Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning Techniques
This work addresses a bottleneck in medical diagnostics for forensic or clinical settings where practitioner shortages exist, though it appears incremental by applying existing machine learning techniques to a specific domain.
The study tackled the problem of automating rib fracture detection in postmortem CT images to reduce manual workload, achieving an F1 score of 0.73 with a neural network model and a precision of 0.60 for fracture images.
Imaging techniques is widely used for medical diagnostics. This leads in some cases to a real bottleneck when there is a lack of medical practitioners and the images have to be manually processed. In such a situation there is a need to reduce the amount of manual work by automating part of the analysis. In this article, we investigate the potential of a machine learning algorithm for medical image processing by computing a topological invariant classifier. First, we select retrospectively from our database of postmortem computed tomography images of rib fractures. The images are prepared by applying a rib unfolding tool that flattens the rib cage to form a two-dimensional projection. We compare the results of our analysis with two independent convolutional neural network models. In the case of the neural network model, we obtain an $F_1$ Score of 0.73. To access the performance of our classifier, we compute the relative proportion of images that were not shared between the two classes. We obtain a precision of 0.60 for the images with rib fractures.