Journal Article Review Epublish 5 citations
PubMed: 40710415 DOI: 10.3390/jpm15070298
Computational and Imaging Approaches for Precision Characterization of Bone, Cartilage, and Synovial Biomolecules.
J Pers Med 15(7)
Authors: Rahul Kumar, Kyle Sporn, Vibhav Prabhakar, Ahab Alnemri, Akshay Khanna, Phani Paladugu, Chirag Gowda, Louis Clarkson, Nasif Zaman, Alireza Tavakkoli
Affiliations
- Department of Biochemistry and Molecular Biology, School of Medicine, University of Miami Miller, 1600 NW 10th Avenue, Miami, FL 33136, USA.
- Norton College of Medicine, Upstate Medical University, Syracuse, NY 13210, USA.
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL 60607, USA.
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA 19144, USA.
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
- School of Medicine, University of Cambridge, Cambridge CB2 1TN, UK.
- Human-Machine Perception Laboratory, Department of Computer Science, University of Nevada Reno, Reno, NV 89557, USA.
Abstract
Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care.
MeSH
Genes, Joint Diseases, Multiple Chronic Conditions, Deep Learning, Molecular Imaging, Tissues, Bone Matrix, Cartilage, Spatial Transcriptomics