Cancers, Vol. 16, Pages 208: Artificial Intelligence and Panendoscopy & mdash;Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy
Cancers, Vol. 16, Pages 208: Artificial Intelligence and Panendoscopy—Automatic Detection of Clinically Relevant Lesions in Multibrand Device-Assisted Enteroscopy
Cancers doi: 10.3390/cancers16010208
Authors:
Francisco Mendes
Miguel Mascarenhas
Tiago Ribeiro
João Afonso
Pedro Cardoso
Miguel Martins
Hélder Cardoso
Patrícia Andrade
João P. S. Ferreira
Miguel Mascarenhas Saraiva
Guilherme Macedo
Device-assisted enteroscopy (DAE) is capable of evaluating the entire gastrointestinal tract, identifying multiple lesions. Nevertheless, DAE’s diagnostic yield is suboptimal. Convolutional neural networks (CNN) are multi-layer architecture artificial intelligence models suitable for image analysis, but there is a lack of studies about their application in DAE. Our group aimed to develop a multidevice CNN for panendoscopic detection of clinically relevant lesions during DAE. In total, 338 exams performed in two specialized centers were retrospectively evaluated, with 152 single-balloon enteroscopies (Fujifilm®, Porto, Portugal), 172 double-balloon enteroscopies (Olympus®, Porto, Portugal) and 14 motorized spiral enteroscopies (Olympus®, Porto, Portugal); then, 40,655 images were divided in a training dataset (90% of the images, n = 36,599) and testing dataset (10% of the images, n = 4066) used to evaluate the model. The CNN’s output was compared to an expert consensus classification....
Source: Cancers - Category: Cancer & Oncology Authors: Francisco Mendes Miguel Mascarenhas Tiago Ribeiro Jo ão Afonso Pedro Cardoso Miguel Martins H élder Cardoso Patr ícia Andrade Jo ão P. S. Ferreira Miguel Mascarenhas Saraiva Guilherme Macedo Tags: Article Source Type: research
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