Authors
Hernandez, A. N., Korhonen, T. K., Pesonen, E. K., Tetri, S., Pikkarainen, L., Pertuz, S., Arponen, O.
Abstract
Objective: To construct and validate a deep-learning (DL) model for the automatic quantification of temporalis muscle thickness (TMT) in CT head scans. Materials and methods: We developed and evaluated the performance of a DL-based method for the measurement of temporalis muscle thickness using publicly available CT head scans. Reference standard TMT was established using a previously published measurement protocol, applied on 198 CT head scans obtained from a publicly available database, originally collected from various radiology centers within one city in 2017. A DL landmark detection model was trained to measure the temporalis muscle thickness. The absolute error and correlation between DL-based and reference standard TMT measurements were calculated. Additionally, the ability of the DL-based measurements to stratify subjects into low TMT vs normal TMT groups was assessed using the metrics of specificity, sensitivity, and accuracy. Results: The median reference TMT value was 6.0 mm (95% CI: 5.4, 6.5); the median DL-based TMT value was 5.8 mm (95% CI: 5.6, 6.3). The mean absolute error for TMT was 0.7 mm (95% CI: 0.6, 0.9). The correlation coefficient between reference and DL-based TMT was 0.9 (95% CI: 0.8, 0.9). The DL-based measurements classified the patients into low and normal TMT groups with sensitivity of 84.2%, specificity of 85.0% and accuracy of 84.6%. Conclusions: Our DL-based pipeline allows for fully automated and reproducible quantification of temporal muscle thickness and patient stratification into low and normal TMT groups.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 02 Nov 2025.
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