ArticleBeijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences2026
[Application of sample-efficient neural network in predicting postoperative facial profiles in skeletal class Ⅱ orthognathic patients].
Article in Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSoft tissue prediction is of great importance for the surgical design and patients ' satisfaction in orthognathic surgery. Previous studies either rely on elastic mechanical models, such as finite element which involves time-consuming computations, or using artificial intelligence method which require insatiable large patient data. Therefore, in this study, we proposed a more efficient and lightweight method for facial morphology prediction and validated its accuracy.
methodsA total of 85 skeletal class Ⅱ patients who underwent double jaw surgery performed by the same senior surgeon were included in the study. Three-dimensional (3D) facial images were acquired before and 6 months after surgery. Among them, 72 data sets were utilized to train the neural network. The Basel face model (BFM), a 3D morphable face model, was employed to parameterically reduce the dimensionality of the three-dimensional facial data. An artificial neural network (ANN) was trained to learn the mapping relationship between the preoperative and postoperative latent codes. The predicted postoperative latent code was subsequently decoded through BFM to reconstruct the three-dimensional facial morphology and gene-rate the predicted postoperative facial appearance. Thirteen patients excluded from the training set were used as the test set. The postoperative facial soft tissue morphology obtained from each patient was considered as the ground truth. The postoperative facial predictions generated by proposed neural network model were defined as the experimental group, while predictions generated by the orthognathic surgical planning software ProPlan CMF were used as the control group. The prediction accuracy was quantitatively evaluated by calculating the root mean square error (RMSE), mean absolute error (MAE), and the percentage of points with prediction errors within ±1 mm and ±2 mm, namely the inlier overlap percen-tages [IO
resultsThe experimental group exhibited a mean RMSE of (0.96±0.15) mm and MAE of (0.84±0.12) mm for the entire face. The IO
conclusionThe accuracy of a soft-tissue prediction system using neural network system based on BFM is clinically feasible.
Indexed as
Identifiers
42880914PMC13672219What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.