Diagnosis of rare diseases based on facial phenotype: a quantitative assessment using 2D and 3D photography in Stickler syndrome

Objective and Quantitative Assessment of Facial Vitiligo via AI-powered 3D Analysis for Diagnostic Support.

ML in Rare Facial Disorders: Hemifacial Microsomia, Parry-Romberg Syndrome, Moebius Syndrome, Treacher Collins Syndrome, Apert Syndrome, and Crouzon Syndrome-From Etiological Mapping and Pathology to AI-Driven Bio-Computational Gene Therapy

There is sufficient evidence indicating the benefits of AI in increasing the accuracy in diagnosis, objective craniofacial evaluation, and customized treatment plans, whereas computational therapy is still experimental, and future studies need to concentrate on multicenter data sharing, multimodal explainable AI, precision genomics, and translational framework.

Lesion Expansion in Craniofacial Fibrous Dysplasia: Natural History and Indicators of Progression in the Frontal, Zygomatic, and Sphenoid Bones.

Fibrous dysplasia (FD) is characterized by expansile fibro-osseous lesions that may occur in association with endocrinopathies as part of McCune-Albright syndrome (MAS). Craniofacial FD may result in substantial morbidity, including facial asymmetry and compression of vital neurovascular structures. There is a critical need to understand the natural history and risk factors for craniofacial lesion expansion to develop preventative trials and identify candidates for intervention. The purpose of this study was to evaluate expansion rates in periorbital bones (frontal, zygomatic, and sphenoid). Patients with craniofacial FD and serial CT imaging were evaluated. Volumetric analyses were performed and generalized mixed model analysis was used to investigate risk factors associated with expansion. Age, MAS-associated endocrinopathies, sex, Skeletal Burden Score, and history of bisphosphonate treatment were evaluated. A previous dataset for the gnathic region was included in statistical analyses to expand upon prior findings. 238 lesions (46 frontal, 43 zygomatic, 45 sphenoid, 41 mandible, 63 maxilla) in 70 patients were evaluated. Frontal, zygomatic, and sphenoid lesion volume increased with age (p<0.001), however expansion rates decreased over time (p<0.001). Both age-related expansion and lesion growth deceleration were highly location dependent, with frontal lesions experiencing the most rapid changes compared to other regions. Patients with growth hormone excess demonstrated greater lesion expansion rates (p=0.0173). There were no associations between sex and other MAS-associated endocrinopathies, or bisphosphonate treatment. Craniofacial lesion expansion rates are most rapid in younger children (generally below age 10) and decline as patients approach adulthood. FD lesions involving the frontal bone expand at a greater rate than sphenoid, zygomatic, and previously- reported gnathic lesions. These differences in growth rates indicate that location-specific analyses are critical to assess FD progression. The availability of quantitative natural history data will guide clinicians in identifying candidates for interventions, and will inform the development of clinical trials for preventative therapies.

PRACTICAL DERMATOLOGY Lentigo Maligna: Keys to Dermoscopic Diagnosis (cid:2)

Diagnostic performance of artificial intelligence for orthodontic and dentofacial assessment using 2D and 3D facial images: A systematic review.

INTRODUCTION Artificial intelligence (AI) has been increasingly applied to facial-image analysis in orthodontics, with potential applications in radiation-free screening, dentofacial assessment, and treatment-decision support. This systematic review evaluated the diagnostic and predictive performance of AI models applied to two-dimensional (2D) and three-dimensional (3D) facial data. METHODS The protocol was registered in PROSPERO (CRD420261419145). PubMed, Embase, Scopus, Web of Science, Cochrane Library, ClinicalTrials.gov, Google Scholar, and Open Science Framework were searched. Eligible studies evaluated AI models using facial photographs, multi-view photographs, 3D facial scans, reconstructed facial models, or landmark-coordinate inputs for downstream diagnostic, predictive, classification, or treatment-decision tasks. Studies limited exclusively to landmark localization were excluded. Data were synthesized qualitatively. Risk of bias was assessed with QUADAS-2 and certainty of evidence with GRADE. RESULTS Fourteen studies were included. Applications comprised cephalometric prediction, skeletal-pattern classification, mandibular-deformity diagnosis, soft-tissue depth assessment, treatment-difficulty classification, and orthognathic-surgery-need prediction. Across classification studies, accuracy ranged from 73.1% to 97.7%, AUC from 0.768 to 0.987, sensitivity or recall from 64.3% to 97.3%, and specificity from 80.5% to 95.6%. Only one study performed true external validation. Methodological heterogeneity, predominantly internal evaluation, and low overall certainty limited clinical translation. CONCLUSION AI-based analysis of 2D and 3D facial data showed promising performance for selected orthodontic and dentofacial tasks. Current evidence supports its use as an adjunct to specialist assessment rather than as an autonomous diagnostic substitute. PROSPERO REGISTRATION CRD420261419145.

Scalp-Ear-Nipple Syndrome (SEN) in Pediatric Patients With Multiple Co-morbidities: A Case Report

This case series highlights phenotypic variability in SEN syndrome, including a novel co-occurrence with Klinefelter syndrome, including a novel co-occurrence with Klinefelter syndrome.