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Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video).

Abstract

BACKGROUND AND AIMS

Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging.

METHODS

We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists.

RESULTS

The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%).

CONCLUSIONS

The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging.

Authors+Show Affiliations

Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai; Endoscopy Research Institute of Fudan University, Shanghai.Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai; Endoscopy Research Institute of Fudan University, Shanghai.School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai.School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai.Kiang Wu Hospital, Macau SAR, China.Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai; Endoscopy Research Institute of Fudan University, Shanghai.Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai; Endoscopy Research Institute of Fudan University, Shanghai.School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai.Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai; Endoscopy Research Institute of Fudan University, Shanghai.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31302091

Citation

Cai, Shi-Lun, et al. "Using a Deep Learning System in Endoscopy for Screening of Early Esophageal Squamous Cell Carcinoma (with Video)." Gastrointestinal Endoscopy, 2019.
Cai SL, Li B, Tan WM, et al. Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointest Endosc. 2019.
Cai, S. L., Li, B., Tan, W. M., Niu, X. J., Yu, H. H., Yao, L. Q., ... Zhong, Y. S. (2019). Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). Gastrointestinal Endoscopy, doi:10.1016/j.gie.2019.06.044.
Cai SL, et al. Using a Deep Learning System in Endoscopy for Screening of Early Esophageal Squamous Cell Carcinoma (with Video). Gastrointest Endosc. 2019 Jul 11; PubMed PMID: 31302091.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Using a deep learning system in endoscopy for screening of early esophageal squamous cell carcinoma (with video). AU - Cai,Shi-Lun, AU - Li,Bing, AU - Tan,Wei-Min, AU - Niu,Xue-Jing, AU - Yu,Hon-Ho, AU - Yao,Li-Qing, AU - Zhou,Ping-Hong, AU - Yan,Bo, AU - Zhong,Yun-Shi, Y1 - 2019/07/11/ PY - 2019/04/29/received PY - 2019/06/30/accepted PY - 2019/7/16/pubmed PY - 2019/7/16/medline PY - 2019/7/15/entrez JF - Gastrointestinal endoscopy JO - Gastrointest. Endosc. N2 - BACKGROUND AND AIMS: Few artificial intelligence-based technologies have been developed to improve the efficiency of screening for esophageal squamous cell carcinoma (ESCC). Here, we developed and validated a novel system of computer-aided detection (CAD) using a deep neural network (DNN) to localize and identify early ESCC under conventional endoscopic white-light imaging. METHODS: We collected 2428 (1332 abnormal, 1096 normal) esophagoscopic images from 746 patients to set up a novel DNN-CAD system in 2 centers and prepared a validation dataset containing 187 images from 52 patients. Sixteen endoscopists (senior, mid-level, and junior) were asked to review the images of the validation set. The diagnostic results, including accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared between the DNN-CAD system and endoscopists. RESULTS: The receiver operating characteristic curve for DNN-CAD showed that the area under the curve was >96%. For the validation dataset, DNN-CAD had a sensitivity, specificity, accuracy, PPV, and NPV of 97.8%, 85.4%, 91.4%, 86.4%, and 97.6%, respectively. The senior group achieved an average diagnostic accuracy of 88.8%, whereas the junior group had a lower value of 77.2%. After referring to the results of DNN-CAD, the average diagnostic ability of the endoscopists improved, especially in terms of sensitivity (74.2% vs 89.2%), accuracy (81.7% vs 91.1%), and NPV (79.3% vs 90.4%). CONCLUSIONS: The novel DNN-CAD system used for screening of early ESCC has high accuracy and sensitivity, and can help endoscopists to detect lesions previously ignored under white-light imaging. SN - 1097-6779 UR - https://www.unboundmedicine.com/medline/citation/31302091/Using_a_deep_learning_system_in_endoscopy_for_screening_of_early_esophageal_squamous_cell_carcinoma__with_video__ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0016-5107(19)32051-6 DB - PRIME DP - Unbound Medicine ER -