| 引用本文: | 裴志伟,田爱现,张葆鑫,苗族康,贾敬坤,白磊,杨璐,蒋宁,马信龙.基于人工智能与多模态数据融合构建膝关节周围截骨术的智能诊疗新范式[J].中国临床新医学,0,():-. |
| Pei Zhiwei.基于人工智能与多模态数据融合构建膝关节周围截骨术的智能诊疗新范式[J].中国临床新医学,0,():-. |
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| 摘要: |
| 膝关节周围截骨术是治疗单间室骨关节炎、矫正下肢力线的关键保膝技术体系，其术式多样，包括胫骨高位截骨术（High Tibial Osteotomy, HTO，开放楔形与闭合楔形）、股骨远端截骨术（Distal Femoral Osteotomy, DFO）以及双部位联合截骨等。传统诊疗方法在解决“如何为具体患者选择和规划最佳术式”的关键临床决策时，只能通过有限的经验和二维影像分析，无法对各个术式可能产生的复杂三维生物力学改变及其对应的特定并发症风险进行统一的量化和比较。人工智能、多模态数据融合技术的快速发展，为破解该问题、实现从术前规划、风险预警到术后管理的全面精准化提供了新的空间。本文着重阐述一种全新的智能诊疗范式，它利用患者全方位的解剖、生物力学和临床数据，建立能够模拟、评估和优化不同截骨策略的智能决策支持系统，实现对不同术式的个性化选择、手术风险的量化预测、手术的精准规划和精准执行、基于具体术式的术后预测。重点阐述其在多术式决策、并发症防护、复杂生物力学仿真、手术精准实施、个体化预后预测方面的优势、具体技术路径和存在的主要问题，并展望其能为保膝手术的数字化与精准化提供前景。 |
| 关键词: 智能骨科 膝关节周围截骨术 多模态数据融合 数字孪生 |
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| 基金项目:国家重点研发项目（批准号：2022YFC3601900） |
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| Building a New Paradigm of Intelligent Diagnosis and Treatment for Knee Osteotomy Based on Artificial Intelligence and Multimodal Data Fusion |
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Pei Zhiwei
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Tianjin Hospital, Tianjin University
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| Abstract: |
| Knee osteotomy is a key joint-preserving technique for treating unicompartmental osteoarthritis and correcting lower limb alignment. It includes various procedures, such as high tibial osteotomy (HTO, open-wedge and closed-wedge), distal femoral osteotomy (DFO), and combined two-level osteotomy. Traditional diagnostic and treatment methods rely on limited experience and two-dimensional imaging analysis when addressing the critical clinical decision of "how to select and plan the optimal procedure for a specific patient." They cannot uniformly quantify and compare the complex three-dimensional biomechanical changes and corresponding risks of complications associated with different procedures. The rapid development of artificial intelligence and multimodal data fusion technologies offers new opportunities to address this challenge, enabling comprehensive precision in preoperative planning, risk warning, and postoperative management. This article focuses on a novel intelligent diagnostic and treatment paradigm. It leverages comprehensive patient data—including anatomical, biomechanical, and clinical information—to establish an intelligent decision support system capable of simulating, evaluating, and optimizing different osteotomy strategies. This system aims to achieve personalized procedure selection, quantitative prediction of surgical risks, precise planning and execution of surgery, and postoperative prognosis prediction tailored to specific procedures. The article highlights its advantages, specific technical pathways, and key challenges in multi-procedure decision-making, complication prevention, complex biomechanical simulation, precise surgical implementation, and individualized outcome prediction. It also envisions its potential to advance the digitalization and precision of joint-preserving surgeries. |
| Key words: Intelligent Orthopedics Pericapsular osteotomy Multimodal Data Fusion Digital Twin |
