Artificial intelligence has the potential to assist in the interpretation of radiographs. The distal radius is one of the most commonly fractured bones, and accurate and timely diagnosis is required for proper treatment. This review aims to summarize the current literature on the use of artificial intelligence, particularly convolutional neural networks, in diagnosing distal radius fractures. The results indicate that artificial intelligence has shown increasingly improved performance with image interpretation over time, often on par with specialized physicians. However, these models still struggle with complex fractures.
DuronLDucarougeAGillibertA, et al. Assessment of an AI aid in detection of adult appendicular skeletal fractures by emergency physicians and radiologists: a multicenter cross-sectional diagnostic study. Radiology. 2021;300(1):120-129. doi:10.1148/radiol.2021203886
2.
OppenheimerJLukenSHammB, et al. A prospective approach to integration of AI fracture detection software in radiographs into clinical workflow. Life. 2023;13(1):223. doi:10.3390/life13010223
3.
MuthukrishnanNMalekiFOvensK, et al. Brief history of artificial intelligence. Neuroimaging Clin N Am. 2020;30(4):393-399. doi:10.1016/j.nic.2020.07.004
4.
BurnsJEYaoJSummersRM.Artificial Intelligence in musculoskeletal imaging: a paradigm shift. J Bone Miner Res. 2020;35(1):28-35. doi:10.1002/jbmr.3849
5.
GuermaziATannouryCKompelAJ, et al. Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology. 2022;302(3):627-636. doi:10.1148/radiol.210937
6.
BenjamensSDhunnooPMeskóB.The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3:118. doi:10.1038/s41746-020-00324-0
7.
KatzmanBDvan der PolCBSoyerP, et al. Artificial intelligence in emergency radiology: a review of applications and possibilities. Diagn Interv Imaging. 2023;104(1):6-10. doi:10.1016/j.diii.2022.07.005
8.
AvanzoMStancanelloJPirroneG, et al. The evolution of artificial intelligence in medical imaging: from computer science to machine and deep learning. Cancers. 2024;16(21):3702. doi:10.3390/cancers16213702
9.
SofferSBen-CohenAShimonO, et al. Convolutional neural networks for radiologic images: a radiologist’s guide. Radiology. 2019;290(3):590-606. doi:10.1148/radiol.2018180547
10.
TianTPChenYLeowWK, et al. Computing neck-shaft angle of femur for X-ray fracture detection. In: PetkovNWestenbergMA, eds. Computer Analysis of Images and Patterns. Springer; 2003:82-89. doi:10.1007/978-3-540-45179-2_11
11.
LumVLFLeowWKChenY, et al. Combining classifiers for bone fracture detection in X-ray images. In: IEEE International Conference on Image Processing; September14, 2005, Genova. doi:10.1109/ICIP.2005.1529959
12.
KimDHMacKinnonT.Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol. 2018;73(5):439-445. doi:10.1016/j.crad.2017.11.015
13.
MacIntyreNJDewanN.Epidemiology of distal radius fractures and factors predicting risk and prognosis. J Hand Ther. 2016;29(2):136-145. doi:10.1016/j.jht.2016.03.003
14.
NellansKWKowalskiEChungKC.The epidemiology of distal radius fractures. Hand Clin. 2012;28(2):113-125. doi:10.1016/j.hcl.2012.02.001
15.
SchneppendahlJWindolfJKaufmannRA.Distal radius fractures: current concepts. J Hand Surg Am. 2012;37(8):1718-1725. doi:10.1016/j.jhsa.2012.06.001
16.
NowakLLMoktarJHenryP, et al. Delayed fixation of distal radial fractures beyond three weeks after initial failed closed reduction increases the odds of reoperation. Bone Joint J. 2024;106-B(11):1257-1262. doi:10.1302/0301-620X.106B11.BJJ-2023-1349.R1
17.
KhanSPersitzJShrouder-HenryJ, et al. Effect of time-to-surgery on distal radius fracture outcomes: a systematic review. J Hand Surg Am. 2023;48(5):435-443. doi:10.1016/j.jhsa.2022.12.018
18.
BreuRAvelarCBertalanZ, et al. Artificial intelligence in traumatology. Bone Joint Res. 2024;13(10):588-595. doi:10.1302/2046-3758.1310.BJR-2023-0275.R3
19.
GanKLiuYZhangT, et al. Deep learning model for automatic identification and classification of distal radius fracture. J Imaging Inform Med. 2024;37(6):2874-2882. doi:10.1007/s10278-024-01144-4
20.
LeeSKimKGKimYJ, et al. Automatic segmentation and radiologic measurement of distal radius fractures using deep learning. Clin Orthop Surg. 2024;16(1):113-124. doi:10.4055/cios23130
21.
RusseMFRebmannPTranPH, et al. AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice. BMJ Open. 2024;14(1):e076954. doi:10.1136/bmjopen-2023-076954
22.
CohenMPuntonetJSanchezJ, et alArtificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs. Eur Radiol. 2023;33(6):3974-3983. doi:10.1007/s00330-022-09349-3
23.
MinHRabiYWadhawanA, et al. Automatic classification of distal radius fracture using a two-stage ensemble deep learning framework. Phys Eng Sci Med. 2023;46(2):877-886. doi:10.1007/s13246-023-01261-4
24.
AnttilaTTKarjalainenTVMäkeläTO, et al. Detecting distal radius fractures using a segmentation-based deep learning model. J Digit Imaging. 2023;36(2):679-687. doi:10.1007/s10278-022-00741-5
25.
SuzukiTMakiSYamazakiT, et al. Detecting distal radial fractures from wrist radiographs using a deep convolutional neural network with an accuracy comparable to hand orthopedic surgeons. J Digit Imaging. 2022;35(1):39-46. doi:10.1007/s10278-021-00519-1
26.
OkaKShiodeRYoshiiY, et al. Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays. J Orthop Surg Res. 2021;16(1):694. doi:10.1186/s13018-021-02845-0
27.
ToblerPCyriacJKovacsBK, et al. AI-based detection and classification of distal radius fractures using low-effort data labeling: evaluation of applicability and effect of training set size. Eur Radiol. 2021;31(9):6816-6824. doi:10.1007/s00330-021-07811-2
28.
RaisuddinAMVaattovaaraENevalainenM, et al. Critical evaluation of deep neural networks for wrist fracture detection. Sci Report. 2021;11(1):6006-6006. doi:10.1038/s41598-021-85570-2
29.
BlüthgenCBeckerASVittoriadeMartiniI, et al. Detection and localization of distal radius fractures: deep learning system versus radiologists. Eur J Radiol. 2020;126:108925-108925. doi:10.1016/j.ejrad.2020.108925
30.
YahalomiEChernofskyMWermanM. Detection of distal radius fractures trained by a small set of X-ray images and faster R-CNN. In: AraiKBhatiaRKapoorS, eds. Intelligent Computing. Springer; 2019:971-981. doi:10.1007/978-3-030-22871-2_69
31.
EbsimRNaqviJCootesTF. Automatic detection of wrist fractures from posteroanterior and lateral radiographs: a deep learning-based approach. In: Computational Methods and Clinical Applications in Musculoskeletal Imaging. Springer; 2019:114-125. doi:10.1007/978-3-030-11166-3_10
32.
GanKXuDLinY, et al. Artificial intelligence detection of distal radius fractures: a comparison between the convolutional neural network and professional assessments. Acta Orthop. 2019;90(4):394-400. doi:10.1080/17453674.2019.1600125
33.
ParkSHHanKJangHY, et al. Methods for clinical evaluation of artificial intelligence algorithms for medical diagnosis. Radiology. 2023;306(1):20-31. doi:10.1148/radiol.220182
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