Improvement in Disease Diagnosis in Computed Tomography Images by Correlating Organ Volumes with Disease Occurrences in Humans.

Some diseases are known to cause or coincide with volume changes of certain structures in the body. Since these changes can be used to identify diseases, in this paper, we aimed to discover such new correlations. To this end, we trained a machine learning model based on the TotalSegmentator model on computed tomography (CT) image data, to segment 104 anatomical structures, while trying to improve the accuracy of the model. We then used the model to segment CT scans of decedents who had at least one of 18 diseases. After correlating the structure volumes with disease occurrences, a possible new correlation between chronic artery failure and iliac artery volume was found and others were confirmed. However, due to the limitations of the model and the underlying data, further research is required. [ABSTRACT FROM AUTHOR]