Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study
4
Zitationen
7
Autoren
2025
Jahr
Abstract
Our study demonstrated that a CNN model can effectively diagnose and classify metacarpal and phalangeal fractures using plain radiographs, achieving a mean weighted AUC of 0.84. 7 categories were deemed as acceptable, 9 categories as excellent, and 3 categories as outstanding. Our findings indicate that a CNN model may be used in the classification of hand fractures.
Ähnliche Arbeiten
RADIOGRAPHIC ATLAS OF SKELETAL DEVELOPMENT OF THE HAND AND WRIST
1959 · 5.536 Zit.
Development of an upper extremity outcome measure: The DASH (disabilities of the arm, shoulder, and head)
1996 · 4.938 Zit.
Rating Systems in the Evaluation of Knee Ligament Injuries
1985 · 4.538 Zit.
ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion—Part II: shoulder, elbow, wrist and hand
2004 · 4.409 Zit.
Isolated Hand Paresis: A Case Series
2013 · 4.070 Zit.