Hasta Sonuçlarını İyileştirmek İçin Yapay Zekâ Kullanımı
Özet
Yapay zekâ insanın olduğu her yerde kendine kullanım alanı bulmuştur. 3Çok geniş bir alana yayılan yapay zekanın çeşitli algoritmaları mevcuttur. Tüm bu algoritmalar ortopedi alanında da yaygın bir kullanım alanı bulmuştur. Bu algoritmalar sayesinde tanı koyma, görüntü tanıma, tedavi planlama, cerrahide robot yardımı, rehabilitasyon, prognoz ve tahminsel analiz, hasta izleme ve proaktif müdahaleler, büyük veri analizi ve implant tasarımında ortopedistlere ve hastlara fayda sağlamaktadır. Ancak standardizasyon eksikliği, güvenlik ve etik problemler nedeniyle tek başına kullanımı yerine sağlık profesyonellerine yardımcı ek materyal olarak kullanımı daha uygundur.
Artificial intelligence has found its use wherever there are humans. We see that artificial intelligence, which is still in its infancy in the health sector, has made a rapid entry into many areas of orthopaedics. Artificial intelligence, which is spread over a very wide area, has various algorithms. All these algorithms have found widespread use in orthopaedics. These algorithms benefit orthopaedists and patients in diagnosis, image recognition, treatment planning, robot assistance in surgery, rehabilitation, prognosis and predictive analysis, patient monitoring and proactive interventions, big data analysis and implant design. However, due to lack of standardisation, safety and ethical problems, it is more suitable to be used as an additional material to assist health professionals instead of being used alone.
Referanslar
Densen P. Challenges and Opportunities Facing Medical Education. Transactions of the American Clinical and Climatological Association.2011;122:48.
Hashimoto DA, Rosman G, Rus D, et al. Artificial Intelligence in Surgery: Promises and Perils. Annals of Surgery. 2018;268(1):70; doi: 10.1097/SLA.0000000000002693.
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education.2023;23(1):1–15; doi: 10.1186/S12909-023-04698-Z.
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal 2019;6(2):94–98; doi: 10.7861/FUTUREHOSP.6-2-94.
Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Journal of the American Medical Association (JAMA). 2016;316(22):2402–2410; doi: 10.1001/JAMA.2016.17216.
Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine. 2019;25(1):65–69; doi: 10.1038/s41591-018-0268-3.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542(7639):115–118; doi: 10.1038/nature21056.
Vaish A, Migliorini F, Vaishya R. Artificial intelligence in foot and ankle surgery: current concepts. Orthopadie (Heidelberg, Germany) 2023;52(12):1011; doi: 10.1007/S00132-023-04426-X.
Myers TG, Ramkumar PN, Ricciardi BF, et al. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. Journal of Bone and Joint Surgery. 2020;102(9):830; doi: 10.2106/JBJS.19.01128.
Vasey B, Ursprung S, Beddoe B, et al. Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems: A Systematic Review. Journal of the American Medical Association (JAMA) Network Open. 2021;4(3); doi: 10.1001/JAMANETWORKOPEN.2021.1276.
Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering 2023;10(12); doi: 10.3390/BIOENGINEERING10121435.
Gyftopoulos S, Lin D, Knoll F, et al. Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions. American journal of roentgenology. 2019;213(3):506; doi: 10.2214/AJR.19.21117.
Langerhuizen DWG, Bulstra AEJ, Janssen SJ, et al. Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid? Clinical Orthopaedics and Related Research. 2020;478(11):2653; doi: 10.1097/CORR.0000000000001318.
Borjali A, Chen AF, Sodhi A, et al. Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network. Journal of orthopaedic research. 2019; 7(38): 1465-1471; doi:10.1002/jor.24617
Xie X, Li Z, Bai L, et al. Deep Learning‐Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury. Scientific Programming, 2021; 1-8 ; doi: 10.1155/2021/9935910.
Park CW, Oh SJ, Kim KS, et al. Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation. Public Library of Science (PLOS) One. 2022;17(2); doi: 10.1371/JOURNAL.PONE.0264140.
Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nature Reviews Cancer. 2018;18(8):500; doi: 10.1038/S41568-018-0016-5.
Swiecicki A, Li N, O’Donnell J, et al. Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Computers in Biology and Medicine. 2021;133; doi: 10.1016/J.COMPBIOMED.2021.104334.
Park JJ, Tiefenbach J, Demetriades AK. The role of artificial intelligence in surgical simulation. Frontiers in Medical Technology. 2022;4:1076755; doi: 10.3389/FMEDT.2022.1076755/BIBTEX.
Lambrechts A, Wirix-Speetjens R, Maes F, et al. Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty. Frontiers in Robotics and AI. 2022;9; doi: 10.3389/FROBT.2022.840282/FULL.
Zhou X, Zhang D, Xie Z, et al. Application of preoperative 3D printing in the internal fixation of posterior rib fractures with embracing device: a cohort study. BMC Surgery. 2023;23(1); doi: 10.1186/S12893-023-02128-X.
Wong RMY, Wong PY, Liu C, et al. 3D printing in orthopaedic surgery: A scoping review of randomized controlled trials. Bone & Joint Research. 2021;10(12):807–819; doi: 10.1302/2046-3758.1012.BJR-2021-0288.R2.
Hasan S, Ahmed A, Waheed MA, et al. Transforming Orthopedic Joint Surgeries: The Role of Artificial Intelligence (AI) and Robotics. Cureus 2023;15(8); doi: 10.7759/CUREUS.43289.
Fan M, Zhang Q, Fang Y, et al. Robotic solution for orthopedic surgery. Chinese Medical Journal. 2023;136(12):1387; doi: 10.1097/CM9.0000000000002702.
Zhang H, Huang C, Wang D, et al. Artificial Intelligence in Scoliosis: Current Applications and Future Directions. Journal of Clinical Medicine. 2023;12(23):7382; doi: 10.3390/JCM12237382.
Tian W, Liu Y jun, Liu B, et al. Guideline for Thoracolumbar Pedicle Screw Placement Assisted by Orthopaedic Surgical Robot. Orthopaedic Surgery. 2019;11(2):153; doi: 10.1111/OS.12453.
Hou C, Yang H, Chen Y, et al. Comparison of robot versus fluoroscopy-assisted pedicle screw instrumentation in adolescent idiopathic scoliosis surgery: A retrospective study. Frontiers in Surgery. 2022;9; doi: 10.3389/FSURG.2022.1085580.
Ravali RS, Vijayakumar TM, Santhana Lakshmi K, et al. A systematic review of artificial intelligence for pediatric physiotherapy practice: Past, present, and future. Neuroscience Informatics 2022;2(4):100045; doi: 10.1016/J.NEURI.2022.100045.
Vélez‐guerrero MA, Callejas‐cuervo M, Mazzoleni S. Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review. Journal of Sensors (Basel). 2021;21(6):1–30; doi: 10.3390/S21062146.
Hashemi A, Lin Y, McNally W. Integration of Machine Learning with Dynamics and Control: From Autonomous Cars to Biomechatronics. Canadian Society for Mechanical Engineering (CSME) Bulletin, 2019;9-10
Novak D, Riener R. Control Strategies and Artificial Intelligence in Rehabilitation Robotics. AI Magazine 2015;36(4):23–33; doi: 10.1609/AIMAG.V36I4.2614.
Lex JR, Di Michele J, Koucheki R, et al. Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis. Journal of the American Medical Association (JAMA) Network Open. 2023;6(3):E233391; doi: 10.1001/JAMANETWORKOPEN.2023.3391.
Shi L, Wang XC, Wang YS. Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China. Brazilian Journal of Medical and Biological Research. 2013;46(11):993; doi: 10.1590/1414-431X20132948.
Lin CC, Ou YK, Chen SH, et al. Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture. Injury 2010;41(8):869–873; doi: 10.1016/J.INJURY.2010.04.023.
Debaun MR, Chavez G, Fithian A, et al. Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning. Journal of the American Academy of Orthopaedic Surgeons. 2021;29(22):977–983; doi: 10.5435/JAAOS-D-20-00429.
Karnuta JM, Navarro SM, Haeberle HS, et al. Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model. Journal of Orthopaedic Trauma. 2019;33(7):324–330; doi: 10.1097/BOT.0000000000001454.
Zhong H, Wang B, Wang D, et al. The application of machine learning algorithms in predicting the length of stay following femoral neck fracture. International Journal of Clinical Practice. 2021;155; doi: 10.1016/J.IJMEDINF.2021.104572.
Scheer JK, Smith JS, Schwab F, et al. Development of a preoperative predictive model for major complications following adult spinal deformity surgery. Journal of Neurosurgery: Spine. 2017;26(6):736–743; doi: 10.3171/2016.10.SPINE16197.
Peng L, Lan L, Xiu P, et al. Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient. Frontiers in Bioengineering and Biotechnology. 2020;8; doi: 10.3389/FBIOE.2020.559387/FULL.
Yagi M, Hosogane N, Fujita N, et al. Predictive model for major complications 2 years after corrective spine surgery for adult spinal deformity. European Spine Journal. 2019;28(1):180–187; doi: 10.1007/S00586-018-5816-5.
Chidambaram S, Maheswaran Y, Patel K, et al. Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation. Journal of Sensors (Basel). 2022;22(18); doi: 10.3390/S22186920.
Liao WJ, Lee KT, Chiang LY, et al. Postoperative Rehabilitation after Anterior Cruciate Ligament Reconstruction through Telerehabilitation with Artificial Intelligence Brace during COVID-19 Pandemic. Journal of Clinical Medicine. 2023;12(14):4865; doi: 10.3390/JCM12144865.
Mallow GM, Hornung A, Barajas JN, et al. Quantum Computing: The Future of Big Data and Artificial Intelligence in Spine. Spine Surgery and Related Research. 2022;6(2):93; doi: 10.22603/SSRR.2021-0251.
Referanslar
Densen P. Challenges and Opportunities Facing Medical Education. Transactions of the American Clinical and Climatological Association.2011;122:48.
Hashimoto DA, Rosman G, Rus D, et al. Artificial Intelligence in Surgery: Promises and Perils. Annals of Surgery. 2018;268(1):70; doi: 10.1097/SLA.0000000000002693.
Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education.2023;23(1):1–15; doi: 10.1186/S12909-023-04698-Z.
Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal 2019;6(2):94–98; doi: 10.7861/FUTUREHOSP.6-2-94.
Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. Journal of the American Medical Association (JAMA). 2016;316(22):2402–2410; doi: 10.1001/JAMA.2016.17216.
Hannun AY, Rajpurkar P, Haghpanahi M, et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine. 2019;25(1):65–69; doi: 10.1038/s41591-018-0268-3.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542(7639):115–118; doi: 10.1038/nature21056.
Vaish A, Migliorini F, Vaishya R. Artificial intelligence in foot and ankle surgery: current concepts. Orthopadie (Heidelberg, Germany) 2023;52(12):1011; doi: 10.1007/S00132-023-04426-X.
Myers TG, Ramkumar PN, Ricciardi BF, et al. Artificial Intelligence and Orthopaedics: An Introduction for Clinicians. Journal of Bone and Joint Surgery. 2020;102(9):830; doi: 10.2106/JBJS.19.01128.
Vasey B, Ursprung S, Beddoe B, et al. Association of Clinician Diagnostic Performance With Machine Learning–Based Decision Support Systems: A Systematic Review. Journal of the American Medical Association (JAMA) Network Open. 2021;4(3); doi: 10.1001/JAMANETWORKOPEN.2021.1276.
Pinto-Coelho L. How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications. Bioengineering 2023;10(12); doi: 10.3390/BIOENGINEERING10121435.
Gyftopoulos S, Lin D, Knoll F, et al. Artificial Intelligence in Musculoskeletal Imaging: Current Status and Future Directions. American journal of roentgenology. 2019;213(3):506; doi: 10.2214/AJR.19.21117.
Langerhuizen DWG, Bulstra AEJ, Janssen SJ, et al. Is Deep Learning On Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid? Clinical Orthopaedics and Related Research. 2020;478(11):2653; doi: 10.1097/CORR.0000000000001318.
Borjali A, Chen AF, Sodhi A, et al. Detecting mechanical loosening of total hip replacement implant from plain radiograph using deep convolutional neural network. Journal of orthopaedic research. 2019; 7(38): 1465-1471; doi:10.1002/jor.24617
Xie X, Li Z, Bai L, et al. Deep Learning‐Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury. Scientific Programming, 2021; 1-8 ; doi: 10.1155/2021/9935910.
Park CW, Oh SJ, Kim KS, et al. Artificial intelligence-based classification of bone tumors in the proximal femur on plain radiographs: System development and validation. Public Library of Science (PLOS) One. 2022;17(2); doi: 10.1371/JOURNAL.PONE.0264140.
Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nature Reviews Cancer. 2018;18(8):500; doi: 10.1038/S41568-018-0016-5.
Swiecicki A, Li N, O’Donnell J, et al. Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. Computers in Biology and Medicine. 2021;133; doi: 10.1016/J.COMPBIOMED.2021.104334.
Park JJ, Tiefenbach J, Demetriades AK. The role of artificial intelligence in surgical simulation. Frontiers in Medical Technology. 2022;4:1076755; doi: 10.3389/FMEDT.2022.1076755/BIBTEX.
Lambrechts A, Wirix-Speetjens R, Maes F, et al. Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty. Frontiers in Robotics and AI. 2022;9; doi: 10.3389/FROBT.2022.840282/FULL.
Zhou X, Zhang D, Xie Z, et al. Application of preoperative 3D printing in the internal fixation of posterior rib fractures with embracing device: a cohort study. BMC Surgery. 2023;23(1); doi: 10.1186/S12893-023-02128-X.
Wong RMY, Wong PY, Liu C, et al. 3D printing in orthopaedic surgery: A scoping review of randomized controlled trials. Bone & Joint Research. 2021;10(12):807–819; doi: 10.1302/2046-3758.1012.BJR-2021-0288.R2.
Hasan S, Ahmed A, Waheed MA, et al. Transforming Orthopedic Joint Surgeries: The Role of Artificial Intelligence (AI) and Robotics. Cureus 2023;15(8); doi: 10.7759/CUREUS.43289.
Fan M, Zhang Q, Fang Y, et al. Robotic solution for orthopedic surgery. Chinese Medical Journal. 2023;136(12):1387; doi: 10.1097/CM9.0000000000002702.
Zhang H, Huang C, Wang D, et al. Artificial Intelligence in Scoliosis: Current Applications and Future Directions. Journal of Clinical Medicine. 2023;12(23):7382; doi: 10.3390/JCM12237382.
Tian W, Liu Y jun, Liu B, et al. Guideline for Thoracolumbar Pedicle Screw Placement Assisted by Orthopaedic Surgical Robot. Orthopaedic Surgery. 2019;11(2):153; doi: 10.1111/OS.12453.
Hou C, Yang H, Chen Y, et al. Comparison of robot versus fluoroscopy-assisted pedicle screw instrumentation in adolescent idiopathic scoliosis surgery: A retrospective study. Frontiers in Surgery. 2022;9; doi: 10.3389/FSURG.2022.1085580.
Ravali RS, Vijayakumar TM, Santhana Lakshmi K, et al. A systematic review of artificial intelligence for pediatric physiotherapy practice: Past, present, and future. Neuroscience Informatics 2022;2(4):100045; doi: 10.1016/J.NEURI.2022.100045.
Vélez‐guerrero MA, Callejas‐cuervo M, Mazzoleni S. Artificial Intelligence-Based Wearable Robotic Exoskeletons for Upper Limb Rehabilitation: A Review. Journal of Sensors (Basel). 2021;21(6):1–30; doi: 10.3390/S21062146.
Hashemi A, Lin Y, McNally W. Integration of Machine Learning with Dynamics and Control: From Autonomous Cars to Biomechatronics. Canadian Society for Mechanical Engineering (CSME) Bulletin, 2019;9-10
Novak D, Riener R. Control Strategies and Artificial Intelligence in Rehabilitation Robotics. AI Magazine 2015;36(4):23–33; doi: 10.1609/AIMAG.V36I4.2614.
Lex JR, Di Michele J, Koucheki R, et al. Artificial Intelligence for Hip Fracture Detection and Outcome Prediction: A Systematic Review and Meta-analysis. Journal of the American Medical Association (JAMA) Network Open. 2023;6(3):E233391; doi: 10.1001/JAMANETWORKOPEN.2023.3391.
Shi L, Wang XC, Wang YS. Artificial neural network models for predicting 1-year mortality in elderly patients with intertrochanteric fractures in China. Brazilian Journal of Medical and Biological Research. 2013;46(11):993; doi: 10.1590/1414-431X20132948.
Lin CC, Ou YK, Chen SH, et al. Comparison of artificial neural network and logistic regression models for predicting mortality in elderly patients with hip fracture. Injury 2010;41(8):869–873; doi: 10.1016/J.INJURY.2010.04.023.
Debaun MR, Chavez G, Fithian A, et al. Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning. Journal of the American Academy of Orthopaedic Surgeons. 2021;29(22):977–983; doi: 10.5435/JAAOS-D-20-00429.
Karnuta JM, Navarro SM, Haeberle HS, et al. Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model. Journal of Orthopaedic Trauma. 2019;33(7):324–330; doi: 10.1097/BOT.0000000000001454.
Zhong H, Wang B, Wang D, et al. The application of machine learning algorithms in predicting the length of stay following femoral neck fracture. International Journal of Clinical Practice. 2021;155; doi: 10.1016/J.IJMEDINF.2021.104572.
Scheer JK, Smith JS, Schwab F, et al. Development of a preoperative predictive model for major complications following adult spinal deformity surgery. Journal of Neurosurgery: Spine. 2017;26(6):736–743; doi: 10.3171/2016.10.SPINE16197.
Peng L, Lan L, Xiu P, et al. Prediction of Proximal Junctional Kyphosis After Posterior Scoliosis Surgery With Machine Learning in the Lenke 5 Adolescent Idiopathic Scoliosis Patient. Frontiers in Bioengineering and Biotechnology. 2020;8; doi: 10.3389/FBIOE.2020.559387/FULL.
Yagi M, Hosogane N, Fujita N, et al. Predictive model for major complications 2 years after corrective spine surgery for adult spinal deformity. European Spine Journal. 2019;28(1):180–187; doi: 10.1007/S00586-018-5816-5.
Chidambaram S, Maheswaran Y, Patel K, et al. Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation. Journal of Sensors (Basel). 2022;22(18); doi: 10.3390/S22186920.
Liao WJ, Lee KT, Chiang LY, et al. Postoperative Rehabilitation after Anterior Cruciate Ligament Reconstruction through Telerehabilitation with Artificial Intelligence Brace during COVID-19 Pandemic. Journal of Clinical Medicine. 2023;12(14):4865; doi: 10.3390/JCM12144865.
Mallow GM, Hornung A, Barajas JN, et al. Quantum Computing: The Future of Big Data and Artificial Intelligence in Spine. Spine Surgery and Related Research. 2022;6(2):93; doi: 10.22603/SSRR.2021-0251.
