Artificial intelligence (AI) is highly recommended used in odontology to make dental-image interpretation more automated and aid in human identification. This critical review discusses the recent evidence of the ante-mortem and post-mortem radiographic matching, tooth detection and numbering, dental-age estimation, biological-sex estimation, bite-mark analysis, cheiloscopy, palatal-rugae comparison, recognition of restoration and implant, virtual autopsy, automated charting and multimodal dental-record integration. The dental-age models can yield low estimation errors when compared with sex-estimation models, which still require consideration of population structure and anatomical predictors chosen. New 3-D rugae-matching and lip-print systems are technically feasible but are not widely and independently validated. The limitations inherent in the biology and evidentiary use of bite marks cannot be solved by AI. Common threats include homogeneous datasets, overfitting, patient level data leakage, class imbalance and insufficient multicentre testing across the various application areas. In addition, maps of attention and confidence scores are not in and of themselves interpretable, valid or legally reliable. The age and sex estimation, restoration classification and automated charting are technically validated but not operationally established; and tooth numbering, candidate retrieval, treatment-pattern recognition and post-mortem imaging supports are closest to implementation. The next steps involve future development of prospective open set evaluations, data separation at the identity level, geographic and cross-device validation of digital records and auditable chains of custody. In light of this, AI should be used as a clear decision-making tool, under the supervision of trained forensic dentists who responsible for the interpretation and evidential aspects of the case.

The VIII International Symposium on Orofacial Clefts and Related Anomalies, organized by the Graduate Program in Rehabilitation Sciences at the Bauru Medical School, University of São Paulo (FMBRU-USP), brought together clinicians, researchers, students, and healthcare professionals to discuss recent advances in craniofacial rehabilitation. Held with the I FMBRU-USP Graduate Program Pre-Symposium, the scientific program comprised a keynote lectures on Robin Sequence management protocols, six thematic panels, and a poster session. The panels explored topics on Robin Sequence, digital technologies and artificial intelligence, velopharyngeal insufficiency, rehabilitation outcomes, translational research, and professional training. Across these themes, the symposium reinforced the value of multidisciplinary collaboration, evidence-based practice, emerging technologies, and international partnerships in advancing patient care and scientific innovation. This proceedings paper summarizes the principal discussions and key messages presented during the meeting, providing an overview of current advances, persistent challenges, and future directions in research, education, and the multidisciplinary management of individuals with orofacial clefts and related craniofacial anomalies.

ObjectivePerceptual evaluation by speech-language pathologists (SLPs) is essential for initial evaluation of velopharyngeal dysfunction (VPD). Machine learning (ML) offers a promising avenue for developing accessible speech assessment tools when SLP expertise is limited. We aimed to develop a workflow and preliminary algorithm for AI-based hypernasality detection based on the Cleft Audit Protocol for Speech-Augmented-Americleft Modification (CAPS-A-AM), a standardized framework for auditory perceptual speech assessment.DesignIn this prospective, single-center study, speech samples were collected during SLP-guided evaluation, with consensus CAPS-A-AM ratings established. Mel spectrograms for high vowels (/i/ and /u/) were generated from sustained vowels, isolated words, and sentences for model development. Three ML approaches were evaluated: logistic regression (LR), Convolutional Neural Network (CNN) Attention-Multiple Instance Learning (MIL) (EfficientNet-V2-S), and a CNN-Extreme Gradient Boosting Hybrid (XGBoost).Patients/ParticipantsForty pediatric participants aged 2 to 17, including individuals with VPD, conditions associated with VPD, and healthy participants.Main Outcome Measure(s)Model performance was tested in binary hypernasality classification compared to SLP consensus at two CAPS-A-AM thresholds: absent (0) versus any hypernasality (1-4) and absent/borderline (0-1) versus mild-to-severe hypernasality (2-4).ResultsMultiple independent modeling approaches were able to detect clinically rated hypernasality. EfficientNet-V2-S achieved the highest observed performance estimates in this cohort (F1 score = 0.786-0.900). However, overlapping confidence intervals precluded demonstration of model superiority.ConclusionML-based hypernasality classification using mel spectrograms demonstrated promising feasibility. Future work will expand sample acquisition and refine model development with increasingly diverse speech inputs for training and sample rating.

To describe a novel free gingival graft (FGG) technique, the 'M-saddle FGG technique' for treating non-keratinized tissue distal to a mandibular second molar associated with a suprabony periodontal pocket, inflammation, and patient discomfort. A 30-year-old patient presented with persistent discomfort, bleeding, food impaction, a 6-mm suprabony periodontal pocket distal to tooth #3.7, 1 mm of keratinized tissue width (KTW), and reduced vestibular depth (VD). A modified horseshoe-shaped FGG was harvested from the palate and adapted over the distal crest and buccal periosteum. Outcomes were assessed after 3 months. At 3 months, discomfort, inflammation, and soft tissue trauma during function were completely resolved. KTW and VD both increased by 4 mm, and periodontal probing depths reduced by 3 mm. The 'M-Saddle FGG technique' improved KTW, periodontal parameters, VD, and patient comfort, representing a promising novel treatment option for selected mandibular second molar mucogingival defects and suprabony periodontal pockets.

Background/Objectives: This study aimed to evaluate the volumes of the maxillary sinus, nasal cavity, and nasolacrimal duct in individuals with unilateral and bilateral cleft lip and palate (CLP) using an artificial intelligence-assisted segmentation method and to compare the findings with those of healthy controls. Methods: This retrospective study included 25 unilateral CLP patients, 20 bilateral CLP patients, and 25 healthy controls. Maxillary sinus, nasal cavity, and nasolacrimal duct volumes were measured on cone-beam computed tomography (CBCT) images using an artificial intelligence-assisted segmentation method. Total volume differences among groups were analyzed using analysis of covariance (ANCOVA) adjusted for age and sex. Side-based comparisons were performed using linear mixed-effects models. Benjamini-Hochberg correction was applied for multiple comparisons. Results: The control group exhibited greater maxillary sinus, nasal cavity, and nasolacrimal duct volumes than the CLP groups. ANCOVA demonstrated nominally significant group differences for the maxillary sinus (p = 0.008), nasal cavity (p = 0.034), and nasolacrimal duct (p = 0.023); however, these differences were no longer significant after Benjamini-Hochberg correction (q > 0.05). Pairwise comparisons showed greater maxillary sinus volumes in controls than in the unilateral (p = 0.037) and bilateral CLP groups (p = 0.007), and greater nasal cavity volumes than in the unilateral (p = 0.049) and bilateral CLP groups (p = 0.045). Nasolacrimal duct volume differed only between the bilateral CLP and control groups (p = 0.021). No significant volumetric differences were found between unilateral and bilateral CLP groups. Conclusions: Although individuals with CLP showed a trend toward reduced maxillary sinus, nasal cavity, and nasolacrimal duct volumes, the total-volume differences did not remain significant after correction for multiple comparisons; reductions relative to the control sides persisted after correction only for the maxillary sinus and the nasolacrimal duct in the side-based analysis. No significant differences were observed between unilateral and bilateral CLP or between the two sides within any group.

Agarose is a thermoreversible, highly biocompatible polysaccharide increasingly used in tissue engineering (TE). Its molecular architecture, optical clarity, tunable mechanics, and chemical inertness make agarose hydrogels attractive scaffolds for generating bioartificial tissues by TE. This review summarizes current knowledge on agarose extraction, purification, structural variants, and physicochemical properties regarding gelation behavior, stiffness, porosity, and bioactivity. We discuss how agarose type and concentration critically determine hydrogel biomechanical and optical performance, influencing cell behavior and in vivo suitability. Although biologically inert, agarose can be functionalized or combined with fibrin, collagen, chitosan, and other biomaterials to enhance cell adhesion, proliferation, and differentiation. Diverse biofabrication approaches-including micromolding, bead production, 3D bioprinting, and de novo assembly of cells, biomaterials and bioactive factors-have enabled the generation of microtissues, organoids, and complex multilayered constructs. Agarose-based biomaterials allowed for the successful generation of bioartificial substitutes of cartilage, bone, adipose tissue, skin, cornea, oral mucosa, and the peripheral nerve, with several fibrin-agarose advanced therapy medicinal products (ATMP) already reaching clinical application, including the skin substitute UGRSKIN, the artificial cornea NANOULCOR and the palate mucosa BIOCLEFT. Together, current evidence positions agarose as a versatile and translationally relevant biomaterial for next-generation TE, warranting further exploration of its potential in additional therapeutic contexts.

Secondary nasolabial aesthetic revision after cleft lip surgery requires coordinated assessment of scar-related contracture, lip subunit distortion, and cleft nasal deformity. Traditionally, these 2 aspects have been managed separately. This narrative review summarizes a focused, revision-oriented framework in which scar management is considered as an adjunct to staged nasolabial subunit reconstruction rather than as an isolated therapeutic goal. Early scar-directed measures, including tension reduction, silicone-based therapy, pulsed-dye laser, and selected pharmacologic interventions, may improve tissue quality and reduce contracture before definitive revision; however, their use should be individualized because evidence and timing remain heterogeneous. For established secondary deformities, definitive revision focuses on the philtrum, Cupid's bow, vermilion, alar base, columella, and nasal tip using local flaps, cartilage grafting, soft-tissue rearrangement, and orbicularis oris reorientation. This approach restores 3-dimensional (3D) symmetry and natural landmarks. Digital technologies such as 3D photogrammetry, finite element analysis (FEA), and artificial intelligence (AI) enable objective scar assessment and personalized surgical planning. However, current limitations remain, including the absence of a cleft lip-specific scar scale, insufficient long-term follow-up data, and unresolved timing of early interventions in infants. Overall, this review reframes scar management as one component of secondary nasolabial revision and emphasizes subunit-based reconstruction, individualized timing, and cautious interpretation of the available evidence.