Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning: A Multi-Reader Retrospective Study
Original language description
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' 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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
30224 - Radiology, nuclear medicine and medical imaging
Result continuities
Project
—
Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
12
Pages from-to
32-43
Publisher name
Springer
Place of publication
Singapore
Event location
Manchester
Event date
Nov 19, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001491664600004