Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)

Cai, Shi‐Lun; Li, Bing; Tan, Weimin; Niu, Xuejing; Yu, Hon-Ho; Yao, Ling; Zhou, Ping‐Hong; Zhong, Yun‐Shi

doi:10.1016/j.gie.2019.06.044

Cited by 116 publications

(118 citation statements)

References 21 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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“…46 Cai et al also developed a system to identify early esophageal squamous cell carcinoma. 47 Using 2,428 standard EGD white-light images from 746 patients, 1,332 showed abnormal endoscopic tissue. Endoscopic submucosal dissection was performed on all cases with abnormal visual findings to verify pathology, while 3 experienced endoscopists annotated the images for pathology based on NBI images and pathology results (2 classifying with 1 verifying).…”

Section: Esophageal Neoplasia Detectionmentioning

confidence: 99%

“…However, all groups demonstrated improvement in metrics when referring to the CNN results, demonstrating that the CNN may assist the endoscopists in diagnosis. 47…”

Section: Esophageal Neoplasia Detectionmentioning

confidence: 99%

Artificial Intelligence in Gastrointestinal Endoscopy

et al. 2020

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Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully. clin endosc 2020;53:132-141

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“…Although WLE has been proven to be unsuitable for screening early ESCC alone, considering its clinical popularity, some researchers still hope to introduce AI to improve the accuracy of WLE. Cai et al[ 26 ] built a CAD system based on DL and tested it with WLE images. The results showed that the accuracy of the system in the diagnosis of early EC was significantly higher than that of junior and mid-level endoscopists, and there was no significant difference between the system and senior endoscopists.…”

Section: Ai In Endoscopic Detection Of Early Ecmentioning

confidence: 99%

Artificial intelligence technique in detection of early esophageal cancer

Huang

Yang

Huang

et al. 2020

WJG

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Due to the rapid progression and poor prognosis of esophageal cancer (EC), the early detection and diagnosis of early EC are of great value for the prognosis improvement of patients. However, the endoscopic detection of early EC, especially Barrett's dysplasia or squamous epithelial dysplasia, is difficult. Therefore, the requirement for more efficient methods of detection and characterization of early EC has led to intensive research in the field of artificial intelligence (AI). Deep learning (DL) has brought about breakthroughs in processing images, videos, and other aspects, whereas convolutional neural networks (CNNs) have shone lights on detection of endoscopic images and videos. Many studies on CNNs in endoscopic analysis of early EC demonstrate excellent performance including sensitivity and specificity and progress gradually from in vitro image analysis for classification to real-time detection of early esophageal neoplasia. When AI technique comes to the pathological diagnosis, borderline lesions that are difficult to determine may become easier than before. In gene diagnosis, due to the lack of tissue specificity of gene diagnostic markers, they can only be used as supplementary measures at present. In predicting the risk of cancer, there is still a lack of prospective clinical research to confirm the accuracy of the risk stratification model.

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Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video)

Cited by 116 publications

References 21 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

Artificial Intelligence in Gastrointestinal Endoscopy

Artificial intelligence technique in detection of early esophageal cancer

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