evidencegaps
Primary study · included by 1 review

Deep learning for the classification of cervical maturation degree and pubertal growth spurts: A pilot study.

Mohammad-Rahimi 2022 · Mohammad-Rahimi Hossein, Motamadian Saeed Reza, Nadimi Mohadeseh, et al. · Korean journal of orthodontics · 2022

Included by

Artificial intelligence for predicting the pubertal growth spurt using cephalometric and hand-wrist radiographs: a systematic review and meta-analysis.
Non-Cochrane · DTA · 2026 · cited as Mohammad-Rahimi 2022 · Table 1; reference 37
Outcome this study informsPooled effectEvidenceCertainty
Pooled accuracy
AI models (best model per study) applied to lateral cephalometric and/or hand-wrist radiographs to identify the pubertal growth spurt (three-class pre-spurt, spurt, post-spurt scheme) vs Reference staging by expert assessment (CVM, Fishman SMI, Grave and Brown, DRU or other validated standards)
Accuracy (proportion) 0.83 [0.76, 0.89]21 studiesGRADE not reported

1 including review in total.