Detecting gastric cancer from video images using convolutional neural networks

Ishioka, Mitsuaki; Hirasawa, Toshiaki; Tada, Takeshi

doi:10.1111/den.13306

Cited by 70 publications

(42 citation statements)

References 6 publications

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“…27 Promising results for real-time diagnosis have been seen with detection of 64 of the 68 (94%) of cancerous lesions in 62 patients using this CNN on video-based images. 12 Diagnosis with magnification NBI using support vector machine algorithms on only 126 training images demonstrated excellent diagnostic performance with sensitivity 97% and specificity 95%; however, limited performance was seen for delineation of the lesion with border (sensitivity 66%, and specificity 81%). 28 Depth of invasion using a feed-forward ANN demonstrated overall accuracy 64.7% and per depth of T1 at 77%, T2 49%, T3 51% and T4 55%.…”

Section: Gastric Cancermentioning

confidence: 96%

“…10,11 Real-time diagnosis of early gastric cancer has been performed with encouraging results. 12 The majority of imaging modalities have been evaluated including, recently, volume laser endomicroscopy (VLE). 13 The first RCT revealed significantly fewer blind spots identified with CAD assisted WLE (WISENSE) versus conventional EGD examination (5.86% vs. 22.46%, P < 0.001).…”

Section: Cad In Upper Gastrointestinal (Gi) Endoscopymentioning

confidence: 99%

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The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

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The latest state of the art technological innovations have led to a palpable progression in endoscopic imaging and may facilitate standardisation of practice. One of the most rapidly evolving modalities is artificial intelligence with recent studies providing real-time diagnoses and encouraging results in the first randomised trials to conventional endoscopic imaging. Advances in functional hypoxia imaging offer novel opportunities to be used to detect neoplasia and the assessment of colitis. Three-dimensional volumetric imaging provides spatial information and has shown promise in the increased detection of small polyps. Studies to date of self-propelling colonoscopes demonstrate an increased caecal intubation rate and possibly offer patients a more comfortable procedure. Further development in robotic technology has introduced ex vivo automated locomotor upper gastrointestinal and small bowel capsule devices. Eye-tracking has the potential to revolutionise endoscopic training through the identification of differences in experts and non-expert endoscopist as trainable parameters. In this review, we discuss the latest innovations of all these technologies and provide perspective into the exciting future of diagnostic luminal endoscopy.

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Section: Gastric Cancermentioning

confidence: 96%

Section: Cad In Upper Gastrointestinal (Gi) Endoscopymentioning

confidence: 99%

The future of endoscopy: Advances in endoscopic image innovations

Gulati

Patel

Emmanuel

et al. 2019

Digestive Endoscopy

View full text Add to dashboard Cite

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“…Computer‐aided diagnosis (CAD) is rapidly developing to help endoscopists improve detection and diagnosis of gastrointestinal disease . CAD is also currently used for endoscopic diagnosis of H. pylori infection.…”

Section: Development Of Computer‐aided Diagnosismentioning

confidence: 99%

“…C OMPUTER -AIDED DIAGNOSIS (CAD) is rapidly developing to help endoscopists improve detection and diagnosis of gastrointestinal disease. [58][59][60][61] CAD is also currently used for endoscopic diagnosis of H. pylori infection. Recent advances in the development of deep learning (DL) algorithm, which autonomously extracts and learns discriminative features of the image, and convolutional neural networks (CNN) can analyze complex features of images including shapes, colors, and textures.…”

Section: Development Of Computer-aided Diagnosismentioning

confidence: 99%

Can image‐enhanced endoscopy improve the diagnosis of Kyoto classification of gastritis in the clinical setting?

Dohi

Majima

Naito

et al. 2019

Digestive Endoscopy

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Endoscopic diagnosis of Helicobacter pylori (H. pylori) infection, the most common cause of gastric cancer, is very important to clarify high-risk patients of gastric cancer for reducing morbidity and mortality of gastric cancer. Recently, the Kyoto classification of gastritis was developed based on the endoscopic characteristics of H. pylori infection-associated gastritis for clarifying H. pylori infection status and evaluating risk factors of gastric cancer. Recently, magnifying endoscopy with narrow-band imaging (NBI) has reported benefits of the accuracy and reproducibility of endoscopic diagnosis for H. pylori-related premalignant lesions. In addition to NBI, various types of image-enhanced endoscopies (IEEs) are available including autofluorescence imaging, blue laser imaging, and linked color imaging. This review focuses on understanding the clinical applications and the corresponding evidences shown to improve the diagnosis of gastritis based on Kyoto classification using currently available advanced technologies of IEEs.

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“…The study by Ishioka et al trained AI to diagnose early gastric cancer using still images; a study using videos of 68 cases of early gastric cancer was also conducted [9]. The AI system detected 64 early gastric cancers out of 68 (94.1%) from the videos, which were at the same level as reported in the still images.…”

Section: Gastric Cancer Diagnosismentioning

confidence: 94%