Background: This study aimed to compare the accuracy of linear measurements obtained using the classical (manual) method versus cone-beam computed tomography (CBCT) in craniofacial anthropometry, specifically targeting the infraorbital foramen (IOF). Methods: This study involved two sample groups: one of 40 dry skulls measured manually using digital calipers, and the other of 40 CBCT 3D images digitally measured. Measurements included IOF height, distances between the IOF and nasion (N), IOF and frontomalar orbital (FMO), and between the two IOFs. Statistical analysis was performed using an ANOVA, t-test, and Fisher’s test with a significance level of 0.05. Results: The manual method recorded a mean IOF height of 2.1 mm, while CBCT showed a mean of 3.52 mm. Significant differences were recorded between the two methods when measuring IOF height, with a p < 0.001. However, CBCT measurements generally yielded higher accuracy and lower variance due to the absence of significant differences (p > 0.05). The distance between the two IOFs measured by both methods differed significantly (p = 0.03157), with CBCT measurements showing higher values. Conclusions: In conclusion, although no significant differences were found in the overall accuracy of the two methods, CBCT proved to be a more reliable tool for detailed craniofacial measurements due to its higher accuracy and reproducibility. CBCT demonstrated superior consistency in measurements, offering enhanced precision in assessing craniofacial structures. These findings support the growing preference for CBCT in maxillofacial surgery, where precision is critical for successful outcomes. Nonetheless, manual techniques remain valuable in settings where advanced imaging is not accessible.

Artificial intelligence (AI) is increasingly proposed for diagnosing superficial mycoses, yet whether this literature covers the diagnostic pathway evenly, and is mature enough for clinical translation, remains unclear. We mapped AI applications onto an operational partition of the diagnostic pathway (D1–D4), a construct of this review: pre-analytical clinician-facing imaging (D1), analytical microscopy and histopathology (D2), analytical molecular identification (D3) and integrative post-analytical interpretation (D4). Thirty-three primary studies (2007 to 2026) were included, 85% published since 2021 and predominantly from Asia. Applications concentrated on D2 (n = 16) and D1 (n = 13), with few in D3 (n = 4) and none in D4. Reported accuracy was frequently high but rested on internal, single-centre validation: complete external validation and prospective preregistration were each present in a single study, only a handful shared both public code and public data, about half reported no demographic data and no D1 study stratified performance by Fitzpatrick phototype. A substantial minority carried conflicts of interest tied to the evaluated tool, concentrated in the molecular domain where independent verification was scarcest; where such re-evaluation was possible, reported accuracy fell markedly (one area under the curve (AUC) from 0.98 to 0.75). This corpus is skewed toward the early diagnostic steps, leaves the integrative post-analytical step unaddressed and shows methodological maturity insufficient for routine clinical translation. Institutionalised external validation, reproducibility through open code and data, declaration of dataset overlap and conflicts of interest, and clinically meaningful outcomes are the priorities for future work.