Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.
BindmanRSMigliorettiDLLarsonEB. Rising use of diagnostic medical imaging in a large integrated health system. Health Aff. 2008;27(6):1491–1502. doi:10.1377/hlthaff.27.6.1491
2.
LecunYBengioYHintonG. Deep learning. Nature. 2015;521:436–444. doi:10.1038/nature14539
3.
HintonG. Deep learning—a technology with the potential to transform health care. JAMA. 2018;320(11):1101–1102. doi:10.1001/jama.2018.11100
4.
RajpurkarPIrvinJZhuK, et al.CheXNet: radiologist-level pneumonia detection on chest x-rays with deep learning. 2017;arXiv:1711.05225.
5.
LakhaniPSundaramB. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–582. doi:10.1148/radiol.2017162326
6.
LindseyRDaluiskiAChopraS, et al.Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A. 2018;115(45):11591–11596. doi:10.1073/pnas.1806905115
ChanHPSamalaRKHadjiiskiLM. CAD and AI for breast cancer—recent development and challenges. Br J Radiol. 2020;93(1108):20190580. doi:10.1259/bjr.20190580
10.
GerasKJMannRMMoyL. Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology. 2019;293(2):246–259. doi:10.1148/radiol.2019182627
11.
LodwickGSKeatsTEDorstJP. The coding of roentgen images for computer analysis as applied to lung cancer. Radiology. 1963;81:185–200. doi:10.1148/81.2.185
12.
Van GinnekenBHogewegLProkopM. Computer-aided diagnosis in chest radiography: Beyond nodules. Eur J Radiol. 2009;72(2):226–230. doi:10.1016/j.ejrad.2009.05.061
13.
van GinnekenB.Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning. Radiol Phys Technol. 2017;10(1):23–32. doi:10.1007/s12194-017-0394-5
14.
HeKZhangXRenSSunJ. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition; Las Vegas, NV; 2016:770–778. doi:10.1109/CVPR.2016.90
15.
HuangGLiuZVan Der MaatenLWeinbergerKQ. Densely connected convolutional networks. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition; Honolulu, HI; 2017:4700–4708. doi:10.1109/CVPR.2017.243
16.
KrizhevskyASutskeverIHintonGE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60:84–90. doi:10.1145/3065386
17.
SzegedyCLiuWJiaY, et al.Going deeper with convolutions. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition; Boston, MA; 2015:1–9. doi:10.1109/CVPR.2015.7298594
18.
WangXPengYLuLLuZBagheriMSummersRM. ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition; Honolulu, HI; 2017:3462–3471. doi:10.1109/CVPR.2017.369
QinZZSanderMSRaiB, et al.Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019;9(1):1–10. doi:10.1038/s41598-019-51503-3
RajpurkarPIrvinJBallRL, et al.Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018;15(11):e1002686. doi:10.1371/journal.pmed.1002686
25.
HuangZLinJXuL, et al.Fusion high-resolution network for diagnosing chest X-ray images. Electronics. 2020;9(1):190. doi:10.3390/electronics9010190
26.
AllaouziIAhmedMB. A novel approach for multi-label chest X-ray classification of common thorax diseases. IEEE Access. 2019;7:64279–64288. doi:10.1109/ACCESS.2019.2916849
27.
PanIAgarwalSMerckD. Generalizable inter-institutional classification of abnormal chest radiographs using efficient convolutional neural networks. J Digit Imaging. 2019;32(5):888–896. doi:10.1007/s10278-019-00180-9
28.
ParkSLeeSMLeeKH, et al.Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings. Eur Radiol. 2019;30(3):1359–1368. doi:10.1007/s00330-019-06532-x
29.
JohnsonAEWPollardTJBerkowitzSJ, et al.MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data. 2019;6(1):317. doi:10.1038/s41597-019-0322-0
SinghVDandaVGorniakRFlandersALakhaniP. Assessment of critical feeding tube malpositions on radiographs using deep learning. J Digit Imaging. 2019;32(4):651–655. doi:10.1007/s10278-019-00229-9
32.
SubramanianVWangHWuJTWongKCLSharmaASyeda-MahmoodT. Automated detection and type classification of central venous catheters in chest X-rays. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. 2019:522–530. Springer, Cham. doi:10.1007/978-3-030-32226-7_58
33.
YiXAdamsSBabynPElnajmiA. Automatic catheter and tube detection in pediatric X-ray images using a scale-recurrent network and synthetic data. J Digit Imaging. 2019;33(1):181–190. doi:10.1007/s10278-019-00201-7
34.
Frid-AdarMAmerRGreenspanH. Endotracheal tube detection and segmentation in chest radiographs using synthetic data. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. 2019:784–792. Springer, Cham. doi:10.1007/978-3-030-32226-7_87
35.
YiXAdamsSJHendersonRDEBabynP. Computer-aided assessment of catheters and tubes on radiographs: How good is artificial intelligence for assessment?Radiol Artif Intell. 2020;2(1):e190082. doi:10.1148/ryai.2020190082
ThodbergHHKreiborgSJuulAPedersenKD. The bonexpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging. 2009;28(1):52–66. doi:10.1109/TMI.2008.926067
39.
SpampinatoCPalazzoSGiordanoDAldinucciMLeonardiR. Deep learning for automated skeletal bone age assessment in X-ray images. Med Image Anal. 2017;36:41–51. doi:10.1016/j.media.2016.10.010
40.
LarsonDBChenMCLungrenMPHalabiSSStenceNVLanglotzCP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology. 2018;287(1):313–322. doi:10.1148/radiol.2017170236
41.
OlczakJFahlbergNMakiA, et al.Artificial intelligence for analyzing orthopedic trauma radiographs: deep learning algorithms—are they on par with humans for diagnosing fractures?Acta Orthop. 2017;88(6):581–586. doi:10.1080/17453674.2017.1344459
42.
VarmaMLuMGardnerR, et al.Automated abnormality detection in lower extremity radiographs using deep learning. Nat Mach Intell. 2019;1(12):578–583. doi:10.1038/s42256-019-0126-0
HalabiSSPrevedelloLMCramerJK, et al.The RSNA pediatric bone age machine learning challenge. Radiology. 2019;290(2):498–503. doi:10.1148/radiol.2018180736
45.
SiegelEL. What can we learn from the RSNA pediatric bone age machine learning challenge?Radiology. 2019;290(2):504–555. doi:10.1148/radiol.2018182657
46.
PrevedelloLMHalabiSSShihG, et al.Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions. Radiol Artif Intell. 2019;1(1):e180031. doi:10.1148/ryai.2019180031
YuJSYuSMErdalBS, et al.Detection and localisation of hip fractures on anteroposterior radiographs with artificial intelligence: proof of concept. Clin Radiol. 2020;75(3):237.e1–e9. doi:10.1016/j.crad.2019.10.022
ChoyGKhalilzadehOMichalskiM, et al.Current applications and future impact of machine learning in radiology. Radiology. 2018;288(2):318–328. doi:10.1148/radiol.2018171820
53.
LakhaniPPraterABHutsonRK, et al.Machine learning in radiology: applications beyond image interpretation. J Am Coll Radiol. 2018;15(2):350–359. doi:10.1016/j.jacr.2017.09.044
RushAMChopraSWestonJ. A neural attention model for abstractive sentence summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. September2015. 379–389. Lisbon, Portugal.
56.
NallapatiRZhouBdos SantosCGulçehreÇXiangB. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. February2016. 280–290. Berlin, Germany. doi:10.18653/v1/k16-1028
57.
QenamBKimTYCarrollMJHogarthM. Text simplification using consumer health vocabulary to generate patient-centered radiology reporting: translation and evaluation. J Med Internet Res. 2017;19(12):e417. doi:10.2196/jmir.8536
58.
AdamsSJTangRBabynP. Patient perspectives and priorities regarding artificial intelligence in radiology: opportunities for patient-centered radiology. J Am Coll Radiol. 2020:S1546–1550(20)30031–30034. doi:10.1016/j.jacr.2020.01.007
59.
HuZYangZLiangXSalakhutdinovRXingEP. Toward controlled generation of text. In: Proceedings of the 34th International Conference on Machine Learning (ICML 2017);Sydney, Australia; PMLR2017;70:1587–1596.
60.
DreyerKJKalraMKMaherMM, et al.Application of recently developed computer algorithm for automatic classification of unstructured radiology reports: validation study. Radiology. 2005;234(2):323–329. doi:10.1148/radiol.2341040049
61.
ChenMCBallRLYangL, et al.Deep learning to classify radiology free-text reports. Radiology. 2018;286(3):845–852. doi:10.1148/radiol.2017171115
62.
AlexanderAJiangAFerreiraCZurkiyaD. An intelligent future for medical imaging: A market outlook on artificial intelligence for medical imaging. J Am Coll Radiol. 2020;17(1 pt B):165–170. doi:10.1016/j.jacr.2019.07.019
BrowningTO’NeillTNgYFieldingJRPeshockRM. Special considerations for integrating artificial intelligence solutions in urban safety-net hospitals. J Am Coll Radiol. 2020;17(1 Pt B):171–174. doi:10.1016/j.jacr.2019.08.016
ChangPJ. Moving artificial intelligence from feasible to real: time to drill for gas and build roads. Radiology. 2020;294(2):432–433. doi:10.1148/radiol.2019192527
67.
AllenB. The role of the FDA in ensuring the safety and efficacy of artificial intelligence software and devices. J Am Coll Radiol. 2019;16(2):208–210. doi:10.1016/j.jacr.2018.09.007
68.
DikiciEBigelowMPrevedelloLMWhiteRDErdalBS. Integrating AI into radiology workflow: levels of research, production, and feedback maturity. J Med Imaging. 2020;7(1):016502. doi:10.1117/1.jmi.7.1.016502
69.
IrvinJRajpurkarPKoM, et al.CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proc AAAI Conf Artif Intell. 2019;33(1):590–597. doi:10.1609/aaai.v33i01.3301590
70.
BustosAPertusaASalinasJMde la VayáMI. PadChest: a large chest x-ray image dataset with multi-label annotated reports. 2019;arXiv:1901.07441.
71.
RajpurkarPIrvinJBagulA, et al.MURA: Large dataset for abnormality detection in musculoskeletal radiographs. In: Proceedings of the 1st Conference on Medical Imaging with Deep learning; Amsterdam, the Netherlands; 2018.
NIMH Data Archive. The Osteoarthritis Initiative n.d. Accessed July 18, 2020. https://nda.nih.gov/oai/
74.
GertychAZhangASayreJKurkowskaSPHuangHK. Bone age assessment of children using a digital hand atlas. Comput Med Imaging Graph. 2007;31(4-5):322–331. doi:10.1016/j.compmedimag.2007.02.012
75.
HwangEJParkSJinKN, et al.Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open. 2019;2(3):e191095. doi:10.1001/jamanetworkopen.2019.1095
FreedmanMTLoSCBSeibelJCBromleyCM. Lung nodules: Improved detection with software that suppresses the rib and clavicle on chest radiographs. Radiology. 2011;260(1):265–273.
