This study examined the morphometry and morphological variations of the external ear among the Omuma population. Specifically, measured the length of the tragus, breadth of the ear below the tragus, and presence of Darwin's tubercle. The aim is to document and analyze these variations to provide a comprehensive understanding of the external ear morphology within this specific population among omuma group. The study adopted a cross-sectional design, 400 participants aged 16-45 were used using simple random sampling method. For data collection a camera was used to take photographs with a 2cm graph sheet placed behind the ear horizontally and vertically to take reading from the photograph using a computer. Data were analyzed using SPSS version and descriptive studies of frequencies, percentages, mean and standard deviation were used Data analysis. The result of the analysis revealed that majority of the participants were female (227, 56.8%) while male was 173(43.3%) and majority fell between the age of 26-35 years. Further findings showed that the mean length ear of the tragus was 1.42±0.26, while mean breadth of just below the tragus was 3.30±0.53cm and 3.27±0.53cm for left and right ear respectively. This study on ear morphometry in the Omuma population has provided data on the length of the tragus, the breadth of the ear below the tragus, and the prevalence of Darwin’s tubercle. It was recommended that ear morphometric and morphological data gotten from the present study should be utilized in forensic studies for identification purposes. Data will contribute to anthropological research and may have implications for clinical practices related to ear morphology

Cleft lip and/or palate (CLP) involves a multidisciplinary and longitudinal care paradigm that provides a rational climate within which collaborative AI systems can buttress clinical decision-making. This study seeks to summarize how Artificial Intelligence AI has been integrated into CLP management by reviewing relevant publications over the past decade while focusing on the deployment of AI into CLP care along the entire care continuum. A pre-existing systematic review analyzing AI in children with CLP was a Foundational Evidence for the study and narratively updated with more recent pediatric craniofacial and orthognathic literature involving CLP subgroups. Other eligible studies had to have leveraged AI or machine learning for CLP-related tasks including diagnosis, landmarking, segmentation, surgical prediction, presurgical orthopedics, or functional outcome evaluation. The results are summarized by clinical domain. Twelve CLP-preferential studies from the prior systematic review, with a number of related craniofacial studies were reported. Across the domains, models tended to record clinically acceptable errors or good classification, reduced clinician’s manual workload, and provided standardized (and readily shareable) outcomes for multidisciplinary participation. The majority of publications were retrospective and single-center with small sample sizes and scant external validation. Collaborative AI in CLP care has advanced to early clinical workflows along the care continuum, essentially augmenting clinician’s expertise. Equity, ethics, and scalability of implementation will require robust multicenter validation, diverse datasets, governance frameworks, and clinician AI literacy