ArticleOrthodontics & craniofacial research2026
Evaluation of a Facial Dysmorphology Analysis Algorithm (Face2Gene) in Identifying Treacher Collins Syndrome Amongst Diverse Population.
Article in Orthodontics & craniofacial research, 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTreacher Collins Syndrome (TCS) is an uncommon congenital disease of the craniofacial complex. While there are 'classic' facial manifestations of TCS, they present with a wide range of variability. Face2Gene (F2G) is a deep-learning algorithm that can provide differential diagnoses of syndromes via analysis of 2-dimensional facial images. This may aid early recognition of TCS, thus improving early multidisciplinary management. Our primary aim is to evaluate and compare the performance of F2G in TCS patients of various races. The secondary aim will be to correlate the pathognomonic facial features of these TCS patients with the diagnostic stratification by F2G.
methodsPublicly available images of TCS patients of White (n = 81), Chinese (n = 23), and Indian (n = 23) race were sourced for and screened prior to inclusion for documentation of facial dysmorphological features and F2G evaluation. The diagnostic sensitivity of F2G and computed gestalt score of TCS for each image were then derived. Statistical analysis for correlation between clinical features and F2G-derived gestalt scores for the TCS patients was performed.
resultsA total of 127 images of TCS patients were analysed by F2G. A high diagnostic accuracy of 93.7% was obtained. However, F2G was less confident when diagnosing Asians with TCS than White individuals (p < 0.05). Malar hypoplasia was less prevalent in Indian TCS patients (p < 0.001). However, multidimensionality reduction showed no significant differences across the racial groups. Certain facial features were associated with higher model certainty in TCS diagnosis.
conclusionF2G is a useful diagnostic adjunct for TCS. Nonetheless, further training on non-White datasets may help improve model certainty in diagnosis.
Indexed as
Identifiers
What 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.