Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F23%3A43907330" target="_blank" >RIV/60076658:12310/23:43907330 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21220/23:00368941
Result on the web
<a href="https://www.jstage.jst.go.jp/article/tjem/260/3/260_2023.J041/_html/-char/en" target="_blank" >https://www.jstage.jst.go.jp/article/tjem/260/3/260_2023.J041/_html/-char/en</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1620/tjem.2023.J041" target="_blank" >10.1620/tjem.2023.J041</a>

Result language
angličtina
Original language name
Deep Learning-Based Diagnosis of Fatal Hypothermia Using Post-Mortem Computed Tomography
Original language description
In forensic medicine, fatal hypothermia diagnosis is not always easy because findings are not specific, especially if traumatized. Post-mortem computed tomography (PMCT) is a useful adjunct to the cause-ofdeath diagnosis and some qualitative image character analysis, such as diffuse hyperaeration with decreased vascularity or pulmonary emphysema, have also been utilized for fatal hypothermia. However, it is challenging for inexperienced forensic pathologists to recognize the subtle differences of fatal hypothermia in PMCT images. In this study, we developed a deep learning-based diagnosis system for fatal hypothermia and explored the possibility of being an alternative diagnostic for forensic pathologists. An in-house dataset of forensic autopsy proven samples was used for the development and performance evaluation of the deep learning system. We used the area under the receiver operating characteristic curve (AUC) of the system for evaluation, and a human-expert comparable AUC value of 0.905, sensitivity of 0.948, and specificity of 0.741 were achieved. The experimental results clearly demonstrated the usefulness and feasibility of the deep learning system for fatal hypothermia diagnosis.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20602 - Medical laboratory technology (including laboratory samples analysis; diagnostic technologies) (Biomaterials to be 2.9 [physical characteristics of living material as related to medical implants, devices, sensors])

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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