Background: There is a link between diabetic peripheral neuropathy (DPN) progression and the increase in the cross-sectional area (CSA) of the tibial nerve at the ankle. Nevertheless, no prior meta-analysis has been conducted to evaluate its usefulness for the diagnosis of DPN. Methods: We searched Google Scholar, Scopus, and PubMed for potential studies. Studies had to report tibial nerve CSA at the ankle and diabetes status (DM, DPN, or healthy) to be included. A random-effect meta-analysis was applied to calculate pooled tibial nerve CSA and mean differences across the groups. Subgroup and correlational analyses were conducted to study the potential covariates. Results: The analysis of 3295 subjects revealed that tibial nerve CSA was 13.39 mm² (CI: 10.94–15.85) in DM patients and 15.12 mm² (CI: 11.76–18.48) in DPN patients. The CSA was 1.93 mm² (CI: 0.92–2.95, I² = 98.69%, p < 0.01) larger than DPN-free diabetic patients. The diagnostic criteria of DPN and age were also identified as potential moderators of tibial nerve CSA. Conclusions: Although tibial nerve CSA at the ankle was significantly larger in the DPN patients, its clinical usefulness is limited by the overlap between groups and the inconsistency in the criteria used to diagnose DPN.

Background and Objectives: The landmark for neurosurgical approaches to access brain lesion is the pterion. The aim of the present study is to classify and examine the prevalence of all types of pterion variations and perform morphometric measurements from previously defined anthropological landmarks. Materials and methods: One-hundred and twenty-four Thai dried skulls were investigated. Classification and morphometric measurement of the pterion was performed. Machine learning models were also used to interpret the morphometric findings with respect to sex and age estimation. Results: Spheno-parietal type was the most common type (62.1%), followed by epipteric (11.7%), fronto-temporal (5.2%) and stellate (1.2%). Complete synostosis of the pterion suture was present in 18.5% and was only present in males. While most morphometric measurements were similar between males and females, the distances from the pterion center to the mastoid process and to the external occipital protuberance were longer in males. Random forest algorithm could predict sex with 80.7% accuracy (root mean square error = 0.38) when the pterion morphometric data were provided. Correlational analysis indicated that the distances from the pterion center to the anterior aspect of the frontozygomatic suture and to the zygomatic angle were positively correlated with age, which may serve as basis for age estimation in the future. Conclusions: Further studies are needed to explore the use of machine learning in anatomical studies and morphometry-based sex and age estimation. Thorough understanding of the anatomy of the pterion is clinically useful when planning pterional craniotomy, particularly when the position of the pterion may change with age.