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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21220/23:00368941

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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])

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

TOHOKU JOURNAL OF EXPERIMENTAL MEDICINE

ISSN

0040-8727

e-ISSN

1349-3329

Svazek periodika

260

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

JP - Japonsko

Počet stran výsledku

9

Strana od-do

253-261

Kód UT WoS článku

001041173400002

EID výsledku v databázi Scopus

2-s2.0-85165220837