Asian Spine J.  2024 Jun;18(3):407-414. 10.31616/asj.2023.0259.

Artificial neural networks for the detection of odontoid fractures using the Konstanz Information Miner Analytics Platform

• Liawrungrueang W
• Cho ST
• Kotheeranurak V
• Pun A
• Jitpakdee K
• Sarasombath P

• KMID: 2556949
• DOI: http://doi.org/10.31616/asj.2023.0259

Abstract

Study Design: An experimental study. Purpose: This study aimed to investigate the potential use of artificial neural networks (ANNs) in the detection of odontoid fractures using the Konstanz Information Miner (KNIME) Analytics Platform that provides a technique for computer-assisted diagnosis using radiographic X-ray imaging. Overview of Literature: In medical image processing, computer-assisted diagnosis with ANNs from radiographic X-ray imaging is becoming increasingly popular. Odontoid fractures are a common fracture of the axis and account for 10%–15% of all cervical fractures. However, a literature review of computer-assisted diagnosis with ANNs has not been made.
Methods
This study analyzed 432 open-mouth (odontoid) radiographic views of cervical spine X-ray images obtained from dataset repositories, which were used in developing ANN models based on the convolutional neural network theory. All the images contained diagnostic information, including 216 radiographic images of individuals with normal odontoid processes and 216 images of patients with acute odontoid fractures. The model classified each image as either showing an odontoid fracture or not. Specifically, 70% of the images were training datasets used for model training, and 30% were used for testing. KNIME’s graphic user interface-based programming enabled class label annotation, data preprocessing, model training, and performance evaluation.
Results
The graphic user interface program by KNIME was used to report all radiographic X-ray imaging features. The ANN model performed 50 epochs of training. The performance indices in detecting odontoid fractures included sensitivity, specificity, F-measure, and prediction error of 100%, 95.4%, 97.77%, and 2.3%, respectively. The model’s accuracy accounted for 97% of the area under the receiver operating characteristic curve for the diagnosis of odontoid fractures.
Conclusions
The ANN models with the KNIME Analytics Platform were successfully used in the computer-assisted diagnosis of odontoid fractures using radiographic X-ray images. This approach can help radiologists in the screening, detection, and diagnosis of acute odontoid fractures.

Keyword

Odontoid processes; Machine intelligence; Computational neural networks; Computer-assisted diagnosis