Department of Chemistry, Biology and Biotechnology, University of Perugia, 06123, Perugia, Italy.
Reconstructive Orthopaedic Surgery and Innovative Techniques-Musculoskeletal Tissue Bank, IRCCS Istituto Ortopedico Rizzoli, Via G.C. Pupilli 1, 40136, Bologna, Italy.
Sci Rep. 2023 Jan 30;13(1):1690. doi: 10.1038/s41598-023-28735-5.
In this study, Brillouin and Raman micro-Spectroscopy (BRamS) and Machine Learning were used to set-up a new diagnostic tool for Osteoarthritis (OA), potentially extendible to other musculoskeletal diseases. OA is a degenerative pathology, causing the onset of chronic pain due to cartilage disruption. Despite this, it is often diagnosed late and the radiological assessment during the routine examination may fail to recognize the threshold beyond which pharmacological treatment is no longer sufficient and prosthetic replacement is required. Here, femoral head resections of OA-affected patients were analyzed by BRamS, looking for distinctive mechanical and chemical markers of the progressive degeneration degree, and the result was compared to standard assignment via histological staining. The procedure was optimized for diagnostic prediction by using a machine learning algorithm and reducing the time required for measurements, paving the way for possible future in vivo characterization of the articular surface through endoscopic probes during arthroscopy.
在这项研究中，布里渊和拉曼微光谱（BRamS）以及机器学习被用于建立一种新的骨关节炎（OA）诊断工具，该工具可能适用于其他肌肉骨骼疾病。OA 是一种退行性病变，会导致软骨破坏引起慢性疼痛。尽管如此，OA 通常被诊断得较晚，常规检查中的放射学评估可能无法识别出药物治疗不再有效的阈值，需要进行假体置换。在这里，通过 BRamS 分析了受 OA 影响的股骨头切除术，寻找渐进性退变程度的独特机械和化学标记物，并将结果与通过组织学染色的标准分配进行比较。通过使用机器学习算法进行诊断预测和减少测量所需的时间，对该程序进行了优化，为将来通过关节镜检查的内窥镜探头对关节表面进行可能的体内特征分析铺平了道路。
