Endoscopic detection and differentiation of esophageal lesions using a deep neural network

Ohmori, Masayasu; Ishihara, Ryu; Aoyama, Kazuharu; Nakagawa, Kentaro; Iwagami, Hiroyoshi; Matsuura, Noriko; Shichijo, Satoki; Yamamoto, Katsumi; Nagaike, Koji; Nakahara, Masanori; Inoue, Takuya; Aoi, Kenji; Okada, Hiroyuki; Tada, Takeshi

doi:10.1016/j.gie.2019.09.034

Cited by 111 publications

(120 citation statements)

References 27 publications

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“…Later, Ohmori et al[ 73 ] evaluated both ME and non-ME images [including WLI and NBI/blue laser imaging (BLI)] using a CNN based on SSD to recognize SCC. The accuracy for ME, non-ME + WLI, and non-ME + NBI/BLI was 77%, 81%, and 77%, respectively, all with high SEN and moderate SPE.…”

Section: Morphology-based Cadmentioning

confidence: 99%

“…A processing speed over 30 images/s is necessary for dynamic video analysis[ 56 ]. Although Horie et al[ 56 ], Ohmori et al[ 73 ] and Nakagawa et al[ 76 ] reported that their systems could process one image in 0.02, 0.027, and 0.033 s, respectively, they have not tested the systems in real-time videos. After Cai et al[ 72 ] had validated the efficacy of their DNN-CAD model, they split the video into images and then assembled them, enabling the model to delineate early cancer in real time.…”

Section: Morphology-based Cadmentioning

confidence: 99%

“…To overcome this problem, various regularization methods have been developed, such as segmenting the image or using cross validation with 5 folds or even 10 folds to augment the datasets. Recently, the size of datasets has been greatly enlarged in several studies[ 56 , 73 , 79 ], the largest of which included over one million UGI images from six centers[ 78 ]. Therefore, further multicenter studies including large dataset with different kinds of images ( i.e., WLI, NBI, ME and non-ME) harvested by different endoscopic systems for SCC and AC are likely to produce results with robustness and external generalizability.…”

Section: Study Limitations and Future Perspectivesmentioning

confidence: 99%

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Artificial intelligence-assisted esophageal cancer management: Now and future

Zhang

Guo

Yuan

et al. 2020

WJG

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Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

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Section: Morphology-based Cadmentioning

confidence: 99%

Section: Morphology-based Cadmentioning

confidence: 99%

Section: Study Limitations and Future Perspectivesmentioning

confidence: 99%

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Artificial intelligence-assisted esophageal cancer management: Now and future

Zhang

Guo

Yuan

et al. 2020

WJG

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“…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. The results of Ohmori et al[ 27 ] showed that the sensitivity of NBI was higher than that of WLE, but the specificity was lower, and there was no significant difference between the overall performance of the system and endoscopic experts[ 27 ]. The results of Horie et al[ 28 ] were similar to those of Ohmori et al[ 27 ].…”

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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“…The Japanese group of Ohmori [ 71 ] developed a CAD system to detect and differentiate superficial esophageal SCC. The AI algorithm used a Single-Shot MultiBox Detector (SSD) containing 16 layers.…”

Section: Principal Applications Of Ai For Assessment Of Precanceromentioning

confidence: 99%

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future

Lazăr

Avram

et al. 2020

Medicina

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In the gastroenterology field, the impact of artificial intelligence was investigated for the purposes of diagnostics, risk stratification of patients, improvement in quality of endoscopic procedures and early detection of neoplastic diseases, implementation of the best treatment strategy, and optimization of patient prognosis. Computer-assisted diagnostic systems to evaluate upper endoscopy images have recently emerged as a supporting tool in endoscopy due to the risks of misdiagnosis related to standard endoscopy and different expertise levels of endoscopists, time-consuming procedures, lack of availability of advanced procedures, increasing workloads, and development of endoscopic mass screening programs. Recent research has tended toward computerized, automatic, and real-time detection of lesions, which are approaches that offer utility in daily practice. Despite promising results, certain studies might overexaggerate the diagnostic accuracy of artificial systems, and several limitations remain to be overcome in the future. Therefore, additional multicenter randomized trials and the development of existent database platforms are needed to certify clinical implementation. This paper presents an overview of the literature and the current knowledge of the usefulness of different types of machine learning systems in the assessment of premalignant and malignant esophageal lesions via conventional and advanced endoscopic procedures. This study makes a presentation of the artificial intelligence terminology and refers also to the most prominent recent research on computer-assisted diagnosis of neoplasia on Barrett’s esophagus and early esophageal squamous cell carcinoma, and prediction of invasion depth in esophageal neoplasms. Furthermore, this review highlights the main directions of future doctor–computer collaborations in which machines are expected to improve the quality of medical action and routine clinical workflow, thus reducing the burden on physicians.

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Endoscopic detection and differentiation of esophageal lesions using a deep neural network

Cited by 111 publications

References 27 publications

Artificial intelligence-assisted esophageal cancer management: Now and future

Artificial intelligence-assisted esophageal cancer management: Now and future

Artificial intelligence technique in detection of early esophageal cancer

The Impact of Artificial Intelligence in the Endoscopic Assessment of Premalignant and Malignant Esophageal Lesions: Present and Future

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