# Use of Modified YOLOv5 Algorithm in the Differential Diagnosis of Colonic Crohn's Disease and Ulcerative Colitis on CTE Images

## TL;DR

This study explores using a modified YOLOv5 algorithm to help distinguish between two types of inflammatory bowel disease using CT scans.

## Contribution

The novel use of a modified YOLOv5 algorithm for differential diagnosis of colonic Crohn's disease and ulcerative colitis on CTE images.

## Key findings

- The YOLOv5x model achieved high mAP scores (0.97 mAP_0.5 and 0.83 mAP_0.5:0.95) in both validation and test sets.
- The model's performance was comparable to two radiologists (84.5% diagnostic accuracy).
- The modified YOLOv5 algorithm shows promise for early detection and differential diagnosis of IBD.

## Abstract

Background: Inflammatory bowel disease (IBD) is an immune-mediated disorder characterized by intestinal inflammation and includes two subtypes: Crohn's disease (CD) and ulcerative colitis (UC). The computed tomography manifestations of colonic CD (cCD) and UC are similar, and differential diagnosis is challenging. Our study aimed to investigate the feasibility of using a modified YOLOv5 algorithm for differentiating between cCD and UC on computed tomography enterography (CTE) images.

Methods: This multicenter retrospective study analyzed data from a total of 29 cCD patients and 29 UC patients. Five submodels (YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x) of YOLOv5 were trained and evaluated on the datasets. The CTE images of the cCD group and UC group were divided into a training set, validation set, and test set at a ratio of 8:1:1. Finally, the precision (Pr), recall rate (Rc), and mean average precision (mAP_0.5 and mAP_0.5:0.95) of the models were compared.

Results: The YOLOv5x model showed the best performance among the five submodels, with mAP_0.5 of 0.97 and mAP_0.5:0.95 of 0.97 and 0.84 in the validation set and mAP_0.5 and mAP_0.5:0.95 of 0.97 and 0.83 in the test set, respectively. These results demonstrated similar diagnostic accuracy to the two radiologists (84.5%).

Conclusion: The modified YOLOv5 algorithm is a feasible approach to distinguish between cCD and UC on CTE images. These findings may facilitate the early detection and differential diagnosis of IBD.