Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network

Sakai, Yoshinori; Takemoto, Satoko; Hori, Keisuke; Nishimura, Masaomi; Ikematsu, Hiroaki; Yano, Tomonori; Yokota, Hideo

doi:10.1109/embc.2018.8513274

Cited by 97 publications

(69 citation statements)

References 12 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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“…Twenty-seven studies were dedicated to improving diagnostic accuracy in case of colorectal polyps or cancer. Nineteen studies focused on the diagnosis of premalignant or malignant lesions of the upper gastrointestinal tract, [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] only 4 studies were limited to the small bowel, [58][59][60][61] and 3 studies assessed the entire digestive tract. [62][63][64] Twenty-four studies used specific validation techniques-mainly k-fold cross- validation.…”

Section: Analysis Of Malignant and Premalignant Lesionsmentioning

confidence: 99%

Application of Artificial Intelligence to Gastroenterology and Hepatology

et al. 2020

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Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

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“…Machine learning models are being intensively developed for the diagnosis phase to deduce and detect cancer from various available sources of medical images. In particular, the following examples were highly successful: A better prediction of breast cancer with a deep learning model combined with a logistic regression model (area under the curve (AUC) = 0.70 vs. 0.62, respectively) in which the image data and assessment records of over 39,571 subjects were combined [12]; the detection of early stage stomach cancer using a convolutional neural network (CNN, detection rate = 82.8%) [13]; the deduction of the risk level of lung cancer using a CNN model (AUC = 0.94) [14]. Regarding the treatment phase, machine learning is most used in cancer therapeutics.…”

Section: Machine Learning In Healthcarementioning

confidence: 99%

Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond

Misawa

Fukuyoshi²,

Sengoku

2020

IJERPH

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There have been prior attempts to utilize machine learning to address issues in the medical field, particularly in diagnoses using medical images and developing therapeutic regimens. However, few cases have demonstrated the usefulness of machine learning for enhancing health consciousness of patients or the public in general, which is necessary to cause behavioral changes. This paper describes a novel case wherein the uptake rate for colorectal cancer examinations has significantly increased due to the application of machine learning and nudge theory. The paper also discusses the effectiveness of social impact bonds (SIBs) as a scheme for realizing these applications. During a healthcare SIB project conducted in the city of Hachioji, Tokyo, machine learning, based on historical data obtained from designated periodical health examinations, digitalized medical insurance receipts, and medical examination records for colorectal cancer, was used to deduce segments for whom the examination was recommended. The result revealed that out of the 12,162 people for whom the examination was recommended, 3264 (26.8%) received it, which exceeded the upper expectation limit of the initial plan (19.0%). We conclude that this was a successful case that stimulated discussion on potential further applications of this approach to wider regions and more diseases.

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Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network

Cited by 97 publications

References 12 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

Application of Artificial Intelligence to Gastroenterology and Hepatology

Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond

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