AI applications in musculoskeletal imaging: a narrative review
Tóm tắt
This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload. Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice.
Key points
• AI may potentially assist musculoskeletal radiologists in several interpretative tasks. • AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed. • AI should help radiologists to optimize workflow and augment diagnostic performance.
Tài liệu tham khảo
Russell S, Bohannon J (2015) Artificial intelligence. Fears of an AI pioneer. Science 349:252. https://doi.org/10.1126/science.349.6245.252
Erickson BJ, Korfiatis P, Akkus Z, Kline TL (2017) Machine learning for medical imaging. Radiographics 37:505–515. https://doi.org/10.1148/rg.2017160130
Chartrand G, Cheng PM, Vorontsov E et al (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131. https://doi.org/10.1148/rg.2017170077
Shin Y, Kim S, Lee YH (2022) AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency? Skeletal Radiol 51:293–304. https://doi.org/10.1007/s00256-021-03876-8
Lee YH (2018) Efficiency improvement in a busy radiology practice: determination of musculoskeletal magnetic resonance imaging protocol using deep-learning convolutional neural networks. J Digit Imaging 31:604–610. https://doi.org/10.1007/s10278-018-0066-y
Galbusera F, Bassani T, Casaroli G et al (2018) Generative models: an upcoming innovation in musculoskeletal radiology? A preliminary test in spine imaging. Eur Radiol Exp 2:29. https://doi.org/10.1186/s41747-018-0060-7
Gale W, Oakden-Rayner L, Carneiro G, et al (2017) Detecting hip fractures with radiologist-level performance using deep neural networks. http://arxiv.org/abs/1711.06504
Chen H-Y, Hsu BW-Y, Yin Y-K et al (2021) Application of deep learning algorithm to detect and visualize vertebral fractures on plain frontal radiographs. PLoS One 16:e0245992. https://doi.org/10.1371/journal.pone.0245992
Jones RM, Sharma A, Hotchkiss R et al (2020) Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs. NPJ Digit Med 3:144. https://doi.org/10.1038/s41746-020-00352-w
Aghnia Farda N, Lai J-Y, Wang J-C et al (2021) Sanders classification of calcaneal fractures in CT images with deep learning and differential data augmentation techniques. Injury 52:616–624. https://doi.org/10.1016/j.injury.2020.09.010
Tanzi L, Vezzetti E, Moreno R et al (2020) Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach. Eur J Radiol 133:109373. https://doi.org/10.1016/j.ejrad.2020.109373
Chung SW, Han SS, Lee JW et al (2018) Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthop 89:468–473. https://doi.org/10.1080/17453674.2018.1453714
Li Y-C, Chen H-H, Horng-Shing LuH et al (2021) Can a deep-learning model for the automated detection of vertebral fractures approach the performance level of human subspecialists? Clin Orthop Relat Res 479:1598–1612. https://doi.org/10.1097/CORR.0000000000001685
Lind A, Akbarian E, Olsson S et al (2021) Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system. PLoS One 16:e0248809. https://doi.org/10.1371/journal.pone.0248809
Olczak J, Emilson F, Razavian A et al (2021) Ankle fracture classification using deep learning: automating detailed AO Foundation/Orthopedic Trauma Association (AO/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. Acta Orthop 92:102–108. https://doi.org/10.1080/17453674.2020.1837420
Bien N, Rajpurkar P, Ball RL et al (2018) Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med 15:e1002699. https://doi.org/10.1371/journal.pmed.1002699
Thodberg HH, Kreiborg S, Juul A, Pedersen KD (2009) The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 28:52–66. https://doi.org/10.1109/TMI.2008.926067
Kim JR, Shim WH, Yoon HM et al (2017) Computerized bone age estimation using deep learning based program: evaluation of the accuracy and efficiency. AJR Am J Roentgenol 209:1374–1380. https://doi.org/10.2214/AJR.17.18224
Tiulpin A, Thevenot J, Rahtu E et al (2018) Automatic knee osteoarthritis diagnosis from plain radiographs: a deep learning-based approach. Sci Rep 8:1727. https://doi.org/10.1038/s41598-018-20132-7
Liu F, Zhou Z, Samsonov A et al (2018) Deep learning approach for evaluating knee MR images: achieving high diagnostic performance for cartilage lesion detection. Radiology 289:160–169. https://doi.org/10.1148/radiol.2018172986
Tiulpin A, Klein S, Bierma-Zeinstra SMA et al (2019) Multimodal machine learning-based knee osteoarthritis progression prediction from plain radiographs and clinical data. Sci Rep 9:20038. https://doi.org/10.1038/s41598-019-56527-3
von Schacky CE, Wilhelm NJ, Schäfer VS et al (2021) Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology 301:398–406. https://doi.org/10.1148/radiol.2021204531
Fritz B, Müller DA, Sutter R et al (2018) Magnetic resonance imaging–based grading of cartilaginous bone tumors. Invest Radiol 53:663–672. https://doi.org/10.1097/RLI.0000000000000486
Gitto S, Cuocolo R, Annovazzi A et al (2021) CT radiomics-based machine learning classification of atypical cartilaginous tumours and appendicular chondrosarcomas. EBioMedicine 68:103407. https://doi.org/10.1016/j.ebiom.2021.103407
Gitto S, Cuocolo R, van Langevelde K et al (2022) MRI radiomics-based machine learning classification of atypical cartilaginous tumour and grade II chondrosarcoma of long bones. EBioMedicine 75:103757. https://doi.org/10.1016/j.ebiom.2021.103757
Peeken JC, Spraker MB, Knebel C et al (2019) Tumor grading of soft tissue sarcomas using MRI-based radiomics. EBioMedicine 48:332–340. https://doi.org/10.1016/j.ebiom.2019.08.059
Lin P, Yang P-F, Chen S et al (2020) A delta-radiomics model for preoperative evaluation of neoadjuvant chemotherapy response in high-grade osteosarcoma. Cancer Imaging 20:7. https://doi.org/10.1186/s40644-019-0283-8
Chen H, Liu J, Cheng Z et al (2020) Development and external validation of an MRI-based radiomics nomogram for pretreatment prediction for early relapse in osteosarcoma: a retrospective multicenter study. Eur J Radiol 129:109066. https://doi.org/10.1016/j.ejrad.2020.109066
Wu Y, Xu L, Yang P et al (2018) Survival prediction in high-grade osteosarcoma using radiomics of diagnostic computed tomography. EBioMedicine 34:27–34. https://doi.org/10.1016/j.ebiom.2018.07.006
Gao Y, Kalbasi A, Hsu W et al (2020) Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Phys Med Biol 65:175006. https://doi.org/10.1088/1361-6560/ab9e58
Kitamura G (2021) Hanging protocol optimization of lumbar spine radiographs with machine learning. Skeletal Radiol 50:1809–1819. https://doi.org/10.1007/s00256-021-03733-8
Yi PH, Wei J, Kim TK et al (2020) Automated detection & classification of knee arthroplasty using deep learning. Knee 27:535–542. https://doi.org/10.1016/j.knee.2019.11.020
Yi PH, Kim TK, Wei J et al (2020) Automated detection and classification of shoulder arthroplasty models using deep learning. Skeletal Radiol 49:1623–1632. https://doi.org/10.1007/s00256-020-03463-3
Rouzrokh P, Wyles CC, Philbrick KA et al (2021) A deep learning tool for automated radiographic measurement of acetabular component inclination and version after total hip arthroplasty. J Arthroplasty 36:2510–2517.e6. https://doi.org/10.1016/j.arth.2021.02.026
Shah RF, Bini SA, Martinez AM et al (2020) Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms. Bone Joint J 102-B:101–106. https://doi.org/10.1302/0301-620X.102B6.BJJ-2019-1577.R1
Laur O, Wang B (2022) Musculoskeletal trauma and artificial intelligence: current trends and projections. Skeletal Radiol 51:257–269. https://doi.org/10.1007/s00256-021-03824-6
Bruno MA, Walker EA, Abujudeh HH (2015) Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35:1668–1676. https://doi.org/10.1148/rg.2015150023
Catapano M, Albano D, Pozzi G et al (2017) Differences between orthopaedic evaluation and radiological reports of conventional radiographs in patients with minor trauma admitted to the emergency department. Injury 48:2451–2456. https://doi.org/10.1016/j.injury.2017.08.054
Rajpurkar P, Irvin J, Bagul A, et al (2017) MURA: large dataset for abnormality detection in musculoskeletal radiographs. http://arxiv.org/abs/1712.06957
Lindsey R, Daluiski A, Chopra S et al (2018) Deep neural network improves fracture detection by clinicians. Proc Natl Acad Sci U S A 115:11591–11596. https://doi.org/10.1073/pnas.1806905115
Krogue JD, Cheng KV, Hwang KM et al (2020) automatic hip fracture identification and functional subclassification with deep learning. Radiol Artif Intell 2:e190023. https://doi.org/10.1148/ryai.2020190023
Ma Y, Luo Y (2021) Bone fracture detection through the two-stage system of Crack-Sensitive Convolutional Neural Network. Informatics Med Unlocked 22:100452. https://doi.org/10.1016/j.imu.2020.100452
Jin L, Yang J, Kuang K et al (2020) Deep-learning-assisted detection and segmentation of rib fractures from CT scans: development and validation of FracNet. EBioMedicine 62:103106. https://doi.org/10.1016/j.ebiom.2020.103106
Zhou Q-Q, Tang W, Wang J et al (2021) Automatic detection and classification of rib fractures based on patients’ CT images and clinical information via convolutional neural network. Eur Radiol 31:3815–3825. https://doi.org/10.1007/s00330-020-07418-z
Pranata YD, Wang K-C, Wang J-C et al (2019) Deep learning and SURF for automated classification and detection of calcaneus fractures in CT images. Comput Methods Programs Biomed 171:27–37. https://doi.org/10.1016/j.cmpb.2019.02.006
Mutasa S, Varada S, Goel A et al (2020) Advanced deep learning techniques applied to automated femoral neck fracture detection and classification. J Digit Imaging 33:1209–1217. https://doi.org/10.1007/s10278-020-00364-8
Tomita N, Cheung YY, Hassanpour S (2018) Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans. Comput Biol Med 98:8–15. https://doi.org/10.1016/j.compbiomed.2018.05.011
Liu F, Guan B, Zhou Z et al (2019) Fully automated diagnosis of anterior cruciate ligament tears on knee MR images by using deep learning. Radiol Artif Intell 1:180091. https://doi.org/10.1148/ryai.2019180091
Kim M, Park H, Kim JY, et al (2020) MRI-based diagnosis of rotator cuff tears using deep learning and weighted linear combinations. In: Proceedings of the 5th Machine Learning for Healthcare Conference (PMLR). Vol. 126, p 292–308
Hong G, Zhang L, Kong X, Herbertl L (2021) Artificial intelligence image–assisted knee ligament trauma repair efficacy analysis and postoperative femoral nerve block analgesia effect research. World Neurosurg 149:492–501. https://doi.org/10.1016/j.wneu.2020.11.122
Shin Y, Yang J, Lee YH, Kim S (2021) Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography 40:30–44. https://doi.org/10.14366/usg.20080
Hendrickx LAM, Sobol GL, Langerhuizen DWG et al (2020) A machine learning algorithm to predict the probability of (occult) posterior malleolar fractures associated with tibial shaft fractures to guide “malleolus first” fixation. J Orthop Trauma 34:131–138. https://doi.org/10.1097/BOT.0000000000001663
Machine learning consortium, on behalf of the SPRINT and FLOW Investigators (2021) A machine learning algorithm to identify patients with tibial shaft fractures at risk for infection after operative treatment. J Bone Joint Surg Am 103:532–40. https://doi.org/10.2106/JBJS.20.00903
Dallora AL, Anderberg P, Kvist O et al (2019) Bone age assessment with various machine learning techniques: a systematic literature review and meta-analysis. PLoS One 14:e0220242. https://doi.org/10.1371/journal.pone.0220242
Greulich WW, Pyle SI (1959) Radiographic atlas of skeletal development of the hand and wrist. Stanford University Press, Stanford
Tanner JM (2001) Assessment of skeletal maturity and prediction of adult height (TW3 method), 3rd edn. W.B. Saunders, London
Lee B-D, Lee MS (2021) Automated bone age assessment using artificial intelligence: the future of bone age assessment. Korean J Radiol 22:792. https://doi.org/10.3348/kjr.2020.0941
Mettler FA, Huda W, Yoshizumi TT, Mahesh M (2008) Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology 248:254–263. https://doi.org/10.1148/radiol.2481071451
Michael DJ, Nelson AC (1989) HANDX: a model-based system for automatic segmentation of bones from digital hand radiographs. IEEE Trans Med Imaging 8:64–69. https://doi.org/10.1109/42.20363
Tanner JM, Oshman D, Lindgren G et al (1994) Reliability and validity of computer-assisted estimates of Tanner-Whitehouse skeletal maturity (CASAS): comparison with the manual method. Horm Res 42:288–294. https://doi.org/10.1159/000184211
Son SJ, Song Y, Kim N et al (2019) TW3-based fully automated bone age assessment system using deep neural networks. IEEE Access 7:33346–33358. https://doi.org/10.1109/ACCESS.2019.2903131
Bui TD, Lee J-J, Shin J (2019) Incorporated region detection and classification using deep convolutional networks for bone age assessment. Artif Intell Med 97:1–8. https://doi.org/10.1016/j.artmed.2019.04.005
Ontell FK, Ivanovic M, Ablin DS, Barlow TW (1996) Bone age in children of diverse ethnicity. AJR Am J Roentgenol 167:1395–1398. https://doi.org/10.2214/ajr.167.6.8956565
Zhang L, Chen J, Hou L et al (2022) Clinical application of artificial intelligence in longitudinal image analysis of bone age among GHD patients. Front Pediatr 10:986500. https://doi.org/10.3389/fped.2022.986500
Joseph GB, McCulloch CE, Sohn JH et al (2022) AI MSK clinical applications: cartilage and osteoarthritis. Skeletal Radiol 51:331–343. https://doi.org/10.1007/s00256-021-03909-2
Halilaj E, Le Y, Hicks JL et al (2018) Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative. Osteoarthritis Cartilage 26:1643–1650. https://doi.org/10.1016/j.joca.2018.08.003
Lodwick GS, Haun CL, Smith WE et al (1963) Computer diagnosis of primary bone tumors. Radiology 80:273–275. https://doi.org/10.1148/80.2.273
Li MD, Ahmed SR, Choy E et al (2022) Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skeletal Radiol 51:245–256. https://doi.org/10.1007/s00256-021-03820-w
Fanciullo C, Gitto S, Carlicchi E et al (2022) Radiomics of musculoskeletal sarcomas: a narrative review. J Imaging 8:45. https://doi.org/10.3390/jimaging8020045
Richardson ML, Amini B, Kinahan PE (2022) Bone and soft tissue tumors: horizons in radiomics and artificial intelligence. Radiol Clin North Am 60:339–358. https://doi.org/10.1016/j.rcl.2021.11.011
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169
Chianca V, Cuocolo R, Gitto S et al (2021) Radiomic machine learning classifiers in spine bone tumors: a multi-software, multi-scanner study. Eur J Radiol 137:109586. https://doi.org/10.1016/j.ejrad.2021.109586
Gitto S, Cuocolo R, Albano D et al (2020) MRI radiomics-based machine-learning classification of bone chondrosarcoma. Eur J Radiol 128:109043. https://doi.org/10.1016/j.ejrad.2020.109043
Gitto S, Corino VDA, Annovazzi A et al (2022) 3D vs. 2D MRI radiomics in skeletal Ewing sarcoma: feature reproducibility and preliminary machine learning analysis on neoadjuvant chemotherapy response prediction. Front Oncol 12:1016123. https://doi.org/10.3389/fonc.2022.1016123
Casale R, Varriano G, Santone A et al (2023) Predicting risk of metastases and recurrence in soft-tissue sarcomas via radiomics and formal methods. JAMIA Open 6:ooad025. https://doi.org/10.1093/jamiaopen/ooad025
Lisson CS, Lisson CG, Flosdorf K et al (2018) Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study. Eur Radiol 28:468–477. https://doi.org/10.1007/s00330-017-5014-6
Pressney I, Khoo M, Endozo R et al (2020) Pilot study to differentiate lipoma from atypical lipomatous tumour/well-differentiated liposarcoma using MR radiomics-based texture analysis. Skeletal Radiol 49:1719–1729. https://doi.org/10.1007/s00256-020-03454-4
Gitto S, Interlenghi M, Cuocolo R et al (2023) MRI radiomics-based machine learning for classification of deep-seated lipoma and atypical lipomatous tumor of the extremities. Radiol Med 128:989–998. https://doi.org/10.1007/s11547-023-01657-y
Gitto S, Cuocolo R, Albano D et al (2021) CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies. Insights Imaging 12:68. https://doi.org/10.1186/s13244-021-01008-3
Gitto S, Cuocolo R, Emili I et al (2021) Effects of interobserver variability on 2D and 3D CT- and MRI-based texture feature reproducibility of cartilaginous bone tumors. J Digit Imaging 34:820–832. https://doi.org/10.1007/s10278-021-00498-3
Gitto S, Bologna M, Corino VDA et al (2022) Diffusion-weighted MRI radiomics of spine bone tumors: feature stability and machine learning-based classification performance. Radiol Med 127:518–525. https://doi.org/10.1007/s11547-022-01468-7
Zhou X, Wang H, Feng C et al (2022) Emerging applications of deep learning in bone tumors: current advances and challenges. Front Oncol 12:908873. https://doi.org/10.3389/fonc.2022.908873
Eweje FR, Bao B, Wu J et al (2021) Deep learning for classification of bone lesions on routine MRI. EBioMedicine 68:103402. https://doi.org/10.1016/j.ebiom.2021.103402
Navarro F, Dapper H, Asadpour R et al (2021) Development and external validation of deep-learning-based tumor grading models in soft-tissue sarcoma patients using MR imaging. Cancers (Basel) 13:2866. https://doi.org/10.3390/cancers13122866
Zhang R, Huang L, Xia W et al (2018) Multiple supervised residual network for osteosarcoma segmentation in CT images. Comput Med Imaging Graph 63:1–8. https://doi.org/10.1016/j.compmedimag.2018.01.006
Lang N, Zhang Y, Zhang E et al (2019) Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI. Magn Reson Imaging 64:4–12. https://doi.org/10.1016/j.mri.2019.02.013
Yin P, Mao N, Chen H et al (2020) Machine and deep learning based radiomics models for preoperative prediction of benign and malignant sacral tumors. Front Oncol 10:564725. https://doi.org/10.3389/fonc.2020.564725
He Y, Guo J, Ding X et al (2019) Convolutional neural network to predict the local recurrence of giant cell tumor of bone after curettage based on pre-surgery magnetic resonance images. Eur Radiol 29:5441–5451. https://doi.org/10.1007/s00330-019-06082-2
Kurtz S, Ong K, Lau E et al (2007) Projections of primary and revision hip and knee arthroplasty in the United States from 2005 to 2030. J Bone Joint Surg Am 89:780–785. https://doi.org/10.2106/JBJS.F.00222
Yi PH, Mutasa S, Fritz J (2022) AI MSK clinical applications: orthopedic implants. Skeletal Radiol 51:305–313. https://doi.org/10.1007/s00256-021-03879-5
Yi PH, Kim TK, Wei J et al (2019) Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning. Pediatr Radiol 49:1066–1070. https://doi.org/10.1007/s00247-019-04408-2
Filice RW, Frantz SK (2019) Effectiveness of deep learning algorithms to determine laterality in radiographs. J Digit Imaging 32:656–664. https://doi.org/10.1007/s10278-019-00226-y
Kitamura G (2020) Deep learning evaluation of pelvic radiographs for position, hardware presence, and fracture detection. Eur J Radiol 130:109139. https://doi.org/10.1016/j.ejrad.2020.109139
Ren M, Yi PH (2022) Artificial intelligence in orthopedic implant model classification: a systematic review. Skeletal Radiol 51:407–416. https://doi.org/10.1007/s00256-021-03884-8
Urban G, Porhemmat S, Stark M et al (2020) Classifying shoulder implants in X-ray images using deep learning. Comput Struct Biotechnol J 18:967–972. https://doi.org/10.1016/j.csbj.2020.04.005
Cho BH, Kaji D, Cheung ZB et al (2020) Automated measurement of lumbar lordosis on radiographs using machine learning and computer vision. Global Spine J 10:611–618. https://doi.org/10.1177/2192568219868190
Zheng Q, Shellikeri S, Huang H et al (2020) Deep learning measurement of leg length discrepancy in children based on radiographs. Radiology 296:152–158. https://doi.org/10.1148/radiol.2020192003
Radlink surgical system. https://radlink.com/radlink-surgical-system/. Accessed 14 Aug 2022
Albano D, Gitto S, Messina C et al (2023) MRI-based artificial intelligence to predict infection following total hip arthroplasty failure. Radiol Med 128:340–346. https://doi.org/10.1007/s11547-023-01608-7
Fritz B, Fritz J (2022) Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches. Skeletal Radiol 51:315–329. https://doi.org/10.1007/s00256-021-03830-8
Berson ER, Aboian MS, Malhotra A, Payabvash S (2023) Artificial intelligence for neuroimaging and musculoskeletal radiology: overview of current commercial algorithms. Semin Roentgenol 58:178–183. https://doi.org/10.1053/j.ro.2023.03.002
Harvey HB, Gowda V (2022) Clinical applications of AI in MSK imaging: a liability perspective. Skeletal Radiol 51:235–238. https://doi.org/10.1007/s00256-021-03782-z