Feasibility of CBCT-based deep learning for predicting 3D soft tissue changes after orthognathic surgery in skeletal class III patients.
This study aimed to evaluate the feasibility of a cone-beam computed tomography (CBCT)-based deep learning (DL) model for predicting three-dimensional (3D) soft tissue changes following orthognathic surgery in skeletal Class III patients. Vector distance analysis revealed region-dependent discrepancies, indicating that estimation precision varied according to local anatomical complexity.
This study aimed to evaluate the feasibility of a cone-beam computed tomography (CBCT)-based deep learning (DL) model for predicting three-dimensional (3D) soft tissue changes following orthognathic surgery in skeletal Class III patients. Vector distance analysis revealed region-dependent discrepancies, indicating that estimation precision varied according to local anatomical complexity.
What the study was
- Study design
- Cohort study
- Sample size
- 64
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Scientific reports
Why it surfaced
Relevant AI/ML in clinical diagnostics and imaging study (promising preliminary signal) meets standard-priority threshold.
A plain-language summary of published research — not medical advice. Talk to a clinician about your own care.