Buccino Federica, Zagra Luigi, Longo Elena, D'Amico Lorenzo, Banfi Giuseppe, Berto Filippo, Tromba Giuliana, Vergani Laura Maria
Department of Mechanical Engineering, Politecnico di Milano, 20156, Italy.
I.R.C.C.S Ospedale Galeazzi - Sant'Ambrogio, Milano 20157, Italy.
Mater Des. 2023 Jul;231:112087. doi: 10.1016/j.matdes.2023.112087. Epub 2023 Jun 11.
While advanced imaging strategies have improved the diagnosis of bone-related pathologies, early signs of bone alterations remain difficult to detect. The Covid-19 pandemic has brought attention to the need for a better understanding of bone micro-scale toughening and weakening phenomena. This study used an artificial intelligence-based tool to automatically investigate and validate four clinical hypotheses by examining osteocyte lacunae on a large scale with synchrotron image-guided failure assessment. The findings indicate that trabecular bone features exhibit intrinsic variability related to external loading, micro-scale bone characteristics affect fracture initiation and propagation, osteoporosis signs can be detected at the micro-scale through changes in osteocyte lacunar features, and Covid-19 worsens micro-scale porosities in a statistically significant manner similar to the osteoporotic condition. Incorporating these findings with existing clinical and diagnostic tools could prevent micro-scale damages from progressing into critical fractures.
虽然先进的成像策略改善了骨相关疾病的诊断，但骨改变的早期迹象仍然难以检测。新冠疫情使人们开始关注更好地理解骨微观尺度的强化和弱化现象的必要性。本研究使用了一种基于人工智能的工具，通过同步加速器图像引导的失效评估大规模检查骨细胞陷窝，自动研究和验证了四个临床假设。研究结果表明，小梁骨特征表现出与外部负荷相关的内在变异性，微观尺度的骨特征影响骨折的起始和扩展，骨质疏松迹象可通过骨细胞陷窝特征的变化在微观尺度上检测到，并且新冠疫情以与骨质疏松状态类似的统计学显著方式使微观尺度孔隙率恶化。将这些发现与现有的临床和诊断工具相结合，可以防止微观损伤发展为严重骨折。
