Improvement in Disease Diagnosis in Computed Tomography Images by Correlating Organ Volumes with Disease Occurrences in Humans
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Le résumé fourni par la source
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.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Improvement in Disease Diagnosis in Computed Tomography Images by Correlating Organ Volumes with Disease Occurrences in Humans
- Date Crossref
- 05/07/2023
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Les institutions déclarées
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