Modified YOLO-V5 model for identifying inflammatory bowel disease on CT enterography
10.13929/j.issn.1003-3289.2024.10.028
- VernacularTitle:改良YOLO-V5模型用于识别CT肠道造影所示炎性肠病
- Author:
Fujin WANG
1
;
Mingzhu MENG
;
Xin WANG
;
Ningning WEI
Author Information
1. 南京大学医学院附属盐城第一医院影像科,江苏盐城 224000
- Keywords:
intestinal diseases;
diagnosis;
artificial intelligence
- From:
Chinese Journal of Medical Imaging Technology
2024;40(10):1593-1598
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the value of modified YOLO-V5 model for identifying inflammatory bowel disease(IBD)displayed on CT enterography(CTE).Methods Totally 192 patients with IBD(103 cases of Crohn disease[CD subgroup]and 89 cases of ulcerative colitis[UC subgroup])and 103 patients with clinically suspected IBD but CTE showed no abnormality(no abnormality subgroup)were retrospectively collected as study group,while 5 patients with CD and 3 with UC were collected as test group.CTE images with diseased intestinal tubes present as thickened intestinal wall or no abnormality intestinal tubes were selected as data set(n=3 511).CTE in study group were divided into training set(n=3 160,including 1 063 from CD subgroup,931 from UC subgroup and 1 166 from no abnormality subgroup)and verification set(n=351,including 118 from CD subgroup,103 from UC subgroup and 130 from no abnormality subgroup)at the ratio of 9∶1,while 25 CET images(17 from 5 cases of CD and 8 from 3 cases of UC)in test group were used as test set.Diseased tubes of CD,UC and no abnormality tubes were labeled.Then 5 sub-models,including YOLO-V5n,YOLO-V5s,YOLO-V5m,YOLO-V5l and YOLO-V5x were constructed and trained with modified YOLO-V5,and their efficacy were verified in test set.Precision(Pr),recall(Rc)and mean average precision(mAP)were used to evaluate the efficacy of each sub-model for identifying IBD lesions displayed on CTE.Results The complexity of the above 5 sub-models increased successively.YOLO-V5l and YOLO-V5x sub-model had better diagnostic efficacy,the overall Pr,Rc,mAP_0.5 and mAP_0.5.0.95 of the former for identifying IBD lesions in training and validation sets was 0.97,0.93,0.96 and 0.91,while of the latter was 0.97,0.95,0.96 and 0.92,respectively.In test set,the efficacy of YOLO-V5n sub-model for identifying IBD lesions was low,with mAP_0.5∶0.95 of 0.66 and AUC of 0.82,whereas mAP_0.5∶0.95 of YOLO-V5x sub-model for identifying CD was as high as 0.92,and of YOLO-V5l sub-model for identifying UC was as high as 0.91.Conclusion YOLO-V5l and YOLO-V5x sub-models based on modified YOLO-V5 could effectively identify IBD lesions displayed on CTE.
