Setting Standards to Promote Artificial Intelligence in Colon Mass Endoscopic Sampling
Zheng, Y.; Su, R.; Wang, W.; Meng, S.; Xiao, H.; Zhang, W.; Xu, H.; Bu, Y.; Zhong, Y.; Zhang, Y.; Qiu, H.; Qin, W.; Zhang, Y.; Xu, W.; Chen, H.; Zhang, C.; Wu, S.; Han, Z.; Zheng, X.; Zhu, H.; Wu, S.; Pan, W.; He, Y.; Hu, Y.
Show abstract
ObjectiveArtificial intelligence (AI) has undeniable values in detection, characterization, and monitoring of tumors during cancer imaging. However, major AI explorations in digestive endoscopy have not been systematically planned, and more important, most AI productions are based on Single-center Studies (ScSs). ScSs result in data scarcity, redundancy as well as island effects, which leads to some limitations in applying it on endoscopy. We investigate the disadvantages of picture processing which may effect the AI detection, and make improvements in AI detection and image recognition accuracy. DesignCurrent investigation aggregates a total of 2,500 gastroenteroscopy samples from various hospitals in multiple regions and carries out deep learning. ResultsIt is found that factors inconducive to AI recognition are common such as: (a) the gastrointestinal tract is not cleaned up completely; (b) shooting angle (from left to right and the top of polyp are unexposed clearly), shooting distance (too close or too far to shoot causes the lump to be unclear), shooting light (insufficient light source or overexposed light source in mass) and unstable shooting lead to poor quality of pictures. ConclusionWe set standards for a multicenter cooperation involving three-level medical institutions from the provincial, municipal and county to improve the recognition accuracy as well as the diagnosis and treatment efficiency meanwhile.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 94%
- Classification of early and late stage Liver Hepatocellular Carcinoma patients from their genomics and epigenomics profiles. 94%
- Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images 94%
Similar papers in this journal
- Prognostic Analysis of Histopathological Images Using Pre-Trained Convolutional Neural Networks 93%
- A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization 93%
- Estimating body volumes and surface areas of animalsfrom cross-sections 91%
Similar papers in this journal
- Benchmarking Deep Learning-based Image Retrieval of Oral Tumor Histology 92%
- Effects of contrast-medium and vertebral measurement level on computed tomography-based body composition parameters of skeletal muscle and adipose tissue 91%
- The Impact of Fasting the Holy Month of Ramadan on Colorectal Cancer Patients and Two Tumor Biomarkers: A Tertiary-Care Hospital Experience 91%
Similar papers in this journal
- Demarcation line determination for diagnosis of gastric cancer disease range using unsupervised machine learning in magnifying narrow-band imaging 96%
- Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications in Kyrgyzstan 92%
- Detection, Isolation and Quantification of Myocardial Infarct with Four Different Histological Staining Techniques 91%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.