Automatic detection of pharyngeal fricatives in cleft palate speech using acoustic features based on the vocal tract area spectrum

Are Children with Cleft Palate at Increased Risk for Laryngeal Pathology?

To determine the prevalence of laryngeal pathology in children presenting with cleft palate with or without cleft lip (CP ± L) who underwent nasoendoscopy to assess palatal function. A secondary aim was to determine the relationship between patient demographics, resonance, articulation, and prevalence of laryngeal pathology in this population.

Retrospective, observational cohort study.

Outpatient pediatric cranio-facial anomalies clinic.

Children ≤18 years of age presenting with CP ± L (N = 215) who underwent nasoendoscopy, speech language pathology, plastic surgery, and otolaryngological evaluations between 2009 and 2020.

Laryngeal diagnosis by pediatric otolaryngologists.

21.9% of children presented with laryngeal pathology. Diagnoses included benign vocal fold lesions and laryngeal edema sufficiently severe to alter vocal fold edge contour. Likelihood of laryngeal pathology increased by approximately 12% with every increase of 1 year in age (P = .001, OR = 1.12). Children with laryngeal pathology were 50% more likely to have undergone palatal repair (P < .001, OR = 1.50). In addition, children with severely hypernasal resonance were 78% less likely to present with laryngeal pathology (P =.046, OR = 0.22).

This population is at increased risk for laryngeal pathologies as determined by nasoendoscopy. This finding underscores the importance of careful laryngeal imaging in assessing these children. Additional research is warranted to identify the mechanisms underlying the increased risk for morphological vocal fold changes.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.