Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064190%3A_____%2F25%3A10001407" target="_blank" >RIV/00064190:_____/25:10001407 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1136/tsaco-2024-001700" target="_blank" >https://doi.org/10.1136/tsaco-2024-001700</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1136/tsaco-2024-001700" target="_blank" >10.1136/tsaco-2024-001700</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study

Popis výsledku v původním jazyce

Introduction Forward military field hospitals often operate in battle zone environments where access to specialized personnel, such as radiologists, is limited, complicating the accuracy of diagnostic imaging. Chest radiographs are crucial for assessing thoracic injuries and other conditions, but their interpretation frequently falls to non-radiologist personnel. This study evaluates the effectiveness of an artificial intelligence (AI)-assisted model in enhancing the diagnostic accuracy of chest radiographs in such resource-limited settings. Methods Nine board-certified military physicians from various non-radiology specialties interpreted 159 anonymized chest radiographs, both with and without the support of AI. The AI model, INSIGHT CXR, generated automated descriptions for 80 radiographs, whereas 79 were interpreted without AI support. A linear mixed-effects model was used to assess the difference in diagnostic accuracy between the two conditions. Secondary analyses examined the effects of radiograph type and physician specialty on diagnostic performance. Results AI support increased mean diagnostic accuracy by 9.4% (p&lt;0.001) from pretest to post-test, representing a 23.15% relative improvement. This improvement was consistent across both normal and abnormal findings, with no significant differences observed based on radiograph type or physician specialty. These findings suggest that AI tools can serve as effective support in field hospitals, improving diagnostic precision and decision-making in the absence of radiologists. Conclusions This study highlights the potential for AI-assisted radiograph interpretation to enhance diagnostic accuracy in military field hospitals. If AI tools are proven reliable, they could be integrated into the workflow of forward field hospitals, improving the quality of care for injured personnel. Immediate benefits may include faster diagnoses, increased personnel readiness, optimized performance, and cost savings, leading to better outcomes in combat operations. Level of evidence II. Diagnostic Test.

Název v anglickém jazyce

Artificial intelligence-assisted chest radiograph interpretation in Role 2 military field hospital settings: a controlled experimental study

Popis výsledku anglicky

Introduction Forward military field hospitals often operate in battle zone environments where access to specialized personnel, such as radiologists, is limited, complicating the accuracy of diagnostic imaging. Chest radiographs are crucial for assessing thoracic injuries and other conditions, but their interpretation frequently falls to non-radiologist personnel. This study evaluates the effectiveness of an artificial intelligence (AI)-assisted model in enhancing the diagnostic accuracy of chest radiographs in such resource-limited settings. Methods Nine board-certified military physicians from various non-radiology specialties interpreted 159 anonymized chest radiographs, both with and without the support of AI. The AI model, INSIGHT CXR, generated automated descriptions for 80 radiographs, whereas 79 were interpreted without AI support. A linear mixed-effects model was used to assess the difference in diagnostic accuracy between the two conditions. Secondary analyses examined the effects of radiograph type and physician specialty on diagnostic performance. Results AI support increased mean diagnostic accuracy by 9.4% (p&lt;0.001) from pretest to post-test, representing a 23.15% relative improvement. This improvement was consistent across both normal and abnormal findings, with no significant differences observed based on radiograph type or physician specialty. These findings suggest that AI tools can serve as effective support in field hospitals, improving diagnostic precision and decision-making in the absence of radiologists. Conclusions This study highlights the potential for AI-assisted radiograph interpretation to enhance diagnostic accuracy in military field hospitals. If AI tools are proven reliable, they could be integrated into the workflow of forward field hospitals, improving the quality of care for injured personnel. Immediate benefits may include faster diagnoses, increased personnel readiness, optimized performance, and cost savings, leading to better outcomes in combat operations. Level of evidence II. Diagnostic Test.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30212 - Surgery

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

TRAUMA SURGERY &amp; ACUTE CARE OPEN

ISSN

—

e-ISSN

2397-5776

Svazek periodika

10

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

nestránkováno

Kód UT WoS článku

001596265300001

EID výsledku v databázi Scopus

2-s2.0-105019375994