A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs.
Nat Commun
; 12(1): 1066, 2021 02 16.
Article
em En
| MEDLINE
| ID: mdl-33594071
ABSTRACT
Pelvic radiograph (PXR) is essential for detecting proximal femur and pelvis injuries in trauma patients, which is also the key component for trauma survey. None of the currently available algorithms can accurately detect all kinds of trauma-related radiographic findings on PXRs. Here, we show a universal algorithm can detect most types of trauma-related radiographic findings on PXRs. We develop a multiscale deep learning algorithm called PelviXNet trained with 5204 PXRs with weakly supervised point annotation. PelviXNet yields an area under the receiver operating characteristic curve (AUROC) of 0.973 (95% CI, 0.960-0.983) and an area under the precision-recall curve (AUPRC) of 0.963 (95% CI, 0.948-0.974) in the clinical population test set of 1888 PXRs. The accuracy, sensitivity, and specificity at the cutoff value are 0.924 (95% CI, 0.912-0.936), 0.908 (95% CI, 0.885-0.908), and 0.932 (95% CI, 0.919-0.946), respectively. PelviXNet demonstrates comparable performance with radiologists and orthopedics in detecting pelvic and hip fractures.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pelve
/
Médicos
/
Ferimentos e Lesões
/
Algoritmos
/
Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
Limite:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article