The goals of this project are: 1) to investigate a novel framework for biomarkers of early stages of osteoarthritis based the temporomandibular joint condyles subchondral bone texture computed from high-resolution Cone- Beam Computed Tomography; 2) to develop a novel computational algorithm, the Bone texture tool, that extends the capabilities of 3D Slicer image analysis open-source software; and 3) oversee the training and dissemination of these tools to the dental research community. Our preliminary work has allowed us to precisely localize and quantify the extent of subchondral bone degenerative changes in the mandibular condyles. The novel bone texture methodology included in this proposal will provide specific analytical tools for the detection, pathology characterization and treatment monitoring of diseases of arthritic origin. Building from this, the proposed mapping of the subchondral architecture of the osteoarthritic condyles using imaging criteria (such as entropy, energy, contrast, homogeneity and gray-level run-lengths) is an excellent model to facilitate detection of early stages of osteoarthritic changes, to monitor treatment outcomes, and to provide the foundation for the development of joint deterioration prevention strategies. This proposed research benefits from the combined efforts of a team of clinicians, and computer scientist and statistical modeling expert. This research team brings special resources to enable the broader objective of developing an infrastructure for image analysis to be used in leading-edge dental clinical research and practice.
The inability to quantify phenotypes of abnormal subchondral bone texture is a severe bottleneck in understanding early stages of osteoarthritis. This proposal will alleviate this information bottleneck by quantifying subchondral bone biomarkers that have the potential to identify patients at risk for further bone destruction. The proposed multi-dimensional texture feature maps of arthritic TMJs will allow improved early diagnosis and better monitoring of treatment outcomes in future studies by generating and disseminating a publicly available set of tools based in 3DSlicer.
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| Vimort, Jean-Baptiste; Ruellas, Antonio; Prothero, Jack et al. (2018) Detection of bone loss via subchondral bone analysis. Proc SPIE Int Soc Opt Eng 10578: |
| Atresh, Arjun; Cevidanes, Lucia H S; Yatabe, Marilia et al. (2018) Three-dimensional treatment outcomes in Class II patients with different vertical facial patterns treated with the Herbst appliance. Am J Orthod Dentofacial Orthop 154:238-248.e1 |
| Shah, Hina; Hernandez, Pablo; Budin, Francois et al. (2018) Automatic quantification framework to detect cracks in teeth. Proc SPIE Int Soc Opt Eng 10578: |
| Paniagua, Beatriz; Pascal, Laura; Prieto, Juan et al. (2017) Diagnostic Index: An open-source tool to classify TMJ OA condyles. Proc SPIE Int Soc Opt Eng 10137: |
| Gomes, L R; Cevidanes, L H; Gomes, M R et al. (2017) Counterclockwise maxillomandibular advancement surgery and disc repositioning: can condylar remodeling in the long-term follow-up be predicted? Int J Oral Maxillofac Surg 46:1569-1578 |
| Prieto, Juan C; Paniagua, Beatriz; Yatabe, Marilia S et al. (2017) Federating Heterogeneous Datasets to Enhance Data Sharing and Experiment Reproducibility. Proc SPIE Int Soc Opt Eng 10137: |
