Enhancing Epstein-Barr virus detection in IBD patients with XAI and clinical data integration

Zheng Wang; Yiqian Chen; Yi Wu; Yang Xue; Kaibin Lin; Jianglin Zhang; Ying Xiao

doi:10.1016/j.compbiomed.2024.109465

Enhancing Epstein-Barr virus detection in IBD patients with XAI and clinical data integration

Comput Biol Med. 2024 Nov 22:184:109465. doi: 10.1016/j.compbiomed.2024.109465. Online ahead of print.

Authors

Zheng Wang¹, Yiqian Chen², Yi Wu¹, Yang Xue¹, Kaibin Lin¹, Jianglin Zhang³, Ying Xiao⁴

Affiliations

¹ School of Computer Science, Hunan First Normal University, Changsha, 410205, China.
² Gastroenterology Department of Xiangya Hospital, Central South University, Changsha, 410008, China; Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha, 410011, China.
³ Department of Dermatology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China; Candidate Branch of National Clinical Research Center for Skin Diseases, Shenzhen, 518020, Guangdong, China; Department of Geriatrics, Shenzhen People's Hospital, (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, 518020, Guangdong, China.
⁴ Gastroenterology Department of Xiangya Hospital, Central South University, Changsha, 410008, China; Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Changsha, 410011, China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China. Electronic address: xiaoying111392@sina.com.

PMID: 39579664
DOI: 10.1016/j.compbiomed.2024.109465

Abstract

Background: There is increasing evidence of a link between Epstein-Barr virus (EBV) and gastrointestinal cancers, particularly in immunocompromised patients and those on thiopurines. Traditional endoscopic techniques have limitations in diagnosing EBV, whereas standard methods for diagnosing EBV have practical challenges in regular clinical settings related to their invasiveness and high costs.

Methods: An explainable AI (XAI) model was developed to analyze 1598 endoscopic images from 287 patients with inflammatory bowel disease (IBD), with a focus on detecting EBV infection. Following the application of data augmentation and transfer learning techniques, the model accurately classified the presence of EBV, and its performance was quantified through receiver operating characteristic (ROC) curve analysis, including calculation of the area under the curve (AUC). Furthermore, a combination of the Grad-CAM method and clinical data analysis significantly improved the interpretability and diagnostic accuracy of the model.

Results: Our model significantly outperformed other models, including those developed with ResNet50, Vision Transformer (ViT), and MobileNet v2 architectures, in detecting EBV on endoscopic images, achieving an accuracy of 73.83 % and an F1-score of 73.70 %. The model showed good performance in distinguishing between EBV-positive and EBV-negative images according to the confusion matrix and ROC curve analysis, with metrics including a true negative rate of 79.76 %, a true positive rate of 67.32 %, and an AUC of 0.74. Additionally, the generated salience maps effectively identified key regions in the images, enhancing lesion detection in patients with EBV infection. Our correlational analysis revealed significant associations between EBV infection and clinical parameters such as age, illness duration, and total bilirubin, and suggested that EBV infection had a notably greater incidence in ulcerative colitis (UC) patients than in Crohn's disease (CD) patients.

Conclusion: Our study successfully created an XAI-assisted system that allows the accurate detection of EBV in endoscopic images and improves the diagnosis of EBV infection through the integration of clinical data.

Keywords: Artificial intelligence (AI); Endoscopic imaging; Epstein–Barr virus (EBV); Inflammatory bowel disease (IBD).