Usefulness of an artificial intelligence system for the detection of esophageal squamous cell carcinoma evaluated with videos simulating overlooking situation

2021 ◽  
Author(s):  
Kotaro Waki ◽  
Ryu Ishihara ◽  
Yusuke Kato ◽  
Ayaka Shoji ◽  
Takahiro Inoue ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Artificial Intelligence System ◽  
Intelligence System

scholarly journals Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos and the effects of real-time assistance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sho Shiroma ◽  
Toshiyuki Yoshio ◽  
Yusuke Kato ◽  
Yoshimasa Horie ◽  
Ken Namikawa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Real Time ◽  
Squamous Cell ◽  
Narrow Band Imaging ◽  
High Sensitivity ◽  
Detection Accuracy

AbstractDiagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCCs using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, respectively, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25–70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p < 0.05). AI can detect superficial ESCCs from EGD videos with high sensitivity and the sensitivity of the endoscopist was improved with AI real-time support.

scholarly journals Ability of artificial intelligence to detect T1 esophageal squamous cell carcinoma from endoscopic videos: supportive effects of real-time assistance

2020 ◽  
Author(s):  
Sho Shiroma ◽  
Toshiyuki Yoshio ◽  
Yusuke Kato ◽  
Yoshimasa Horie ◽  
Ken Namikawa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Squamous Cell Carcinoma ◽  
Deep Learning ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Real Time ◽  
Squamous Cell ◽  
Narrow Band Imaging ◽  
High Sensitivity ◽  
Detection Accuracy

Abstract Diagnosis using artificial intelligence (AI) with deep learning could be useful in endoscopic examinations. We investigated the ability of AI to detect superficial esophageal squamous cell carcinoma (ESCC) from esophagogastroduodenoscopy (EGD) videos. We retrospectively collected 8428 EGD images of esophageal cancer to develop a convolutional neural network through deep learning. We evaluated the detection accuracy of the AI diagnosing system compared with that of 18 endoscopists. We used 144 EGD videos for the two validation sets. First, we used 64 EGD observation videos of ESCC using both white light imaging (WLI) and narrow-band imaging (NBI). We then evaluated the system using 80 EGD videos from 40 patients (20 with superficial ESCC and 20 with non-ESCC). In the first set, the AI system correctly diagnosed 100% ESCCs. In the second set, it correctly detected 85% (17/20) ESCCs. Of these, 75% (15/20) and 55% (11/22) were detected by WLI and NBI, and the positive predictive value was 36.7%. The endoscopists correctly detected 45% (25-70%) ESCCs. With AI real-time assistance, the sensitivities of the endoscopists were significantly improved without AI assistance (p<0.05). AI can detect superficial ESCC from EGD videos with high sensitivity and improve endoscopists’ detection of ESCC with real-time support.

Sa1252 THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO DETECT ESOPHAGEAL SQUAMOUS CELL CARCINOMA IN MOVIES USING CONVOLUNTIONAL NEURAL NETWORKS

2019 ◽  
Vol 89 (6) ◽  
pp. AB189 ◽  
Author(s):  
Sho Shiroma ◽  
Toshiyuki Yoshio ◽  
Kazuharu Aoyama ◽  
Akiyoshi Ishiyama ◽  
Tomohiro Tsuchida ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell
Sign in / Sign up

Export Citation Format

Share Document