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Binh LN, Nhu NT, Vy VPT, Son DLH, Hung TNK, Bach N, Huy HQ, Tuan LV, Le NQK, Kang JH. Multi-Class Deep Learning Model for Detecting Pediatric Distal Forearm Fractures Based on the AO/OTA Classification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:725-733. [PMID: 38308069 PMCID: PMC11031555 DOI: 10.1007/s10278-024-00968-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/19/2023] [Accepted: 01/11/2024] [Indexed: 02/04/2024]
Abstract
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.
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Affiliation(s)
- Le Nguyen Binh
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan
- Department of Orthopedics and Trauma, Cho Ray Hospital, Ho Chi Minh City, Vietnam
- AIBioMed Research Group, Taipei Medical University, Taipei, 11031, Taiwan
| | - Nguyen Thanh Nhu
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan
- Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho 94117, Can Tho, Vietnam
| | - Vu Pham Thao Vy
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan
| | - Do Le Hoang Son
- Department of Orthopedics and Trauma, Cho Ray Hospital, Ho Chi Minh City, Vietnam
| | | | - Nguyen Bach
- Department of Orthopedics, University Medical Center Ho Chi Minh City, 201 Nguyen Chi Thanh Street, District 5, Ho Chi Minh City, Vietnam
| | - Hoang Quoc Huy
- Department of Orthopedics, University Medical Center Ho Chi Minh City, 201 Nguyen Chi Thanh Street, District 5, Ho Chi Minh City, Vietnam
| | - Le Van Tuan
- Department of Orthopedics and Trauma, Cho Ray Hospital, Ho Chi Minh City, Vietnam
| | - Nguyen Quoc Khanh Le
- AIBioMed Research Group, Taipei Medical University, Taipei, 11031, Taiwan.
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan.
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 11031, Taiwan.
| | - Jiunn-Horng Kang
- International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan.
- Department of Physical Medicine and Rehabilitation, School of Medicine, College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan.
- Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, 11031, Taiwan.
- Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Xinyi District, No.250, Wuxing Street, Taipei, 11031, Taiwan.
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