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: Tags: Article Source Type: research