Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064165%3A_____%2F25%3A10499894" target="_blank" >RIV/00064165:_____/25:10499894 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-981-96-3863-5_4" target="_blank" >https://doi.org/10.1007/978-981-96-3863-5_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-981-96-3863-5_4" target="_blank" >10.1007/978-981-96-3863-5_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study

Popis výsledku v původním jazyce

Fracture detection using radiography is crucial for effective patient management. Despite advances, missed fractures remain a significant issue. This study evaluates the diagnostic performance of a deep learning model versus radiologists in identifying fractures on musculoskeletal X-rays. For the purpose of our study, we collected a study sample (n(SAMPLE)) of 600 pediatric and adult radiographs, and retrospectively nSAMPLE the images by two ground truth readers, four radiologists in a multi-reader study with varying experience, and an AI model (Carebot AI Bones 1.2.2 Carebot s.r.o.). The ground truth was reached for 548 images (n(GT)), including 95 fracture cases (n(FRACTURE)) and 453 normal cases (n(NORMAL)). The results demonstrated that the AI system achieved a sensitivity (Se) of 0.884 (0.804-0.934) and a specificity (S-p) of 0.879 (0.845-0.906). In comparison, the radiologists&apos; sensitivity ranged from 0.695 (0.596-0.778) to 0.832 (0.744-0.894) and their specificity ranged from 0.962 (0.941-0.976) to 0.993 (0.981-0.998). The AI model outperformed radiologists in Se across various body parts, particularly in areas with higher fracture prevalence, while showing comparable Sp in some categories. This study highlights the potential of AI to enhance diagnostic accuracy in clinical practice.

Název v anglickém jazyce

Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study

Popis výsledku anglicky

Fracture detection using radiography is crucial for effective patient management. Despite advances, missed fractures remain a significant issue. This study evaluates the diagnostic performance of a deep learning model versus radiologists in identifying fractures on musculoskeletal X-rays. For the purpose of our study, we collected a study sample (n(SAMPLE)) of 600 pediatric and adult radiographs, and retrospectively nSAMPLE the images by two ground truth readers, four radiologists in a multi-reader study with varying experience, and an AI model (Carebot AI Bones 1.2.2 Carebot s.r.o.). The ground truth was reached for 548 images (n(GT)), including 95 fracture cases (n(FRACTURE)) and 453 normal cases (n(NORMAL)). The results demonstrated that the AI system achieved a sensitivity (Se) of 0.884 (0.804-0.934) and a specificity (S-p) of 0.879 (0.845-0.906). In comparison, the radiologists&apos; sensitivity ranged from 0.695 (0.596-0.778) to 0.832 (0.744-0.894) and their specificity ranged from 0.962 (0.941-0.976) to 0.993 (0.981-0.998). The AI model outperformed radiologists in Se across various body parts, particularly in areas with higher fracture prevalence, while showing comparable Sp in some categories. This study highlights the potential of AI to enhance diagnostic accuracy in clinical practice.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

30224 - Radiology, nuclear medicine and medical imaging

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 statě ve sborníku

Proceedings of 2024 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2024)

ISBN

978-981-9638-62-8

ISSN

1876-1100

e-ISSN

1876-1119

Počet stran výsledku

12

Strana od-do

32-43

Název nakladatele

Springer

Místo vydání

Singapore

Místo konání akce

Manchester

Datum konání akce

19. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001491664600004