Total hip arthroplasty preoperative planning for childhood hip disorders' sequelae: Focus on developmental dysplasia of the hip.

IF 2 Q2 ORTHOPEDICS
Saran S Gill, Valerio Pace
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引用次数: 0

Abstract

Developmental dysplasia of the hip (DDH) poses significant challenges in both childhood and adulthood, affecting up to 10 per 1000 live births in the United Kingdom and United States. While newborn screening aims to detect DDH early, missed diagnoses can lead to severe complications such as hip dysplasia and early onset osteoarthritis in adults. Treatment options range from less invasive procedures like hip-preserving surgery to more extensive interventions such as total hip arthroplasty (THA), depending on the severity of the condition. Preoperative planning plays a critical role in optimizing surgical outcomes for DDH patients undergoing THA. This includes accurate imaging modalities, precise measurement of acetabular bone stock, assessment of femoral head subluxation, and predicting prosthesis size and leg length discrepancy. Recent advancements artificial intelligence and machine learning offer promising tools to enhance preoperative planning accuracy. However, challenges remain in validating these technologies and integrating them into clinical practice. This editorial highlights the importance of ongoing research to refine preoperative strategies and improve outcomes in DDH management through evidence-based approaches and technological innovations.

儿童髋关节疾病后遗症的全髋关节置换术术前规划:重点关注髋关节发育不良。
髋关节发育不良(DDH)对儿童和成年都构成重大挑战,在英国和美国,每1000个活产婴儿中就有10个受到影响。虽然新生儿筛查旨在早期发现DDH,但漏诊可能导致严重的并发症,如成人髋关节发育不良和早发性骨关节炎。根据病情的严重程度,治疗选择从微创手术(如髋关节保护手术)到更广泛的干预(如全髋关节置换术)不等。术前计划在DDH患者行THA手术效果优化中起着关键作用。这包括准确的成像方式,精确测量髋臼骨,评估股骨头半脱位,预测假体尺寸和腿长差异。人工智能和机器学习的最新进展为提高术前规划的准确性提供了有前途的工具。然而,在验证这些技术并将其整合到临床实践中仍然存在挑战。这篇社论强调了正在进行的研究通过循证方法和技术创新来完善术前策略和改善DDH管理结果的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
3.10
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814
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