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DL models are a promising tool, as recent research shows that radiologists using DL models can detect rib fractures in clinical CT scans at higher sensitivity and in shorter time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A dataset of 50 PMCT scans (24% women; age: mean 61, range 19 \u2013 96\u00a0years) was retrospectively collected and annotated, and used to train a first instance of the model, nnDetPM. Another instance of the model, nnDetClin, was trained on data from another dataset, RibFrac, consisting of 660 clinical CT scans (36% women; age: mean 55, range 21 \u2013 94\u00a0years).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On the PM testing set, nnDetPM achieved an average sensitivity of 70.2% and an average precision (at 0.1 intersection over union) of 78.1%, whereas nnDetClin fell far behind at 19.8% average sensitivity and 25.5% average precision, indicating a substantial impact of the domain shift from clinical to PM CT data. Further inspection of the results showed that the main factors of this domain shift were the position of the arms and the presence of medical ware in the image acquisition area of the PMCT scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The performance of nnDetPM, with an average sensitivity of 70.2%, was notable and comparable to that of radiologists. However, more advanced techniques must be explored to decide if DL models can overcome the domain shift factors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00414-025-03669-x","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T01:51:55Z","timestamp":1764813115000},"page":"857-866","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic rib fracture detection on postmortem CT data using deep learning"],"prefix":"10.1007","volume":"140","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5195-2543","authenticated-orcid":false,"given":"Manel","family":"Lopez-Melia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Virginie","family":"Magnin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9151-1433","authenticated-orcid":false,"given":"Sami","family":"Schranz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0793-5821","authenticated-orcid":false,"given":"Vincent","family":"Andrearczyk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2362-0304","authenticated-orcid":false,"given":"Adrien","family":"Depeursinge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-6101","authenticated-orcid":false,"given":"St\u00e9phane","family":"Marchand-Maillet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9914-2310","authenticated-orcid":false,"given":"Silke","family":"Grabherr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"3669_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.forsciint.2024.112002","volume":"357","author":"L Ihn\u00e1t Rudinsk\u00e1","year":"2024","unstructured":"Ihn\u00e1t Rudinsk\u00e1 L, Delongov\u00e1 P, Vaculov\u00e1 J, Farka\u0161ov\u00e1 Iannaccone S, Tulinsk\u00fd L, Ihn\u00e1t P (2024) Pulmonary fat embolism in non-survivors after cardiopulmonary resuscitation. 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