Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification.
Common pediatric distal forearm fractures necessitate precise detection. To support prompt treatment planning by clinicians, our study aimed to create a multi-class convolutional neural network (CNN) model for pediatric distal forearm fractures, guided by the AO Foundation/Orthopaedic Trauma Associa...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 725 - 734 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177626016&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626016 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626016 177626016 177626016 10.1007/s10278-024-00968-4 177626016 ppf: 725 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification. aug: au: Binh, Le Nguyen Nhu, Nguyen Thanh Vy, Vu Pham Thao Son, Do Le Hoang Hung, Truong Nguyen Khanh Bach, Nguyen Huy, Hoang Quoc Tuan, Le Van Le, Nguyen Quoc Khanh Kang, Jiunn-Horng affil: https://ror.org/05031qk94 International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, 11031, Taipei, Taiwan sug: subj: Radius Fractures, Distal Diagnosis Image Processing, Computer Assisted Deep Learning Prediction Models Evaluation Radius Fractures, Distal Classification Neural Networks (Computer) Radius Fractures, Distal Radiography Human Child Wrist Radiography Predictive Value of Tests Sensitivity and Specificity ROC Curve Descriptive Statistics Funding Source Child: 6-12 years ab: Common pediatric distal forearm fractures necessitate precise detection. To support prompt treatment planning by clinicians, our study aimed to create a multi-class convolutional neural network (CNN) model for pediatric distal forearm fractures, guided by the AO Foundation/Orthopaedic Trauma Association (AO/ATO) classification system for pediatric fractures. The GRAZPEDWRI-DX dataset (2008–2018) of wrist X-ray images was used. We labeled images into four fracture classes (FRM, FUM, FRE, and FUE with F, fracture; R, radius; U, ulna; M, metaphysis; and E, epiphysis) based on the pediatric AO/ATO classification. We performed multi-class classification by training a YOLOv4-based CNN object detection model with 7006 images from 1809 patients (80% for training and 20% for validation). An 88-image test set from 34 patients was used to evaluate the model performance, which was then compared to the diagnosis performances of two readers—an orthopedist and a radiologist. The overall mean average precision levels on the validation set in four classes of the model were 0.97, 0.92, 0.95, and 0.94, respectively. On the test set, the model's performance included sensitivities of 0.86, 0.71, 0.88, and 0.89; specificities of 0.88, 0.94, 0.97, and 0.98; and area under the curve (AUC) values of 0.87, 0.83, 0.93, and 0.94, respectively. The best performance among the three readers belonged to the radiologist, with a mean AUC of 0.922, followed by our model (0.892) and the orthopedist (0.830). Therefore, using the AO/OTA concept, our multi-class fracture detection model excelled in identifying pediatric distal forearm fractures. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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