|
[1]
|
Chen, M. and Decary, M. (2020) Artificial Intelligence in Healthcare: An Essential Guide for Health Leaders. Healthcare Management Forum, 33, 10-18. [Google Scholar] [CrossRef] [PubMed]
|
|
[2]
|
Myers, T.G., Ramkumar, P.N., Ricciardi, B.F., et al. (2020) Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. The Journal of Bone and Joint Surgery, 102, 830-840. [Google Scholar] [CrossRef]
|
|
[3]
|
Gómez-Barrena, E. and García-Rey, E. (2019) Complications in Total Joint Arthroplasties. Journal of Clinical Medicine, 8, Article 1891. [Google Scholar] [CrossRef] [PubMed]
|
|
[4]
|
Sepehri, A., Howard, L.C., Neufeld, M.E., et al. (2022) Compartment Syndrome after Hip and Knee Arthroplasty. Orthopedic Clinics of North America, 53, 25-32. [Google Scholar] [CrossRef] [PubMed]
|
|
[5]
|
Curlewis, K., Leung, B., Sinclair, L., et al. (2023) Systemic Medical Complications following Joint Replacement: A Review of the Evidence. The Annals of the Royal College of Surgeons of England, 105, 191-195. [Google Scholar] [CrossRef] [PubMed]
|
|
[6]
|
Rubinger, L., Gazendam, A., Ekhtiari, S. and Bhandari, M. (2023) Machine Learning and Artificial Intelligence in Research and Healthcare. Injury, 54, S69-S73. [Google Scholar] [CrossRef] [PubMed]
|
|
[7]
|
Navarro, S.M., Wang, E.Y., Haeberle, H.S., et al. (2018) Ma-chine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model. The Journal of Arthroplasty, 33, 3617-3623. [Google Scholar] [CrossRef] [PubMed]
|
|
[8]
|
Taunton, M.J., Liu, S.S. and Mont, M.A. (2023) Deep Learning: Orthopaedic Research Evolves for the Future. The Journal of Arthroplasty, 38, 1919-1920. [Google Scholar] [CrossRef] [PubMed]
|
|
[9]
|
Li, W., Xiao, Z., Liu, J., et al. (2023) Deep Learning-Assisted Knee Osteoarthritis Automatic Grading on Plain Radiographs: The Value of Multiview X-Ray Images and Prior Knowledge. Quantitative Imaging in Medicine and Surgery, 13, 3587-3601. [Google Scholar] [CrossRef] [PubMed]
|
|
[10]
|
Leung, K., Zhang, B., Tan, J., et al. (2020) Prediction of Total Knee Replacement and Diagnosis of Osteoarthritis by Using Deep Learning on Knee Radiographs: Data from the Osteoarthritis Initiative. Radiology, 296, 584-593. [Google Scholar] [CrossRef] [PubMed]
|
|
[11]
|
Jiřík, M., Moulisová, V., Hlaváč, M., et al. (2022) Artificial Neural Networks and Computer Vision in Medicine and Surgery. Rozhledy v Chirurgii, 101, 564-570. [Google Scholar] [CrossRef]
|
|
[12]
|
Soffer, S., Ben-Cohen, A., Shimon, O., et al. (2019) Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide. Radiology, 290, 590-606. [Google Scholar] [CrossRef] [PubMed]
|
|
[13]
|
Kim, M.S., Cho, R.K., Yang, S.C., et al. (2023) Machine Learning for Detecting Total Knee Arthroplasty Implant Loosening on Plain Radiographs. Bioengineering, 10, Article 632. [Google Scholar] [CrossRef] [PubMed]
|
|
[14]
|
Yi, P.H., Wei, J., Kim, T.K., et al. (2020) Automated Detec-tion & Classification of Knee Arthroplasty Using Deep Learning. Knee, 27, 535-542. [Google Scholar] [CrossRef] [PubMed]
|
|
[15]
|
Zoppo, G., Marrone, F., Pittarello, M., et al. (2020) AI Technology for Remote Clinical Assessment and Monitoring. Journal of Wound Care, 29, 692-706. [Google Scholar] [CrossRef] [PubMed]
|
|
[16]
|
Tiulpin, A., Thevenot, J., Rahtu, E., et al. (2018) Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach. Scientific Reports, 8, Article No. 1727. [Google Scholar] [CrossRef] [PubMed]
|
|
[17]
|
Norman, B., Pedoia, V., Noworolski, A., et al. (2019) Applying Densely Connected Convolutional Neural Networks for Staging Osteoarthritis Severity from Plain Radio-graphs. Journal of Digital Imaging, 32, 471-477. [Google Scholar] [CrossRef] [PubMed]
|
|
[18]
|
Schwartz, A.J., Clarke, H.D., Spangehl, M.J., et al. (2020) Can a Convolutional Neural Network Classify Knee Osteoarthritis on Plain Radiographs as Accurately as Fellowship-Trained Knee Arthroplasty Surgeons. The Journal of Arthroplasty, 35, 2423-2428. [Google Scholar] [CrossRef] [PubMed]
|
|
[19]
|
El-Galaly, A., Grazal, C., Kappel, A., et al. (2020) Can Ma-chine-Learning Algorithms Predict Early Revision TKA in the Danish Knee Arthroplasty Registry. Clinical Orthopaedics and Related Research, 478, 2088-2101. [Google Scholar] [CrossRef]
|
|
[20]
|
Klemt, C., Yeo, I., Harvey, M., et al. (2023) The Use of Artificial Intelligence for the Prediction of Periprosthetic Joint Infection following Aseptic Revision Total Knee Arthro-plasty. The Journal of Knee Surgery. [Google Scholar] [CrossRef] [PubMed]
|
|
[21]
|
Nie, L., Sun, Z., Shan, F., et al. (2023) An Artificial Intelligence Framework for the Diagnosis of Prosthetic Joint Infection Based on (99m) Tc-MDP Dynamic Bone Scintigraphy. Euro-pean Radiology, 33, 6794-6803. [Google Scholar] [CrossRef] [PubMed]
|
|
[22]
|
(2022) Deep Learning Method for Hip Knee Ankle Angle Pre-diction on Postoperative Full-Limb Radiographs of Total Knee Arthroplasty Patients. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2022, 5070-5073.
|
|
[23]
|
Gurung, B., Liu, P., Harris, P., et al. (2022) Artificial Intelligence for Image Analysis in Total Hip and Total Knee Arthroplasty: A Scoping Review. The Bone & Joint Journal, 104, 929-937. [Google Scholar] [CrossRef]
|
|
[24]
|
Park, J., Zhong, X., Miley, E.N., et al. (2023) Preoperative Prediction and Risk Factor Identification of Hospital Length of Stay for Total Joint Arthroplasty Patients Using Machine Learning. Arthroplasty Today, 22, Article ID: 101166. [Google Scholar] [CrossRef] [PubMed]
|
|
[25]
|
Ramkumar, P.N., Haeberle, H.S., Bloomfield, M.R., et al. (2019) Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring. The Journal of Arthroplasty, 34, 2204-2209. [Google Scholar] [CrossRef] [PubMed]
|
|
[26]
|
Chen, X., Liu, X., Wang, Y., et al. (2022) Development and Vali-dation of an Artificial Intelligence Preoperative Planning System for Total Hip Arthroplasty. Frontiers in Medicine, 9, Article 841202. [Google Scholar] [CrossRef] [PubMed]
|
|
[27]
|
Karnuta, J.M., Luu, B.C., Roth, A.L., et al. (2021) Artificial Intel-ligence to Identify Arthroplasty Implants from Radiographs of the Knee. The Journal of Arthroplasty, 36, 935-940. [Google Scholar] [CrossRef] [PubMed]
|
|
[28]
|
Karnuta, J.M., Shaikh, H., Murphy, M.P., et al. (2023) Artificial Intelligence for Automated Implant Identification in Knee Arthroplasty: A Multicenter External Validation Study Exceed-ing 3.5 Million Plain Radiographs. The Journal of Arthroplasty, 38, 2004-2008. [Google Scholar] [CrossRef] [PubMed]
|
|
[29]
|
Klemt, C., Uzosike, A.C., Cohen-Levy, W.B., et al. (2022) The Ability of Deep Learning Models to Identify Total Hip and Knee Arthroplasty Implant Design From Plain Radiographs. Journal of the American Academy of Orthopaedic Surgeons, 30, 409-415. [Google Scholar] [CrossRef]
|
|
[30]
|
Polce, E.M., Kunze, K.N., Paul, K.M., et al. (2021) Machine Learning Predicts Femoral and Tibial Implant Size Mismatch for Total Knee Arthroplasty. Arthroplasty Today, 8, 268-277.E2. [Google Scholar] [CrossRef] [PubMed]
|
|
[31]
|
Rodrigues, P., Antunes, M., Raposo, C., et al. (2019) Deep Segmentation Leverages Geometric Pose Estimation in Computer-Aided Total Knee Arthroplasty. Healthcare Technology Letters, 6, 226-230. [Google Scholar] [CrossRef] [PubMed]
|
|
[32]
|
Félix, I., Raposo, C., Antunes, M., et al. (2021) Towards Markerless Computer-Aided Surgery Combining Deep Segmentation and Geometric Pose Estimation: Application in Total Knee Ar-throplasty. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9, 271-278. [Google Scholar] [CrossRef]
|
|
[33]
|
Verstraete, M.A., Moore, R.E., Roche, M. and Conditt, M.A. (2020) The Application of Machine Learning to Balance a Total Knee Arthroplasty. Bone & Joint Open, 1, 236-244. [Google Scholar] [CrossRef]
|
|
[34]
|
田华. 机器人辅助人工髋膝关节置换手术是必然趋势[J]. 中华医学杂志, 2022, 102(1): 4-8.
|