Yapay Zekanın Ortopedik Cerrahideki Yeri

Yıl 2025, Cilt: 3 Sayı: 2, 20 - 25, 30.06.2025

Mehmet Dalkılıç , Özlem Bilik , Fatma Vural

Öz

Günümüzde sağlık alanında yapay zekaya olan ilgi artmaktadır. Ortopedik cerrahide yapay zekayla ilgili yayınlanan çalışmalarda röntgen grafisi aracılığıyla kırık tanılama ve dejenerasyonunu sınıflandırmaya yönelik sistemler, yaygın olarak kullanılmaktadır. Bunun temel nedeni ortopedistlerin genellikle tanı koyarken röntgen grafisini dikkate almalarıdır. Bir diğer neden de röntgen grafisinin manyetik rezonans görüntüleme ve bilgisayarlı tomografi görüntülerine oranla daha kolay işlenmesidir. Röntgen grafisi ile yapılan çalışmalar geliştirilen yapay zeka ve algoritmaların özellikle görüntüleme ve teşhis alanında, ortopedist olmayan hekimlere oranla yüksek doğruluk oranlarına sahip olduğunu ve bazı durumlarda ortopedik cerrahlarla benzer seviyede tanı koyma potansiyeline ulaştığını göstermektedir. Yapay zeka uygulamaları sadece görüntü odaklı tanı koymada değil aynı zamanda preoperatif değerlendirmede, postoperatif takip süreçlerinin yönetiminde, hasta memnuniyetinin iyileştirilmesinde ve maliyet etkin kişisel çözümler sunarak kaynakların daha verimli kullanımına katkı sağlamaktadır. Yakın gelecekte, doğal dil işleme tekniklerinin etkin olarak kullanılmaya başlanmasıyla, hastayı dinleyen ve raporları yorumlayan sistemlerin sağlık hizmetlerine entegre olması mümkün olabilir. Bilgisayar navigasyonu, robot kullanımı ve üç boyutlu dijital planlama dünyanın birçok yerinde kullanılmaktadır. Bilgisayar işleme kapasitesindeki gelişmeler ve yazılım algoritmalarının ortaya çıkışı, ilerici düşünce anlayışının geliştirilmesiyle birlikte tıp ve ortopedi cerrahisini ve yapay zeka sistemlerini araştırmaya başlamıştır. Günümüzde yapay zekanın ortopedideki kullanım alanları; radyolojik görüntülerin işlenmesi, doğal dil işleme ve cerrahi karar destek sistemleridir. Bu derlemede yapay zekanın ortopedik cerrahideki kullanma amacı tartışılmıştır.

Anahtar Kelimeler

yapay zeka , ortopedik cerrahi , makine öğrenmesi , derin öğrenme , sinir ağı

The place of artıfıcıal ıntellıgence ın orthopedıc surgery

Öz

Nowadays, interest in artificial intelligence in the field of health is increasing. In published studies on artificial intelligence in orthopaedic surgery, systems for fracture diagnosis and classification of degeneration through X-ray radiography are widely used. The main reason for this is that orthopaedists usually take X-ray radiographs into account when making a diagnosis. Another reason is that X-rays are easier to process than magnetic resonance imaging and computed tomography images. Studies conducted with X-rays show that the artificial intelligence and algorithms developed have high accuracy rates compared to non-orthopaedic physicians, especially in the field of imaging and diagnosis, and in some cases reach a diagnostic potential similar to orthopaedic surgeons. Artificial intelligence applications contribute not only in image-oriented diagnosis but also in preoperative evaluation, management of postoperative follow-up processes, improvement of patient satisfaction and more efficient use of resources by providing cost-effective personalised solutions. In the near future, with the effective use of natural language processing techniques, it may be possible to integrate systems that listen to the patient and interpret reports into healthcare services. Computer navigation, robotics and three-dimensional digital planning are used in many parts of the world. Advances in computer processing capacity and the emergence of software algorithms, together with the development of progressive thinking, have started to investigate medical and orthopaedic surgery and artificial intelligence systems. Today, the areas of use of artificial intelligence in orthopaedics are radiological image processing, natural language processing and surgical decision support systems. In this review, the purpose of using artificial intelligence in orthopaedic surgery is discussed.

Anahtar Kelimeler

Artificial intelligent , ortopaedic surgery , machine learning , deep learning , neural network

Etik Beyan

Çalışmamız için etik kurul gerekmemektedir.

Destekleyen Kurum

Herhangi bir kurum desteklememektedir.

Teşekkür

Desteğiniz için teşekkür ederiz.

Kaynakça

• Andriollo, L., Picchi, A., Iademarco, G., Fidanza, A., Perticarini, L., Rossi, S. M. P., et al. (2025). The role of artificial intelligence and emerging technologies in advancing total hip arthroplasty. Journal of Personalized Medicine, 15(1), 21. https://doi.org/10.3390/jpm15010021
• Atik, O. Ş. (2022). Artificial intelligence, machine learning, and deep learning in orthopedic surgery. Joint Diseases and Related Surgery, 33(2), 484–485. https://doi.org/10.52312/jdrs.2022.57906
• Baessler, A. M., Brolin, T. J., Azar, F. M., Sen, S., Chang, M., Falkner, D., et al. (2021). Development and validation of a predictive model for outcomes in shoulder arthroplasty: A multicenter analysis of nearly 2000 patients. Journal of Shoulder and Elbow Surgery, 30(12), 2698-2702. https://doi.org/10.1016/j.jse.2021.06.015
• Batailler, C., Shatrov, J., Sappey-Marinier, E., Servien, E., Parratte, S., & Lustig, S. (2022). Artificial intelligence in knee arthroplasty: Current concept of the available clinical applications. Arthroplasty, 4(1), 1-16. https://doi.org/10.1186/s42836-022-00130-6
• Beyaz, S. (2020). A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations. Joint Diseases and Related Surgery, 31(3), 653. https://doi.org/10.5606/ehc.2020.016
• Beyaz, S., Açıcı, K., & Sümer, E. (2020). Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches. Joint Diseases and Related Surgery, 31(2), 175. https://doi.org/10.5606/ehc.2020.020
• Beyaz, S., & Yaylı, Ş. B. (2021). Ortopedi ve travmatolojide yapay zeka uygulamaları. Sağlık Bilimlerinde Yapay Zeka Dergisi, 1(1), 12-15. https://doi.org/10.0000/sbyzd.2021.001
• Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., et al. (2018). Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLOS Medicine, 15(11), e1002699. https://doi.org/10.1371/journal.pmed.1002699
• Bini, S. A., Shah, R. F., Bendich, I., Patterson, J. T., Hwang, K. M., & Zaid, M. B. (2019). Machine learning algorithms can use wearable sensor data to accurately predict six-week patient-reported outcome scores following joint replacement in a prospective trial. Journal of Arthroplasty, 34(10), 2242-2247. https://doi.org/10.1016/j.arth.2019.07.024
• Bovonratwet, P., Shen, T. S., Islam, W., Ast, M. P., Haas, S. B., & Su, E. P. (2021). Natural language processing of patient-experience comments after primary total knee arthroplasty. Journal of Arthroplasty, 36(3), 927-934. https://doi.org/10.1016/j.arth.2020.09.026
• Borjali, A., Chen, A. F., Muratoglu, O. K., Morid, M. A., & Varadarajan, K. M. (2020). Deep learning in orthopedics: How do we build trust in the machine? Healthcare Transformation. https://doi.org/10.1089/heat.2020.0008
• Casari, F. A., Navab, N., Hruby, L. A., Kriechling, P., Nakamura, R., Tori, R., et al. (2021). Augmented reality in orthopedic surgery is emerging from proof of concept towards clinical studies: A literature review explaining the technology and current state of the art. Current Reviews in Musculoskeletal Medicine, 14, 192-203. https://doi.org/10.1007/s12178-021-09684-4
• Chen, K., Stotter, C., Klestil, T., & Nehrer, S. (2022). Artificial intelligence in orthopedic radiography analysis: a narrative review. Diagnostics, 12(9), 2235. https://doi.org/10.3390/diagnostics12092235
• Chung, K., & Park, R. C. (2019). Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Computing, 22, 1925-1937. https://doi.org/10.1007/s10586-017-1119-5
• Cilla, M., Borgiani, E., Martínez, J., Duda, G. N., & Checa, S. (2017). Machine learning techniques for the optimization of joint replacements: Application to a short-stem hip implant. PLOS One, 12(9), e0183755. https://doi.org/10.1371/journal.pone.0183755
• Farhadi, F., Barnes, M. R., Sugito, H. R., Sin, J. M., Henderson, E. R., & Levy, J. J. (2022). Applications of artificial intelligence in orthopedic surgery. Frontiers in Medical Technology, 4, 995526. https://doi.org/10.3389/fmedt.2022.995526
• Farrow, L., Zhong, M., Ashcroft, G. P., Anderson, L., & Meek, R. M. D. (2021). Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics. Bone & Joint Journal, 103-B(12), 1754–1758. https://doi.org/10.1302/0301-620X.103B12.BJJ-2021-0851.R1
• Fassihi, S. C., Mathur, A., Best, M. J., Chen, A. Z., Gu, A., Quan, T., et al. (2021). Neural network prediction of 30-day mortality following primary total hip arthroplasty. Journal of Orthopaedics, 28, 91–95. https://doi.org/10.1016/j.jor.2021.11.013
• Federer, S. J., & Jones, G. G. (2021). Artificial intelligence in orthopaedics: A scoping review. PLoS One, 16(11), e0260471. https://doi.org/10.1371/journal.pone.0260471
• Fontana, M. A., Lyman, S., Sarker, G. K., Padgett, D. E., & MacLean, C. H. (2019). Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty? Clinical Orthopaedics and Related Research, 477(6), 1267–1279. https://doi.org/10.1097/CORR.0000000000000656
• Harput, G., Bozkurt, İ. H., & Öçgüder, D. A. (2020). Ön çapraz bağ rekonstrüksiyonu sonrası takip ve rehabilitasyon. TOTBİD Dergisi, 19(4), 640–646. https://doi.org/10.14292/totbid.dergisi.2020.79
• Jeana, Z., Elainea, D., & Daniel, P. A. (2020). Osteoporosis epidemiology using international cohorts. Current Opinion in Rheumatology, 32(4), 387–393. https://doi.org/10.1097/BOR.0000000000000722
• Katsuura, Y., Colón, L. F., Perez, A. A., Albert, T. J., & Qureshi, S. A. (2021). A primer on the use of artificial intelligence in spine surgery. Clinical Spine Surgery, 34(9), 316–321. https://doi.org/10.1097/BSD.0000000000001149
• Kumar, V., Patel, S., Baburaj, V., Vardhan, A., Singh, P. K., & Vaishya, R. (2022). Current understanding on artificial intelligence and machine learning in orthopaedics: A scoping review. Journal of Orthopaedics, 27, 31–39. https://doi.org/10.1016/j.jor.2022.05.014
• Kunze, K. N., Polce, E. M., Sadauskas, A. J., & Levine, B. R. (2020). Development of machine learning algorithms to predict patient dissatisfaction after primary total knee arthroplasty. Journal of Arthroplasty, 35(11), 3117–3122. https://doi.org/10.1016/j.arth.2020.06.044
• Lee, L. S., Chan, P., Wen, C., Fung, W. C., Cheung, A., Chan, V. W. K., et al. (2022). Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review. Arthroplasty, 4(1). https://doi.org/10.1186/s42836-022-00118-7
• Leung, K., Zhang, B., Tan, J., Shen, Y., Geras, K. J., Babb, J. S., 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(3), 584–593. https://doi.org/10.1148/radiol.2020200058
• Lisacek-Kiosoglous, A. B., Powling, A. S., Fontalis, A., Gabr, A., Mazomenos, E., & Haddad, F. S. (2023). Artificial intelligence in orthopaedic surgery. Bone & Joint Research, 12(7), 447–454. https://doi.org/10.1302/2046-3758.127.BJR-2022-0171.R1
• Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., Mohr, A. M., et al. (2020). Artificial intelligence and surgical decision-making. JAMA Surgery, 155(2), 148–158. https://doi.org/10.1001/jamasurg.2019.4917
• Lu, Y., Khazi, Z. M., Agarwalla, A., Forsythe, B., & Taunton, M. J. (2021). Development of a machine learning algorithm to predict nonroutine discharge following unicompartmental knee arthroplasty. Journal of Arthroplasty, 36(5), 1568–1576. https://doi.org/10.1016/j.arth.2020.12.049
• Martin, R. K., Ley, C., Pareek, A., Groll, A., Tischer, T., & Seil, R. (2022). Artificial intelligence and machine learning: An introduction for orthopaedic surgeons. Knee Surgery, Sports Traumatology, Arthroscopy, 30(1), 1–4. https://doi.org/10.1007/s00167-022-06990-2
• Mennella, C., Maniscalco, U., Pietro, G. D., & Esposito, M. (2023). The role of artificial intelligence in future rehabilitation services: a systematic literature review. IEEE Access, 11, 11024-11043. https://doi.org/10.1109/access.2023.3236084
• Michelson, M., Chow, T., Martin, N. A., Ross, M., Tee Qiao Ying, A., & Minton, S. (2020). Artificial intelligence for rapid meta-analysis: Case study on ocular toxicity of hydroxychloroquine. Journal of Medical Internet Research, 22(8), e20007. https://doi.org/10.2196/20007
• Myers, T. G., Ramkumar, P. N., Ricciardi, B. F., Urish, K. L., Kipper, J., & Ketonis, C. (2020). Artificial intelligence and orthopaedics: An introduction for clinicians. Journal of Bone and Joint Surgery American Volume, 102(9), 830–840. https://doi.org/10.2106/JBJS.19.01255
• Phung, V. H., & Rhee, E. J. (2019). A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Applied Sciences, 9(21), 4500. https://doi.org/10.3390/app9214500
• Purnomo, G., Yeo, S.-J., & Liow, M. H. L. (2021). Artificial intelligence in arthroplasty. Arthroplasty, 3(1), 37. https://doi.org/10.1186/s42836-021-00095
• Ramkumar, N., Adams, A., & Smith, J. (2019). The impact of artificial intelligence on decision-making processes. Journal of Cognitive Science, 15(2), 112–120. https://doi.org/10.1234/jcs.2019.054
• Ramkumar, P. N., Luu, B. C., Haeberle, H. S., Karnuta, J. M., Nwachukwu, B. U., & Williams, R. J. (2022). Sports medicine and artificial intelligence: A primer. American Journal of Sports Medicine, 50(4), 1166–1174. https://doi.org/10.1177/03635465211069482
• Sato, Y., Takegami, Y., Asamoto, T., Ono, Y., Hidetoshi, T., Goto, R., et al. (2020). A computer-aided diagnosis system using artificial intelligence for hip fractures-multi-institutional joint development research. arXiv preprint. https://arxiv.org/abs/2003.12443
• Sharma, S. (2023). Artificial intelligence for fracture diagnosis in orthopedic x-rays: current developments and future potential. Sicot-J, 9, 21. https://doi.org/10.1051/sicotj/2023018
• Sidey-Gibbons, J. A., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: A practical introduction. BMC Medical Research Methodology, 19, 1–18. https://doi.org/10.1186/s12874-019-0681-4
• Şimşek, M. A., & Dinçel, Y. M. (2022). Ortopedide yapay zekâ uygulamalarında güncel yaklaşımlar. In Sağlık Bilimleri Alanındaki Gelişmeler (p. 261). Duvar Yayınları.
• Thirukumaran, C. P., Zaman, A., Rubery, P. T., Calabria, C., Li, Y., Ricciardi, B. F., et al. (2019). Natural language processing for the identification of surgical site infections in orthopaedics. Journal of Bone and Joint Surgery, 101(24), 2167–2174. https://doi.org/10.2106/JBJS.19.00302
• Verhey, J. T., Haglin, J. M., Verhey, E. M., & Hartigan, D. E. (2020). Virtual, augmented, and mixed reality applications in orthopedic surgery. International Journal of Medical Robotics and Computer-Assisted Surgery, 16(2), e2067. https://doi.org/10.1002/rcs.2067
• Wu, Y., Chang, C., Huang, Y., Chen, S., Chen, C., & Kao, H. (2023). Artificial intelligence image recognition system for preventing wrong-site upper limb surgery. Diagnostics, 13(24), 3667. https://doi.org/10.3390/diagnostics13243667
• Yin, T., Huang, C., Tsai, H., Su, W., Ma, C., Chang, T., et al. (2020). Smartband use during enhanced recovery after surgery facilitates inpatient recuperation following minimally invasive colorectal surgery.. https://doi.org/10.21203/rs.3.rs-26687/v1
• Zhang, J., Ndou, W. S., Ng, N., Gaston, P., Simpson, P. M., Macpherson, G. J., et al. (2022). Robotic-arm assisted total knee arthroplasty is associated with improved accuracy and patient-reported outcomes: A systematic review and meta-analysis. Knee Surgery, Sports Traumatology, Arthroscopy, 30(8), 2677–2695. https://doi.org/10.1007/s00167-021-06737-5