Primary study · included by 1 review
Quantitative bone imaging biomarkers and joint space analysis of the articular fossa in temporomandibular joint osteoarthritis using artificial intelligence models
Mackie 2022 · Mackie Tamara, Al Turkestani Najla, Bianchi Jonas, et al. · Frontiers in Dental Medicine · 2022
Included by
| Outcome this study informs | Pooled effect | Evidence | Certainty |
|---|---|---|---|
Pooled specificity AI-based diagnostic algorithms (machine learning, deep learning, radiomics) applied to CBCT, OPG, MRI or clinical data vs Traditional diagnostic reference standard (clinical examination per RDC/TMD or DC/TMD, expert radiologist or clinician consensus) | Specificity 0.766 [0.711, 0.814] | 5 studies · 866 participants | GRADE not reported |
Pooled sensitivity AI-based diagnostic algorithms (machine learning, deep learning, radiomics) applied to CBCT, OPG, MRI or clinical data vs Traditional diagnostic reference standard (clinical examination per RDC/TMD or DC/TMD, expert radiologist or clinician consensus) | Sensitivity 0.751 [0.703, 0.794] | 5 studies · 866 participants | GRADE not reported |
Pooled sensitivity AI-based diagnostic algorithms (machine learning, deep learning, radiomics) applied to CBCT, OPG, MRI or clinical data vs Traditional diagnostic reference standard (clinical examination per RDC/TMD or DC/TMD, expert radiologist or clinician consensus) | Change in pooled sensitivity (leave-one-out delta) -0.014 | 4 studies | GRADE not reported |
Pooled specificity AI-based diagnostic algorithms (machine learning, deep learning, radiomics) applied to CBCT, OPG, MRI or clinical data vs Traditional diagnostic reference standard (clinical examination per RDC/TMD or DC/TMD, expert radiologist or clinician consensus) | Change in pooled specificity (leave-one-out delta) 0.036 | 4 studies | GRADE not reported |
1 including review in total.