A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video)

Tang, Dehua; Zhou, Jie; Wang, Lei; Ni, Muhan; Chen, Min; Hassan, Shahzeb; Luo, Renquan; Chen, Xi; He, Xinqi; Zhang, Lihui; Ding, Xiwei; Yu, Honggang; Xu, Guifang; Zou, Xiaoping

doi:10.3389/fonc.2021.622827

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…Tang et al. ( 16 ) developed a D-CNN model to predict gastric mucosal cancer using 3407 gastroscopic images from 666 gastric cancer patients as the training set and 228 gastroscopic images as the test set. The AUC of the AI model for distinguishing intramucosal cancer from advanced gastric cancer was found to be 0.942, with a sensitivity of 0.905 and a specificity of 0.853.Zhu et al.…”

Section: Discussionmentioning

confidence: 99%

Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm

et al. 2022

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ObjectiveConvolutional Neural Network(CNN) is increasingly being applied in the diagnosis of gastric cancer. However, the impact of proportion of internal data in the training set on test results has not been sufficiently studied. Here, we constructed an artificial intelligence (AI) system called EGC-YOLOV4 using the YOLO-v4 algorithm to explore the optimal ratio of training set with the power to diagnose early gastric cancer.DesignA total of 22,0918 gastroscopic images from Yixing People’s Hospital were collected. 7 training set models were established to identify 4 test sets. Respective sensitivity, specificity, Youden index, accuracy, and corresponding thresholds were tested, and ROC curves were plotted.Results1. The EGC-YOLOV4 system completes all tests at an average reading speed of about 15 ms/sheet; 2. The AUC values in training set 1 model were 0.8325, 0.8307, 0.8706, and 0.8279, in training set 2 model were 0.8674, 0.8635, 0.9056, and 0.9249, in training set 3 model were 0.8544, 0.8881, 0.9072, and 0.9237, in training set 4 model were 0.8271, 0.9020, 0.9102, and 0.9316, in training set 5 model were 0.8249, 0.8484, 0.8796, and 0.8931, in training set 6 model were 0.8235, 0.8539, 0.9002, and 0.9051, in training set 7 model were 0.7581, 0.8082, 0.8803, and 0.8763.ConclusionEGC-YOLOV4 can quickly and accurately identify the early gastric cancer lesions in gastroscopic images, and has good generalization.The proportion of positive and negative samples in the training set will affect the overall diagnostic performance of AI.In this study, the optimal ratio of positive samples to negative samples in the training set is 1:1~ 1:2.

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Section: Discussionmentioning

confidence: 99%

Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm

et al. 2022

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“…Tang et al reported that the CADx system showed an accuracy of 88.2%, a sensitivity of 90.5%, and a specificity of 85.3% for differentiating mucosal/submucosal invasion in WLI images. They also reported that the diagnostic performance of endoscopists improved by using a CADx system not only in novice endoscopists (accuracy 74.0% vs. 84.6%, p < 0.001; sensitivity 81.1% vs. 85.7%, p = 0.018; specificity 65.2% vs. 83.3%, p < 0.001) but also in expert endoscopists (accuracy 79.8% vs. 85.5%, p < 0.001; sensitivity 84.3% vs. 87.4%, p = 0.018; specificity 74.2% vs. 83.0%, p < 0.001) [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Ochiai

Ozawa

Shibata³

et al. 2022

Diagnostics

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Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist’s skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

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“…Machine learning approaches started to improve medical care and biomedical research in recent years. It has been used in fields such as radiology, telehealth, clinical care, and even stem cell biology [20,[31][32][33].…”

Section: Microscopic Preservation Of Cardiac Ecmmentioning

confidence: 99%

Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks

et al. 2022

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Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research.

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A Novel Model Based on Deep Convolutional Neural Network Improves Diagnostic Accuracy of Intramucosal Gastric Cancer (With Video)

Cited by 20 publications

References 30 publications

Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm

Multi-center verification of the influence of data ratio of training sets on test results of an AI system for detecting early gastric cancer based on the YOLO-v4 algorithm

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks

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