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

Abstract

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.

Intelligent Computer-Aided Diagnostic System for Magnifying Endoscopy Images of Superficial Esophageal Squamous Cell Carcinoma

“…A recent study on artificial intelligence for the classification of IPCL patterns in the endoscopic diagnosis of SESCCs analyzed 7046 magnifying endoscopy images from 17 patients (10 SESCC, 7 normal) using a convolutional neural network (CNN) [20]. The accuracy, sensitivity, and specificity for abnormal IPCL patterns were 93.7%, 89.3%, and 98%, respectively.…”

“…In Equation (20), F even (u) and F odd (u) represent the calculation results for the even and odd data, respectively. F(u) can be expressed as shown in Equation (21).…”

Role of artificial intelligence in the diagnosis of oesophageal neoplasia: 2020 an endoscopic odyssey

“…There is a linear progression from non-dysplastic BE, to low grade and high-grade dysplasia. Early neoplasia which is confined to the mucosa have significant eradication rates of > 80%[ 16 ].…”

“…With advances in endoscopic therapy in recent years ESCN confined to the mucosal layer can be curatively resected endoscopically with a < 2% incidence of local lymph node metastasis. IPCL are the microvascular features which can be endoscopically used to help classify and identify ESCN and if there is a degree of invasion in the muscularis mucosa and submucosal tissue[ 16 ].…”

Towards Real-Time Detection of Squamous Pre-Cancers from Oesophageal Endoscopic Videos

“…It is therefore of clinical priority to have a computer assisted system to help clinicians to detect and highlight those potential suspected regions for further examinations. While there exists a number of promising results for computer-aided recognition of early neoplastic oesophageal lesions from endoscopic still images [9,10], there are less algorithms that are applicable to real-time endoscopy to allow computeraided decision-making during endoscopy at the point of examination. Furthermore, the existing studies focus mainly on the classification of endoscopic images between normal and abnormal stages with little work providing bounding boxes of the suspicious regions (detection) and delineating (segmentation).…”