Colonoscopy is an effective tool for early screening of colorectal diseases.However,the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accurac...Colonoscopy is an effective tool for early screening of colorectal diseases.However,the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy.Here we constructed and evaluated a deep convolution neural network(CNN)model based on 117055 images from 16004 individuals,which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp,colitis,colorectal cancer(CRC)from normal.The proposed approach was further validated onmulti-center real-time colonoscopy videos and images,which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets.The diagnostic performance of the model was further compared to the skilled endoscopists and the novices.In addition,our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975.Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.展开更多
基金This study was funded by the National Natural Science Foundation of China(Grant No.81902861 to F.Z.and 32000485 to X.H.)“Xinglin Scholars”Scientific Research Project Fund of Chengdu University of Traditional Chinese Medicine(Grant No.YYZX2019012 to F.Z.)+1 种基金the Scientific Research Fund of Technology Bureau in Dazhou(Grant No.17YYJC0004 to F.-W.Z.)the Key Research and Development Project Fund of Science and Technology Bureau in Dazhou,Sichuan Province(Grant No.20ZDYF0001 to F.-W.Z.).We express our deepest appreciation to J.Z,Y.C.for organizing the raw data and G.Y.for the revising the manuscript.
文摘Colonoscopy is an effective tool for early screening of colorectal diseases.However,the application of colonoscopy in distinguishing different intestinal diseases still faces great challenges of efficiency and accuracy.Here we constructed and evaluated a deep convolution neural network(CNN)model based on 117055 images from 16004 individuals,which achieved a high accuracy of 0.933 in the validation dataset in identifying patients with polyp,colitis,colorectal cancer(CRC)from normal.The proposed approach was further validated onmulti-center real-time colonoscopy videos and images,which achieved accurate diagnostic performance on detecting colorectal diseases with high accuracy and precision to generalize across external validation datasets.The diagnostic performance of the model was further compared to the skilled endoscopists and the novices.In addition,our model has potential in diagnosis of adenomatous polyp and hyperplastic polyp with an area under the receiver operating characteristic curve of 0.975.Our proposed CNN models have potential in assisting clinicians in making clinical decisions with efficiency during application.
