A Review on the Use of Artificial Intelligence in Fracture Detection

被引:8
作者
Bhatnagar, Aayushi [1 ]
Kekatpure, Aditya L. [2 ]
Velagala, Vivek R. [1 ]
Kekatpure, Aashay [3 ]
机构
[1] Datta Meghe Inst Higher Educ & Res, Jawaharlal Nehru Med Coll, Med, Wardha, India
[2] Datta Meghe Inst Higher Educ & Res, Jawaharlal Nehru Med Coll, Orthoped Surg, Wardha, India
[3] Narendra Kumar Prasadrao Salve Inst Med Sci & Res, Orthoped Surg, Nagpur, India
关键词
natural language processing; orthopedic traumatology; radio-diagnosis; recurrent neural networks; convolutional neural networks; deep learning; machine learning; artificial intelligence; CLASSIFICATION;
D O I
10.7759/cureus.58364
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Artificial intelligence (AI) simulates intelligent behavior using computers with minimum human intervention. Recent advances in AI, especially deep learning, have made significant progress in perceptual operations, enabling computers to convey and comprehend complicated input more accurately. Worldwide, fractures affect people of all ages and in all regions of the planet. One of the most prevalent causes of inaccurate diagnosis and medical lawsuits is overlooked fractures on radiographs taken in the emergency room, which can range from 2% to 9%. The workforce will soon be under a great deal of strain due to the growing demand for fracture detection on multiple imaging modalities. A dearth of radiologists worsens this rise in demand as a result of a delay in hiring and a significant percentage of radiologists close to retirement. Additionally, the process of interpreting diagnostic images can sometimes be challenging and tedious. Integrating orthopedic radio-diagnosis with AI presents a promising solution to these problems. There has recently been a noticeable rise in the application of deep learning techniques, namely convolutional neural networks (CNNs), in medical imaging. In the field of orthopedic trauma, CNNs are being documented to operate at the proficiency of expert orthopedic surgeons and radiologists in the identification and categorization of fractures. CNNs can analyze vast amounts of data at a rate that surpasses that of human observations. In this review, we discuss the use of deep learning methods in fracture detection and classification, the integration of AI with various imaging modalities, and the benefits and disadvantages of integrating AI with radio-diagnostics.
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页数:12
相关论文
共 37 条
[21]   Artificial intelligence to diagnosis distal radius fracture using biplane plain X-rays [J].
Oka, Kunihiro ;
Shiode, Ryoya ;
Yoshii, Yuichi ;
Tanaka, Hiroyuki ;
Iwahashi, Toru ;
Murase, Tsuyoshi .
JOURNAL OF ORTHOPAEDIC SURGERY AND RESEARCH, 2021, 16 (01)
[22]   Artificial intelligence for analyzing orthopedic trauma radiographs Deep learning algorithms-are they on par with humans for diagnosing fractures? [J].
Olczak, Jakub ;
Fahlberg, Niklas ;
Maki, Atsuto ;
Razavian, Ali Sharif ;
Jilert, Anthony ;
Stark, Andre ;
Skoldenberg, Olof ;
Gordon, Max .
ACTA ORTHOPAEDICA, 2017, 88 (06) :581-586
[23]   Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images [J].
Pranata, Yoga Dwi ;
Wang, Kuan-Chung ;
Wang, Jia-Ching ;
Idram, Irwansyah ;
Lai, Jiing-Yih ;
Liu, Jia-Wei ;
Hsieh, I-Hui .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2019, 171 :27-37
[24]   Development and external validation of automated detection, classification, and localization of ankle fractures: inside the black box of a convolutional neural network (CNN) [J].
Prijs, Jasper ;
Liao, Zhibin ;
To, Minh-Son ;
Verjans, Johan ;
Jutte, Paul C. ;
Stirler, Vincent ;
Olczak, Jakub ;
Gordon, Max ;
Guss, Daniel ;
DiGiovanni, Christopher W. ;
Jaarsma, Ruurd L. ;
IJpma, Frank F. A. ;
Doornberg, Job N. .
EUROPEAN JOURNAL OF TRAUMA AND EMERGENCY SURGERY, 2023, 49 (02) :1057-1069
[25]   Artificial Intelligence: Threat or Boon to Radiologists? [J].
Recht, Michael ;
Bryan, R. Nick .
JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY, 2017, 14 (11) :1476-1480
[26]   Clinically focused multi-cohort benchmarking as a tool for external validation of artificial intelligence algorithm performance in basic chest radiography analysis [J].
Rudolph, Jan ;
Schachtner, Balthasar ;
Fink, Nicola ;
Koliogiannis, Vanessa ;
Schwarze, Vincent ;
Goller, Sophia ;
Trappmann, Lena ;
Hoppe, Boj F. ;
Mansour, Nabeel ;
Fischer, Maximilian ;
Ben Khaled, Najib ;
Joergens, Maximilian ;
Dinkel, Julien ;
Kunz, Wolfgang G. ;
Ricke, Jens ;
Ingrisch, Michael ;
Sabel, Bastian O. ;
Rueckel, Johannes .
SCIENTIFIC REPORTS, 2022, 12 (01)
[27]   Artificial intelligence for fracture diagnosis in orthopedic X-rays: current developments and future potential [J].
Sharma, Sanskrati .
SICOT-J, 2023, 9
[28]  
Shen DG, 2017, ANNU REV BIOMED ENG, V19, P221, DOI [10.1146/annurev-bioeng-071516-044442, 10.1146/annurev-bioeng-071516044442]
[29]   Machine Learning Solutions for Osteoporosis-A Review [J].
Smets, Julien ;
Shevroja, Enisa ;
Hugle, Thomas ;
Leslie, William D. ;
Hans, Didier .
JOURNAL OF BONE AND MINERAL RESEARCH, 2021, 36 (05) :833-851
[30]   Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs [J].
Thian, Yee Liang ;
Li, Yiting ;
Jagmohan, Pooja ;
Sia, David ;
Chan, Vincent Ern Yao ;
Tan, Robby T. .
RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2019, 1 (01)