TY  - JOUR
IS  - 2
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10067591
Y1  - 2019/03//
A1  - Everson, M
A1  - Herrera, LCGP
A1  - Li, W
A1  - Luengo, IM
A1  - Ahmad, O
A1  - Banks, M
A1  - Magee, C
A1  - Alzoubaidi, D
A1  - Hsu, HM
A1  - Graham, D
A1  - Vercauteren, T
A1  - Lovat, L
A1  - Ourselin, S
A1  - Kashin, S
A1  - Wang, HP
A1  - Wang, WL
A1  - Haidry, RJ
SN  - 2050-6406
AV  - public
VL  - 7
KW  - Artificial intelligence
KW  -  computer-aided diagnosis
KW  -  endoscopy
KW  -  neural networks
KW  -  oesophageal cancer
KW  -  squamous cell cancer
N2  - BACKGROUND: 
Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate with invasion depth ? an important factor in the success of curative endoscopic therapy. IPCLs visualised on magnification endoscopy with Narrow Band Imaging (ME-NBI) can be used to train convolutional neural networks (CNNs) to detect the presence and classify staging of ESCN lesions.

METHODS:
A total of 7046 sequential high-definition ME-NBI images from 17 patients (10 ESCN, 7 normal) were used to train a CNN. IPCL patterns were classified by three expert endoscopists according to the Japanese Endoscopic Society classification. Normal IPCLs were defined as type A, abnormal as B1?3. Matched histology was obtained for all imaged areas.

RESULTS:
This CNN differentiates abnormal from normal IPCL patterns with 93.7% accuracy (86.2% to 98.3%) and sensitivity and specificity for classifying abnormal IPCL patterns of 89.3% (78.1% to 100%) and 98% (92% to 99.7%), respectively. Our CNN operates in real time with diagnostic prediction times between 26.17?ms and 37.48?ms.

CONCLUSION: 
Our novel and proof-of-concept application of computer-aided endoscopic diagnosis shows that a CNN can accurately classify IPCL patterns as normal or abnormal. This system could be used as an in vivo, real-time clinical decision support tool for endoscopists assessing and directing local therapy of ESCN.
EP  - 306
TI  - Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study
SP  - 297
JF  - United European Gastroenterology Journal
UR  - https://doi.org/10.1177%2F2050640618821800
ER  -