$Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays$
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Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays

Abstract Background Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CN...

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Autores principales:	Kunihiro Oka, Ryoya Shiode, Yuichi Yoshii, Hiroyuki Tanaka, Toru Iwahashi, Tsuyoshi Murase
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Artificial intelligence Distal radius fracture Small data Computer-aided diagnosis Orthopedic surgery RD701-811 Diseases of the musculoskeletal system RC925-935
Acceso en línea:	https://doaj.org/article/1494460d8ecc445fbb1936b9376a0c53
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spelling	oai:doaj.org-article:1494460d8ecc445fbb1936b9376a0c532021-11-28T12:26:59ZArtificial intelligence to diagnosis distal radius fracture using biplane plain X-rays10.1186/s13018-021-02845-01749-799Xhttps://doaj.org/article/1494460d8ecc445fbb1936b9376a0c532021-11-01T00:00:00Zhttps://doi.org/10.1186/s13018-021-02845-0https://doaj.org/toc/1749-799XAbstract Background Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images. Methods VGG16, a learned image recognition model, was used as the CNN. It was modified into a network with two output layers to identify the fractures in plain X-ray images. We augmented 369 plain X-ray anteroposterior images and 360 lateral images of distal radius fractures, as well as 129 anteroposterior images and 125 lateral images of normal wrists to conduct training and diagnostic tests. Similarly, diagnostic tests for fractures of the styloid process of the ulna were conducted using 189 plain X-ray anteroposterior images of fractures and 302 images of the normal styloid process. The distal radius fracture is determined by entering an anteroposterior image of the wrist for testing into the trained AI. If it identifies a fracture, it is diagnosed as the same. However, if the anteroposterior image is determined as normal, the lateral image of the same patient is entered. If a fracture is identified, the final diagnosis is fracture; if the lateral image is identified as normal, the final diagnosis is normal. Results The diagnostic accuracy of distal radius fractures and fractures of the styloid process of the ulna were 98.0 ± 1.6% and 91.1 ± 2.5%, respectively. The areas under the receiver operating characteristic curve were 0.991 {n = 540; 95% confidence interval (CI), 0.984–0.999} and 0.956 (n = 450; 95% CI 0.938–0.973). Conclusions Our method resulted in a good diagnostic rate, even when using a relatively small amount of data.Kunihiro OkaRyoya ShiodeYuichi YoshiiHiroyuki TanakaToru IwahashiTsuyoshi MuraseBMCarticleArtificial intelligenceDistal radius fractureSmall dataComputer-aided diagnosisOrthopedic surgeryRD701-811Diseases of the musculoskeletal systemRC925-935ENJournal of Orthopaedic Surgery and Research, Vol 16, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial intelligence Distal radius fracture Small data Computer-aided diagnosis Orthopedic surgery RD701-811 Diseases of the musculoskeletal system RC925-935
spellingShingle	Artificial intelligence Distal radius fracture Small data Computer-aided diagnosis Orthopedic surgery RD701-811 Diseases of the musculoskeletal system RC925-935 Kunihiro Oka Ryoya Shiode Yuichi Yoshii Hiroyuki Tanaka Toru Iwahashi Tsuyoshi Murase Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays
description	Abstract Background Although the automatic diagnosis of fractures using artificial intelligence (AI) has recently been reported to be more accurate than those by orthopedics specialists, big data with at least 1000 images or more are required for deep learning of the convolutional neural network (CNN) to improve diagnostic accuracy. The aim of this study was to develop an AI system capable of diagnosing distal radius fractures with high accuracy even when learning with relatively small data by learning to use bi-planar X-rays images. Methods VGG16, a learned image recognition model, was used as the CNN. It was modified into a network with two output layers to identify the fractures in plain X-ray images. We augmented 369 plain X-ray anteroposterior images and 360 lateral images of distal radius fractures, as well as 129 anteroposterior images and 125 lateral images of normal wrists to conduct training and diagnostic tests. Similarly, diagnostic tests for fractures of the styloid process of the ulna were conducted using 189 plain X-ray anteroposterior images of fractures and 302 images of the normal styloid process. The distal radius fracture is determined by entering an anteroposterior image of the wrist for testing into the trained AI. If it identifies a fracture, it is diagnosed as the same. However, if the anteroposterior image is determined as normal, the lateral image of the same patient is entered. If a fracture is identified, the final diagnosis is fracture; if the lateral image is identified as normal, the final diagnosis is normal. Results The diagnostic accuracy of distal radius fractures and fractures of the styloid process of the ulna were 98.0 ± 1.6% and 91.1 ± 2.5%, respectively. The areas under the receiver operating characteristic curve were 0.991 {n = 540; 95% confidence interval (CI), 0.984–0.999} and 0.956 (n = 450; 95% CI 0.938–0.973). Conclusions Our method resulted in a good diagnostic rate, even when using a relatively small amount of data.
format	article
author	Kunihiro Oka Ryoya Shiode Yuichi Yoshii Hiroyuki Tanaka Toru Iwahashi Tsuyoshi Murase
author_facet	Kunihiro Oka Ryoya Shiode Yuichi Yoshii Hiroyuki Tanaka Toru Iwahashi Tsuyoshi Murase
author_sort	Kunihiro Oka
title	Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays
title_short	Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays
title_full	Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays
title_fullStr	Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays
title_full_unstemmed	Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays
title_sort	artificial intelligence to diagnosis distal radius fracture using biplane plain x-rays
publisher	BMC
publishDate	2021
url	https://doaj.org/article/1494460d8ecc445fbb1936b9376a0c53
work_keys_str_mv	AT kunihirooka artificialintelligencetodiagnosisdistalradiusfractureusingbiplaneplainxrays AT ryoyashiode artificialintelligencetodiagnosisdistalradiusfractureusingbiplaneplainxrays AT yuichiyoshii artificialintelligencetodiagnosisdistalradiusfractureusingbiplaneplainxrays AT hiroyukitanaka artificialintelligencetodiagnosisdistalradiusfractureusingbiplaneplainxrays AT toruiwahashi artificialintelligencetodiagnosisdistalradiusfractureusingbiplaneplainxrays AT tsuyoshimurase artificialintelligencetodiagnosisdistalradiusfractureusingbiplaneplainxrays
_version_	1718407936765591552

Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays

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