While cell morphology has been recognized as a fundamental regulator of cell behavior, few studies have measured the complex cell morphological changes of chondrocytes using quantitative cell morphometry descriptors in relation to inflammation and phenotypic outcome.

Acute vs. persistent exposure to IL-1β and how IL-1β modulated dynamic changes in cell morphology in relation to the phenotype, donor and OA grade in healthy and osteoarthritis (OA) chondrocytes was investigated. A panel of quantitative cell morphometry descriptors was measured using an automated high-throughput method. Absolute quantification of gene expression was measured by ddPCR followed by correlation analyses.

In OA chondrocytes, chronic IL-1β significantly decreased COL2A1, SOX9, and ACAN, increased IL-6 and IL-8 levels and caused chondrocytes to become less wide, smaller, longer, slimmer, less round and more circular, consistent with a de-differentiated phenotype. In healthy chondrocytes, 3 days after acute (72 h) IL-1β exposure, COL1A2 and IL-6 significantly increased but had minor effects on cell morphology. However, in healthy chondrocytes, persistent IL-1β led to more profound effects in all cell morphology descriptors and chondrocytes expressed significantly less COL2A1 and more IL-6 and IL-8 vs. controls and acutely-stimulated chondrocytes. In both OA and healthy chronically-stimulated chondrocytes, area, width and circularity were sensitive to the persistent presence of the IL-1β cytokine. Moreover, there were many significant and strong correlations among the measured parameters, with several indications of an IL-1β-mediated mechanism.

Cell morphology combined with gene expression analysis could guide researchers interested in understanding inflammatory effects in the complex domain of cartilage/chondrocyte biology. Use of quantitative cell morphometry could complement classical approaches by providing numerical data on a large number of cells, thereby providing a biological fingerprint for describing chondrocyte phenotype, which could help to understand how changes in cell morphology lead to disease progression.

Polyethylene wear represents a significant risk factor for the long-term success of knee arthroplasty [1]. This work aimed to develop and in vivo validate an automated algorithm for accurate and precise AI based wear measurement in knee arthroplasty using clinical AP radiographs for scientifically meaningful multi-centre studies.

Twenty postoperative radiographs (knee joint AP in standing position) after knee arthroplasty were analysed using the novel algorithm. A convolutional neural network-based segmentation is used to localize the implant components on the X-Ray, and a 2D-3D registration of the CAD implant models precisely calculates the three-dimensional position and orientation of the implants in the joint at the time of acquisition. From this, the minimal distance between the involved implant components is determined, and its postoperative change over time enables the determination of wear in the radiographs.

The measured minimum inlay height of 335 unloaded inlays excluding the weight-induced deformation, served as ground truth for validation and was compared to the algorithmically calculated component distances from 20 radiographs.

With an average weight of 94 kg in the studied TKA patient cohort, it was determined that an average inlay height of 6.160 mm is expected in the patient. Based on the radiographs, the algorithm calculated a minimum component distance of 6.158 mm (SD = 81 µm), which deviated by 2 µm in comparison to the expected inlay height.

An automated method was presented that allows accurate and precise determination of the inlay height and subsequently the wear in knee arthroplasty based on a clinical radiograph and the CAD models. Precision and accuracy are comparable to the current gold standard RSA [2], but without relying on special radiographic setups. The developed method can therefore be used to objectively investigate novel implant materials with meaningful clinical cohorts, thus improving the quality of patient care.