AI-based organ weight estimation from postmortem computed tomography

Abstract

Abstract Accurate organ weight determination is essential in forensic autopsy. Postmortem computed tomography (CT) combined with Artificial Intelligence (AI)-based segmentation allows automated, non-invasive organ delineation. This study evaluated the accuracy of CT-derived organ weights against conventional autopsy measurements. A total of 100 postmortem CT examinations (mean age 54 ± 21 years; 35 females, 65 males) were analyzed. Brain, heart, liver, kidneys, and spleen were segmented automatically in two model resolutions, and organ weights were derived from voxel volumes and Hounsfield unit-based tissue densities. Across all organs, mean differences between CT- and autopsy-derived organ weights were − 12.69 ± 107.96 g (median − 2.95 g) for the 1.5 mm model and − 18.42 ± 116.67 g (median − 1.95 g) for the 3 mm model. Mean absolute differences were 70.76 ± 83.07 g (median 29.05 g) and 77.43 ± 90.60 g (median 30.50 g), respectively. Agreement was highest for the brain and liver, followed by the kidneys and spleen, whereas the heart showed the largest deviations. Segmentation failures were uncommon and were mainly associated with decomposition, trauma, or incomplete organ coverage. These findings indicate that AI-based segmentation enables reliable, largely non-invasive organ weight estimation in postmortem CT and may support forensic investigations and personal identification workflows based on antemortem imaging.