic accuracy. Nonetheless, several limitations of the CNN system and controversies exist on whether it provides a better diagnostic performance compared to human endoscopists. Therefore, this study sought to address this issue. Online databases (PubMed, Web of Science, Cochrane Library, and EMBASE) were used to search for studies conducted up to April 2020. Besides, the quality assessment of diagnostic accuracy scale-2 (QUADAS-2) was used to evaluate the quality of the enrolled studies. Moreover, publication bias was determined using the Deeks’ funnel plot. In total, 13 studies were enrolled for this meta-analysis (ranged between 2016 and 2020). Consequently, the CNN system had a satisfactory diagnostic performance in the field of CP detection (sensitivity: 0.848 [95% CI: 0.692–0.932]; specificity: 0.965 [95% CI: 0.946–0.977]; and AUC: 0.98 [95% CI: 0.96–0.99]) and CP classification (sensitivity: 0.943 [95% CI: 0.927–0.955]; specificity: 0.894 [95% CI: 0.631–0.977]; and AUC: 0.95 [95% CI: 0.93–0.97]). In comparison with human endoscopists, the CNN system was comparable to the expert but significantly better than the non-expert in the field of CP classification (CNN vs. expert: RDOR: 1.03, P = 0.9654; non-expert vs. expert: RDOR: 0.29, P = 0.0559; non-expert vs. CNN: 0.18, P = 0.0342). Therefore, the CNN system exhibited a satisfactory diagnostic performance for CP and could be used as a potential clinical diagnostic tool during colonoscopy.

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“…A Japanese study group has proposed simultaneous polyp detection and characterization by using the technologies they have developed: (i) an algorithm based on a deep‐learning algorithm for detecting polyps in white‐light images; and (ii) another algorithm that predicts a polyp's pathology and was designed for endocytoscopic images obtained by taking a photograph with the tip of the endoscope in contact with the polyp 61 . Ozawa et al 62 . reported similar systems using a Single Shot MultiBox Detector that can detect and characterize a target object simultaneously (Fig.…”

Section: Simultaneous Usage Of Cade and Cadxmentioning

confidence: 99%

Current status and future perspective on artificial intelligence for lower endoscopy

Misawa

Kudo

Mori

et al. 2020

Digestive Endoscopy

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The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesions. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for nonexperts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computeraided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision-making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

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Automated endoscopic detection and classification of colorectal polyps using convolutional neural networks

Cited by 100 publications

References 37 publications

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis

Current status and future perspective on artificial intelligence for lower endoscopy

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