Current status and future perspective of artificial intelligence applications in endoscopic diagnosis and management of gastric cancer

Hirasawa, Toshiaki; Ikenoyama, Yohei; Ishioka, Mitsuaki; Namikawa, Ken; Horiuchi, Yusuke; Nakashima, Hirotaka; Fujisaki, Junko

doi:10.1111/den.13890

Cited by 28 publications

(19 citation statements)

References 48 publications

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“…These techniques are now being applied to gastrointestinal endoscopy worldwide. AI has high diagnostic accuracy for esophageal, gastric, and colorectal cancers 16–18 . However, AI has been mostly used to identify irregular or malignant lesions.…”

Section: Discussionmentioning

confidence: 99%

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Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis

Watanabe

Oikawa

Agawa

et al. 2021

Clinical Laboratory Analysis

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Background and Aim Gastrointestinal endoscopy and biopsy‐based pathological findings are needed to diagnose early gastric cancer. However, the information of biopsy specimen is limited because of the topical procedure; therefore, pathology doctors sometimes diagnose as gastric indefinite for dysplasia (GIN). Methods We compared the accuracy of physician‐performed endoscopy (trainee, n = 3; specialists, n = 3), artificial intelligence (AI)‐based endoscopy, and/or molecular markers (DNA methylation: BARHL2, MINT31, TET1, miR‐148a, miR‐124a‐3, NKX6‐1; mutations: TP53; and microsatellite instability) in diagnosing GIN lesions. We enrolled 24,388 patients who underwent endoscopy, and 71 patients were diagnosed with GIN lesions. Thirty‐two cases of endoscopic submucosal dissection (ESD) in 71 GIN lesions and 32 endoscopically resected tissues were assessed by endoscopists, AI, and molecular markers to identify benign or malignant lesions. Results The board‐certified endoscopic physicians group showed the highest accuracy in the receiver operative characteristic curve (area under the curve [AUC]: 0.931), followed by a combination of AI and miR148a DNA methylation (AUC: 0.825), and finally trainee endoscopists (AUC: 0.588). Conclusion AI with miR148s DNA methylation‐based diagnosis is a potential modality for diagnosing GIN.

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Section: Discussionmentioning

confidence: 99%

“…AI has high diagnostic accuracy for esophageal, gastric, and colorectal cancers. 16 , 17 , 18 However, AI has been mostly used to identify irregular or malignant lesions. Qualitative investigations for a comprehensive diagnosis to facilitate appropriate therapy remain limited.…”

Section: Discussionmentioning

confidence: 99%

Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis

Watanabe

Oikawa

Agawa

et al. 2021

Clinical Laboratory Analysis

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“…In Japan, as mentioned earlier, Hirasawa et al [12] also reported a retrospective RCT, in which the CNN system showed ability to process endoscopic images with a decent diagnostic ability. As an attempt to compare the diagnostic ability of the system, the same group of authors conducted a test to compare it with expert endoscopists [32].…”

Section: Detectionmentioning

confidence: 99%

Application of Artificial Intelligence in Early Gastric Cancer Diagnosis

Xiao

et al. 2021

Digestion

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Background: With the development of new technologies such as magnifying endoscopy with narrow band imaging, endoscopists achieved better accuracy for diagnosis of gastric cancer (GC) in various aspects. However, to master such skill takes substantial effort and could be difficult for inexperienced doctors. Therefore, a novel diagnostic method based on artificial intelligence (AI) was developed and its effectiveness was confirmed in many studies. AI system using convolutional neural network has showed marvelous results in the ongoing trials of computer-aided detection of colorectal polyps. Summary: With AI’s efficient computational power and learning capacities, endoscopists could improve their diagnostic accuracy and avoid the overlooking or over-diagnosis of gastric neoplasm. Several systems have been reported to achieved decent accuracy. Thus, AI-assisted endoscopy showed great potential on more accurate and sensitive ways for early detection, differentiation, and invasion depth prediction of gastric lesions. However, the feasibility, effectiveness, and safety in daily practice remain to be tested. Key messages: This review summarizes the current status of different AI applications in early GC diagnosis. More randomized controlled trails will be needed before AI could be widely put into clinical practice.

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“…In the deep learning structure, if the parameters of the structure are too many and the training samples are too few, over fitting is a common problem in trained models [37]. The phenomenon of over fitting could appear in neural network training, which is shown in the following aspects [38]: the structure has small loss function and high prediction accuracy in the training data. However, contrary to the results obtained from the training data, the model has large loss function and low prediction accuracy.…”

Section: Dropoutmentioning

confidence: 99%

Alcoholic brain injury via 8-layer customized deep convolution neural network

Zhu

Brown

2021

STEMedicine

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Alcohol can act quickly in the human body and alter mood and behavior. If alcohol is consumed in excess, it will accumulate in the organs of the body, especially in the liver and brain. To a certain extent, the symptoms of alcoholism will appear. So far, the main method of diagnosis of alcoholic brain injury is through MRI images by radiologists. However, this is a very subjective diagnosis. Radiologists may be affected by external factors, such as physical discomfort, lack of rest, inattention, etc., resulting in diagnostic errors. In this paper, we proposed a novel 8-layer customized deep convolution neural network for alcoholic brain injury detection, which contains five convolution layers, five pooling layers, and three fully connected layers. There are three improvements in this paper, (i) Based on deep learning, we proposed a method for automatic diagnosis of alcoholic brain injury; (ii) We introduced Dropout to the proposed structure to improve robustness; (iii) Compared with other state-of-the-art approaches, the proposed structure is more efficient. The experimental results showed that the sensitivity, specificity, precision, accuracy, F1, MCC and FMI were 96.14±1.99, 96.20±1.47, 95.98±1.54, 96.17±1.55, 96.05±1.62, 93.34±3.11, 96.06±1.62 respectively. According to comparison results, our method performed the best. The proposed model is effective in detecting alcoholic brain injury based on MRI images.

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Current status and future perspective of artificial intelligence applications in endoscopic diagnosis and management of gastric cancer

Cited by 28 publications

References 48 publications

Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis

Combination of artificial intelligence‐based endoscopy and miR148a methylation for gastric indefinite dysplasia diagnosis

Application of Artificial Intelligence in Early Gastric Cancer Diagnosis

Alcoholic brain injury via 8-layer customized deep convolution neural network

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