Assessing the intimate mechanobiological link between human bone micro-scale trabecular architecture and micro-damages. (July 2022)

• Record Type: Journal Article
• Authors: Buccino, Federica; Bagherifard, Sara; D'Amico, Lorenzo; Zagra, Luigi; Banfi, Giuseppe; Tromba, Giuliana; Vergani, Laura Maria
• Is Part Of: Engineering fracture mechanics. Volume 270(2022)
• Publication Date: 2022-07
• Subjects: Trabecular bone -- Synchrotron -- Micro-scale fracture mechanics -- Lacunae -- Micro-cracks; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2022.108582
• Languages: English
• ISSNs: 0013-7944

Abstract

Graphical abstract: Highlights: Bone lacunae act as crack deviators predominantly in healthy samples; Osteoporotic samples are characterized by high density of roundish and oblate lacunae; Artificial intelligence is a powerful tool to develop micro-crack recognition systems; With CNN, fracture mechanics parameters can be quantified from synchrotron images; Validated micro-FE models quantify stress intensity factors in human trabecular bones. Abstract: The dramatic increase in fragility fractures and the related health and economic burden rise the urge of a cutting-edge perspective to anticipate catastrophic fracture propagation in human bones. Recent studies address the issue from a multi-scale perspective, elevating the micro-scale phenomena as the key for detecting early damage occurrence. However, several limitations arise specifically for defining a quantitative framework to assess the contribution of lacunar micro-pores to fracture initiation and propagation. Moreover, the need for high resolution imaging imposes time-demanding post-processing phases. Here, we exploit synchrotron scans in combination with micro-mechanical tests, to offer a fracture mechanics-based approach for quantifying the critical stress intensification in healthy and osteoporotic trabecular human bones. This is paired with a morphological and densitometric framework for capturing lacunar network differences in presence of pathological alterations. To address the current time-consuming and computationally expensive manual/semi-automatic segmenting steps, we implement convolutional neural network to detect the initiation and propagation of micro-scale damages. The results highlight the intimate cross talks between toughening and weakening phenomena at micro-scale as a fundamental aspect for fracture prevention.