Research in Health and Medical Sciences
Sat, Aug 15, 2026
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Volume 4, Issue 4 (10-2025)
JRHMS 2025, 4(4): 21-27 |
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Ghanbarloo A. Applications of machine learning in predicting complications and revision rates following total hip arthroplasty: A narrative literature review. JRHMS 2025; 4 (4) :21-27
URL: http://jrhms.thums.ac.ir/article-1-171-en.html
URL: http://jrhms.thums.ac.ir/article-1-171-en.html
Department of Operating Room, School of Allied Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
Abstract: (3 Views)
Total hip arthroplasty (THA) is one of the most successful orthopaedic operations. However, the increased volume of these treatments is accompanied by an increased number of post-operative problems and revision rates. Traditional statistical models for risk stratification sometimes fail to account for complicated non-linear interactions among patient-specific variables. Thus, machine learning (ML), a subset of artificial intelligence, has been proposed as a viable method in the orthopedic literature to optimize predictive accuracy. The purpose of this narrative review was to critically appraise the current research on the application of machine learning approaches to predict short-term problems, long-term implant survival, and revision rates after THA. A literature search of relevant publications on the development and clinical validation of ML algorithms was performed in PubMed and Scopus. A review of the literature available shows that ML models, and specifically ensemble methods such as Random Forest and Gradient Boosting, have outperformed traditional logistic regression in predicting surgical site infections, dislocations, and readmissions, and have produced robust survival analyses for long-term implant failure. However, currently, these benefits are constrained by the reliance on retrospective data, algorithmic bias and the ‘black-box’ character of sophisticated models that limit the broad clinical integration of ML. ML has the transformative potential to tailor preoperative counseling, but additional prospective, multicenter studies using comprehensible artificial intelligence (XAI) are required to ensure safe clinical adoption.
Keywords: Machine learning, Total hip arthroplasty, Complication prediction, Revision arthroplasty, Artificial intelligence.
Type of Study: Narrative Review |
Subject:
General
Received: 2026/06/22 | Accepted: 2026/07/8 | Published: 2026/08/15
Received: 2026/06/22 | Accepted: 2026/07/8 | Published: 2026/08/15
References
1. Rasmussen MB, El-Galaly A, Daugberg L, Nielsen PT, Jakobsen T. Projection of primary and revision hip arthroplasty surgery in Denmark from 2020 to 2050. Acta Orthop. 2022;93:849-53. [DOI:10.2340/17453674.2022.5255]
2. Amiri F, Haghi S, Khachian A, Hosseini SA, Azadi N. The effect of professional ethics training on the awareness and attitude of operating room nurses. Perioperative Care and Operating Room Management. 2025;41:100564. [DOI:10.1016/j.pcorm.2025.100564]
3. Egerci OF, Yapar A, Dogruoz F, Selcuk H, Kose O. Preventive strategies to reduce the rate of periprosthetic infections in total joint arthroplasty; a comprehensive review. Arch Orthop Trauma Surg. 2024;144(12):5131-46. [DOI:10.1007/s00402-024-05301-w]
4. Hosseini SA, Amiri F, Hanani S, Rashidi N, Azadi N. Manual environmental disinfection with hydrogen peroxide and silver ions versus sodium hypochlorite in operating rooms: a multi-centre randomized trial on residual antimicrobial efficacy. Journal of Hospital Infection. 2026;172:150-6. [DOI:10.1016/j.jhin.2026.02.007]
5. Farash Khayalo H, Bahlgerdi M, Habibi A, Yektay S. Air pollution as a silent driver of osteoporosis: A narrative review of vitamin D impairment and skeletal vulnerability in female populations. Journal of Research in Health and Medical Sciences. 2024;3(4):8-29.
6. Abbasnia A, Bahlgerdi M, Khayalo HF, Bazrafshan E, Moeini Z. Assessment of Carbon Emissions Associated with Artificial Intelligence: A Narrative Review of Data Center Environmental Impacts and Green AI Strategies. Journal of Research in Health and Medical Sciences. 2024;3(1):29-42.
7. Yurtlu DA, Aksun M, Ayvat P, Karahan N, Koroglu L, Aran G. Comparison of Risk Scoring Systems to Predict the Outcome in ASA-PS V Patients Undergoing Surgery: A Retrospective Cohort Study. Medicine (Baltimore). 2016;95(13):e3238. [DOI:10.1097/MD.0000000000003238]
8. Hosseini SA, Pourdel B, Rajabi E, Alizadeh A. Surgical Time-Outs: Ritual or Real Safety Practice? Perioperative Care and Operating Room Management. 2025:100573. [DOI:10.1016/j.pcorm.2025.100573]
9. Zhang M, Zheng Y, Maidaiti X, Liang B, Wei Y, Sun F. Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review. Health Data Sci. 2024;4:0165. [DOI:10.34133/hds.0165]
10. Hosseini SA, Rajabi E, Pourdel B. The Empty Chair: OR Nurse Retention as a Patient Safety Imperative. Perioperative Care and Operating Room Management. 2026:100645. [DOI:10.1016/j.pcorm.2026.100645]
11. Fahim YA, Hasani IW, Kabba S, Ragab WM. Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. Eur J Med Res. 2025;30(1):848. [DOI:10.1186/s40001-025-03196-w]
12. Pourdel B, Hosseini SA, Rajabi E. Clinical and structural outcomes of hybrid operating rooms in adult cardiac surgery: a systematic review. Perioperative Care and Operating Room Management. 2026;42:100607. [DOI:10.1016/j.pcorm.2025.100607]
13. Khayalo HF, Yeganeh M, Esrafili A. Critical review on photocatalytic removal of Ciprofloxacin from water and wastewater: Implications for environmental management and wastewater treatment. Critical review. 2024;3(4).
14. Hosseini SA, Pourdel B, Rajabi E, Farash Khayalo H. Efficacy of photocatalytic processes for hospital environmental disinfection: A systematic review. Journal of Research in Health and Medical Sciences. 2025;4(2):11-31.
15. Khan AA, Chaudhari O, Chandra R. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Systems with Applications. 2024;244:122778. [DOI:10.1016/j.eswa.2023.122778]
16. Pourdel B, Hosseini SA, Rajabi E, Alizadeh A. Alarm fatigue in the operating room: A synthesis of the dual threat from device overload and ambient noise. Perioperative Care and Operating Room Management. 2026;42:100624. [DOI:10.1016/j.pcorm.2026.100624]
17. Wongsak S, Janyawongchot T, Sri-Utenchai N, Owasirikul D, Jaovisidha S, Woratanarat P, et al. Development of a Deep Learning Model for Hip Arthroplasty Templating Using Anteroposterior Hip Radiograph. Journal of Clinical Medicine. 2025;14(24):8689. [DOI:10.3390/jcm14248689]
18. Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Med Inform. 2019;7(2):e12239. [DOI:10.2196/12239]
19. Khayalo HF, Bahlgerdi M, Habibi A. Efficiency of photocatalytic processes in the removal of the antibiotic ciprofloxacin from aqueous environments: A systematic review.
20. Farash Khayalo H, Bahlgerdi M, Esrafili A. Emerging AI-Driven Methodologies for Rapid Environmental Health Assessment in Crisis Conditions: A Narrative Review. Journal of Research in Health and Medical Sciences. 2024;3(3):38-48.
21. Khayalo HF, Bahlgerdi M, Esrafili A. Novel artificial intelligence-based approaches for rapid environmental health assessment in crisis situations: A narrative review. health.12:14.
22. Heo KY, Rajan PV, Khawaja S, Barber LA, Yoon ST. Machine learning approach to predict venous thromboembolism among patients undergoing multi-level spinal posterior instrumented fusion. J Spine Surg. 2024;10(2):214-23. [DOI:10.21037/jss-24-8]
23. Farash Khayalo H, Rajabi E, Yeganeh M. The role of Polycyclic Aromatic Hydrocarbons (PAHs) in urban air as neuroendocrine disruptors in the development of menstrual mood disorders: A narrative review. Journal of Research in Health and Medical Sciences. 2024;3(3):17-27.
24. Farash Khayalo H, Aabbasnia A, Yeganeh M, Rajabi E, Hosseini SA, Pourdel B, et al. Sustainable management of operating room wastes from the perspective of environmental health engineering: A critical review emphasizing strategies for the prevention of healthcare-associated infections. Journal of Research in Health and Medical Sciences. 2025;4(1):14-25.
25. Çetin O, Öztürk A, Avci Ö, Kaya A, Çevik N, Akalin Y. Spinopelvic assessment in preoperative planning of total hip arthroplasty: a comparative cohort analysis. Acta Orthop Traumatol Turc. 2025;59(5):286-91. [DOI:10.5152/j.aott.2025.24065]
26. Khayalo HF, Bahlgerdi M, Yeganeh M, Esrafili A, Abbasnia A. Systematic review of the efficiency of photocatalytic processes for doxycycline antibiotic removal from aqueous media. 2026.
27. Sharda M, Sharma S, Raikar S, Verhagen N, Wagle J, Mathur R, et al. The Role of Machine Learning in Predicting Hospital Readmissions Among General Internal Medicine Patients: A Systematic Review. Cureus. 2025;17(5):e84761. [DOI:10.7759/cureus.84761]
28. Austin PC, Giardiello D. The Impact of Violation of the Proportional Hazards Assumption on the Calibration of the Cox Proportional Hazards Model. Stat Med. 2025;44(13-14):e70161. [DOI:10.1002/sim.70161]
29. Kar İ, Kocaman G, İbrahimov F, Enön S, Coşgun E, Elhan AH. Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients. Cancer Med. 2023;12(18):19272-8. [DOI:10.1002/cam4.6479]
30. Foster A, Beeharry MW. Periprosthetic Proximal Femoral Fractures: A Comprehensive Review of Epidemiology, Risk Factors, Classification and Management. Cureus. 2026;18(1):e100589. [DOI:10.7759/cureus.100589]
31. Suleman MU, Mursaleen M, Khalil U, Saboor A, Bilal M, Khan SA, et al. Assessing the generalizability of artificial intelligence in radiology: a systematic review of performance across different clinical settings. Ann Med Surg (Lond). 2025;87(12):8803-11. [DOI:10.1097/MS9.0000000000004166]
32. Ahmed MI, Spooner B, Isherwood J, Lane M, Orrock E, Dennison A. A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus. 2023;15(10):e46454. [DOI:10.7759/cureus.46454]
33. Sheu RK, Pardeshi MS. A Survey on Medical Explainable AI (XAI): Recent Progress, Explainability Approach, Human Interaction and Scoring System. Sensors (Basel). 2022;22(20). [DOI:10.3390/s22208068]
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