Artificial intelligence assisted recognition and diagnosis of Magnetically controlled capsule gastroscopy

To enhance the diagnostic efficiency and accuracy of gastric mucosal lesions in magnetically controlled capsule endoscopy (MCCE) images, a Jigsaw Guided Deep Feature Fusion (JG-DFF) artificial intelligence (AI)-assisted recognition model based on the ResNet-50 convolutional neural network (CNN) is presented in this paper. Based on a dataset consisting of 4,053 MCCE images retrospectively collected from Sun Yat-sen Memorial Hospital, we constructed a ResNet-50 AI recognition model, as well as a JG-DFF AI recognition model under the jigsaw guided deep feature fusion. Additionally, the diagnostic ability and run time of the JG-DFF model were compared against a digestive endoscopist using the same data. The results indicate that the JG-DFF model has the potential for clinical application in improving diagnostic efficiency and reducing false positive rates. The overall accuracy of the JG-DFF artificial intelligence assisted recognition model established in this study is 92.1%. Compared to the ResNet-50 model, recognition by the JG-DFF model was more consistent; Compared with the digestive endoscopist, observed specificity of the JG-DFF model in identifying positive lesions was similar (P>0.05). That said, the JG-DFF model was more sensitive in identifying certain classes (P<0.05), and the time required to identify a single image was significantly shorter.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>