Small Bowel Capsule Endoscopy and artificial intelligence: First or second reader?

被引：11
作者：
Piccirelli, Stefania [1 ,2 ]
Milluzzo, Sebastian Manuel [1 ,2 ]
Bizzotto, Alessandra [1 ]
Cesaro, Paola [1 ]
Pecere, Silvia [3 ]
Spada, Cristiano [1 ,2 ]
机构：
[1] Fdn Poliambulanza Ist Osped, Endoscopia Digest & Gastroenterol, Brescia, Italy
[2] Univ Cattolica Sacro Cuore, Rome, Italy
[3] Fdn Policlin Univ A Gemelli IRCCS, Rome, Italy
来源：
BEST PRACTICE & RESEARCH CLINICAL GASTROENTEROLOGY | 2021年 / 52-53卷
关键词：
Deep-learning; Artificial intelligence; Capsule endoscopy; Automatic reading; CNN-based algorithm; CELIAC-DISEASE; AUTOMATIC DETECTION; DIAGNOSIS; LESIONS; SYSTEM;
D O I：
10.1016/j.bpg.2021.101742
中图分类号：
R57 [消化系及腹部疾病];
学科分类号：
摘要：
Several machine learning algorithms have been developed in the past years with the aim to improve SBCE (Small Bowel Capsule Endoscopy) feasibility ensuring at the same time a high diagnostic accuracy. If past algorithms were affected by low performances and unsatisfactory accuracy, deep learning systems raised up the expectancy of effective AI (Artificial Intelligence) application in SBCE reading. Automatic detection and characterization of lesions, such as angioectasias, erosions and ulcers, would significantly shorten reading time other than improve reader attention during SBCE review in routine activity. It is debated whether AI can be used as first or second reader. This issue should be further investigated measuring accuracy and cost-effectiveness of AI systems. Currently, AI has been mostly evaluated as first reader. However, second reading may play an important role in SBCE training as well as for better characterizing lesions for which the first reader was uncertain. (c) 2021 Elsevier Ltd. All rights reserved.
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页数：7
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