Publications
2025
Turkestani, Najla Al; Cevidanes, Lucia; Bianchi, Jonas; Sugai, James; Gurgel, Marcela; Prieto, Juan; Hatfield, Elizabeth; Philips, Kristine; Benavides, Erika; Soki, Fabiana; Mishina, Yuji; Fontana, Margherita; Rao, Arvind; Zhu, Hongtu; Li, Tengfei
In: Osteoarthritis and Cartilage, vol. 33, iss. 12, pp. 1522-1533, 2025, ISSN: 1063-4584.
Abstract | Links | BibTeX | Tags: Cartilage degeneration, Diagnostic biomarkers, osteoarthritis, Subchondral bone remodeling
@article{nokey,
title = {Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model},
author = {Najla Al Turkestani and Lucia Cevidanes and Jonas Bianchi and James Sugai and Marcela Gurgel and Juan Prieto and Elizabeth Hatfield and Kristine Philips and Erika Benavides and Fabiana Soki and Yuji Mishina and Margherita Fontana and Arvind Rao and Hongtu Zhu and Tengfei Li},
url = {https://www.sciencedirect.com/science/article/pii/S1063458425011124},
doi = {https://doi.org/10.1016/j.joca.2025.08.002},
issn = {1063-4584},
year = {2025},
date = {2025-12-01},
journal = {Osteoarthritis and Cartilage},
volume = {33},
issue = {12},
pages = {1522-1533},
abstract = {Objective: Osteoarthritis (OA) is a pan-joint degenerative disorder characterized by cartilage degradation and subchondral bone remodeling. The temporomandibular joint (TMJ) offers a unique model for early OA due to its anatomy and early-onset disease. Current diagnostics rely on late-stage changes, underscoring the need for biomarker integration. We hypothesized that machine learning (ML) combining imaging, molecular, and clinical data would improve diagnostic accuracy, and that SHapley Additive exPlanations (SHAP) would clarify key predictors and interactions, enhancing mechanistic understanding of disease heterogeneity. Design: A case-control study of 162 participants (81 TMJ OA and 81 age- and sex-matched controls) integrated clinical, high-resolution imaging (radiomics, trabecular architecture, joint space), and systemic/articular biomarkers (serum and saliva). Seventy-seven ML combinations were evaluated via nested 10-fold cross-validation. Results: The final ensemble model achieved strong diagnostic performance (AUC=0.828, 95% CI: 0.757–0.892). SHAP analysis revealed top predictors such as headache severity, trabecular thickening, restless sleep, muscle soreness, limited mouth opening and joint space narrowing. Mechanistic interactions captured early inflammatory, structural, and neurovascular changes, including radiomics-cartilage degradation links (e.g., condyle grey level nonuniformity with saliva CXCL-16), clinical-molecular associations (e.g., headaches with saliva VE-cadherin), and subchondral microstructure correlations (e.g., grey level nonuniformity with run length nonuniformity). Conclusions: This study presents a clinically useful, explainable AI model for OA diagnosis. Key predictors and cross-domain interactions improved accuracy and clarified early disease mechanisms. Although cross-validation minimized overfitting risk, external validation is needed. These findings support biomarker-driven precision diagnostics and highlight multi-tissue predictors as potential targets for early OA intervention.},
keywords = {Cartilage degeneration, Diagnostic biomarkers, osteoarthritis, Subchondral bone remodeling},
pubstate = {published},
tppubtype = {article}
}
Objective: Osteoarthritis (OA) is a pan-joint degenerative disorder characterized by cartilage degradation and subchondral bone remodeling. The temporomandibular joint (TMJ) offers a unique model for early OA due to its anatomy and early-onset disease. Current diagnostics rely on late-stage changes, underscoring the need for biomarker integration. We hypothesized that machine learning (ML) combining imaging, molecular, and clinical data would improve diagnostic accuracy, and that SHapley Additive exPlanations (SHAP) would clarify key predictors and interactions, enhancing mechanistic understanding of disease heterogeneity. Design: A case-control study of 162 participants (81 TMJ OA and 81 age- and sex-matched controls) integrated clinical, high-resolution imaging (radiomics, trabecular architecture, joint space), and systemic/articular biomarkers (serum and saliva). Seventy-seven ML combinations were evaluated via nested 10-fold cross-validation. Results: The final ensemble model achieved strong diagnostic performance (AUC=0.828, 95% CI: 0.757–0.892). SHAP analysis revealed top predictors such as headache severity, trabecular thickening, restless sleep, muscle soreness, limited mouth opening and joint space narrowing. Mechanistic interactions captured early inflammatory, structural, and neurovascular changes, including radiomics-cartilage degradation links (e.g., condyle grey level nonuniformity with saliva CXCL-16), clinical-molecular associations (e.g., headaches with saliva VE-cadherin), and subchondral microstructure correlations (e.g., grey level nonuniformity with run length nonuniformity). Conclusions: This study presents a clinically useful, explainable AI model for OA diagnosis. Key predictors and cross-domain interactions improved accuracy and clarified early disease mechanisms. Although cross-validation minimized overfitting risk, external validation is needed. These findings support biomarker-driven precision diagnostics and highlight multi-tissue predictors as potential targets for early OA intervention.
Turkestani, Najla Al; Cevidanes, Lucia; Bianchi, Jonas; Sugai, James; Gurgel, Marcela; Prieto, Juan; Hatfield, Elizabeth; Philips, Kristine; Benavides, Erika; Soki, Fabiana; Mishina, Yuji; Fontana, Margherita; Rao, Arvind; Zhu, Hongtu; Li, Tengfei
Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model Journal Article
In: Osteoarthritis and Cartilage, vol. 33, iss. 12, pp. 1522-1533, 2025, ISSN: 1063-4584.
@article{nokey,
title = {Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model},
author = {Najla Al Turkestani and Lucia Cevidanes and Jonas Bianchi and James Sugai and Marcela Gurgel and Juan Prieto and Elizabeth Hatfield and Kristine Philips and Erika Benavides and Fabiana Soki and Yuji Mishina and Margherita Fontana and Arvind Rao and Hongtu Zhu and Tengfei Li},
url = {https://www.sciencedirect.com/science/article/pii/S1063458425011124},
doi = {https://doi.org/10.1016/j.joca.2025.08.002},
issn = {1063-4584},
year = {2025},
date = {2025-12-01},
journal = {Osteoarthritis and Cartilage},
volume = {33},
issue = {12},
pages = {1522-1533},
abstract = {Objective: Osteoarthritis (OA) is a pan-joint degenerative disorder characterized by cartilage degradation and subchondral bone remodeling. The temporomandibular joint (TMJ) offers a unique model for early OA due to its anatomy and early-onset disease. Current diagnostics rely on late-stage changes, underscoring the need for biomarker integration. We hypothesized that machine learning (ML) combining imaging, molecular, and clinical data would improve diagnostic accuracy, and that SHapley Additive exPlanations (SHAP) would clarify key predictors and interactions, enhancing mechanistic understanding of disease heterogeneity. Design: A case-control study of 162 participants (81 TMJ OA and 81 age- and sex-matched controls) integrated clinical, high-resolution imaging (radiomics, trabecular architecture, joint space), and systemic/articular biomarkers (serum and saliva). Seventy-seven ML combinations were evaluated via nested 10-fold cross-validation. Results: The final ensemble model achieved strong diagnostic performance (AUC=0.828, 95% CI: 0.757–0.892). SHAP analysis revealed top predictors such as headache severity, trabecular thickening, restless sleep, muscle soreness, limited mouth opening and joint space narrowing. Mechanistic interactions captured early inflammatory, structural, and neurovascular changes, including radiomics-cartilage degradation links (e.g., condyle grey level nonuniformity with saliva CXCL-16), clinical-molecular associations (e.g., headaches with saliva VE-cadherin), and subchondral microstructure correlations (e.g., grey level nonuniformity with run length nonuniformity). Conclusions: This study presents a clinically useful, explainable AI model for OA diagnosis. Key predictors and cross-domain interactions improved accuracy and clarified early disease mechanisms. Although cross-validation minimized overfitting risk, external validation is needed. These findings support biomarker-driven precision diagnostics and highlight multi-tissue predictors as potential targets for early OA intervention.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective: Osteoarthritis (OA) is a pan-joint degenerative disorder characterized by cartilage degradation and subchondral bone remodeling. The temporomandibular joint (TMJ) offers a unique model for early OA due to its anatomy and early-onset disease. Current diagnostics rely on late-stage changes, underscoring the need for biomarker integration. We hypothesized that machine learning (ML) combining imaging, molecular, and clinical data would improve diagnostic accuracy, and that SHapley Additive exPlanations (SHAP) would clarify key predictors and interactions, enhancing mechanistic understanding of disease heterogeneity. Design: A case-control study of 162 participants (81 TMJ OA and 81 age- and sex-matched controls) integrated clinical, high-resolution imaging (radiomics, trabecular architecture, joint space), and systemic/articular biomarkers (serum and saliva). Seventy-seven ML combinations were evaluated via nested 10-fold cross-validation. Results: The final ensemble model achieved strong diagnostic performance (AUC=0.828, 95% CI: 0.757–0.892). SHAP analysis revealed top predictors such as headache severity, trabecular thickening, restless sleep, muscle soreness, limited mouth opening and joint space narrowing. Mechanistic interactions captured early inflammatory, structural, and neurovascular changes, including radiomics-cartilage degradation links (e.g., condyle grey level nonuniformity with saliva CXCL-16), clinical-molecular associations (e.g., headaches with saliva VE-cadherin), and subchondral microstructure correlations (e.g., grey level nonuniformity with run length nonuniformity). Conclusions: This study presents a clinically useful, explainable AI model for OA diagnosis. Key predictors and cross-domain interactions improved accuracy and clarified early disease mechanisms. Although cross-validation minimized overfitting risk, external validation is needed. These findings support biomarker-driven precision diagnostics and highlight multi-tissue predictors as potential targets for early OA intervention.
2025 |
Turkestani, Najla Al; Cevidanes, Lucia; Bianchi, Jonas; Sugai, James; Gurgel, Marcela; Prieto, Juan; Hatfield, Elizabeth; Philips, Kristine; Benavides, Erika; Soki, Fabiana; Mishina, Yuji; Fontana, Margherita; Rao, Arvind; Zhu, Hongtu; Li, Tengfei: Interpretable machine learning integrates multi-source biomarkers for osteoarthritis diagnosis and mechanistic insights: A temporomandibular joint model. In: Osteoarthritis and Cartilage, vol. 33, iss. 12, pp. 1522-1533, 2025, ISSN: 1063-4584. (Type: Journal Article | Abstract | Links | BibTeX | Tags: Cartilage degeneration, Diagnostic biomarkers, osteoarthritis, Subchondral bone remodeling)@article{nokey,Objective: Osteoarthritis (OA) is a pan-joint degenerative disorder characterized by cartilage degradation and subchondral bone remodeling. The temporomandibular joint (TMJ) offers a unique model for early OA due to its anatomy and early-onset disease. Current diagnostics rely on late-stage changes, underscoring the need for biomarker integration. We hypothesized that machine learning (ML) combining imaging, molecular, and clinical data would improve diagnostic accuracy, and that SHapley Additive exPlanations (SHAP) would clarify key predictors and interactions, enhancing mechanistic understanding of disease heterogeneity. Design: A case-control study of 162 participants (81 TMJ OA and 81 age- and sex-matched controls) integrated clinical, high-resolution imaging (radiomics, trabecular architecture, joint space), and systemic/articular biomarkers (serum and saliva). Seventy-seven ML combinations were evaluated via nested 10-fold cross-validation. Results: The final ensemble model achieved strong diagnostic performance (AUC=0.828, 95% CI: 0.757–0.892). SHAP analysis revealed top predictors such as headache severity, trabecular thickening, restless sleep, muscle soreness, limited mouth opening and joint space narrowing. Mechanistic interactions captured early inflammatory, structural, and neurovascular changes, including radiomics-cartilage degradation links (e.g., condyle grey level nonuniformity with saliva CXCL-16), clinical-molecular associations (e.g., headaches with saliva VE-cadherin), and subchondral microstructure correlations (e.g., grey level nonuniformity with run length nonuniformity). Conclusions: This study presents a clinically useful, explainable AI model for OA diagnosis. Key predictors and cross-domain interactions improved accuracy and clarified early disease mechanisms. Although cross-validation minimized overfitting risk, external validation is needed. These findings support biomarker-driven precision diagnostics and highlight multi-tissue predictors as potential targets for early OA intervention. |