Morphometric analysis and tortuosity typing of the large intestine segments on computed tomography colonography with artificial intelligence.

Abstract

Background: Morphological properties such as length and tortuosity of the large intestine segments play important roles, especially in interventional procedures like colonoscopy.

Objective: Using computed tomography (CT) colonoscopy images, this study aimed to examine the morphological features of the colon's anatomical sections and investigate the relationship of these sections with each other or with age groups. The shapes of the transverse colon were analyzed using artificial intelligence.

Methods: The study was conducted as a two- and three-dimensional examination of CT colonography images of people between 40 and 80 years old, which were obtained retrospectively. An artificial intelligence algorithm (YOLOv8) was used for shape detection on 3D colon images.

Results: 160 people with a mean age of 89 men and 71 women included in the study were 57.79±8.55 and 56.55±6.60, respectively, and there was no statistically significant difference (p= 0.24). The total colon length was 166.11±25.07 cm for men and 158.73±21.92 cm for women, with no significant difference between groups (p=0.12). As a result of the training of the model Precision, Recall, and Mean Average Precision (mAP) were found to be 0.8578, 0.7940, and 0.9142, respectively.

Conclusion: The study highlights the importance of understanding the type and morphology of the large intestine for accurate interpretation of CT colonography results and effective clinical management of patients with suspected large intestine abnormalities. Furthermore, this study showed that 88.57% of the images in the test data set were detected correctly and that AI can play an important role in colon typing.