Xiao, Yao

ABSTRACT Pathogenic variants in AMMECR1 have been associated with a rare multisystem disorder characterized by midface hypoplasia, hearing impairment, elliptocytosis, and nephrocalcinosis (MFHIEN). To date, most reported cases involve copy number variants or presumed loss‐of‐function alterations, with only a single prior report describing a missense variant supported by functional studies. Here, we report a patient with a heterozygous de novo AMMECR1 missense variant, NM_015365.3:c.649G>A p.(Val217Met) presenting with clinical features consistent with MFHIEN, including midface hypoplasia, partial hearing impairment, nephrocalcinosis, and elliptocytosis identified on peripheral blood smear. Comparative review of the literature highlights that while previously reported missense variants in AMMECR1 demonstrated altered intranuclear protein distribution and reduced expression in functional assays, clinical evidence supporting pathogenicity of non‐truncating variants remains limited. The phenotypic overlap between our patient and prior report strengthens the association between missense variations and the MFHIEN phenotype. Our findings support the pathogenic relevance of missense variation in AMMECR1 and emphasize the importance of integrating detailed phenotyping, including hematologic evaluation, with genomic data in the diagnosis of rare multisystem disorders. Additional cases and functional studies are needed to clarify genotype–phenotype correlations and underlying disease mechanisms.

Artificial Intelligence (AI) has the potential to revolutionize cytopathology by enhancing diagnostic accuracy, efficiency, and accessibility. However, the implementation of AI in this field presents significant challenges and opportunities. This review paper explores the current landscape of AI applications in cytopathology, highlighting the critical challenges, including data quality and availability, algorithm development, integration and standardization, and clinical validation. We discuss challenges such as the limitation of only one optical section and z-stack scanning, the complexities associated with acquiring high-quality labeled data, the intricacies of developing robust and generalizable AI models, and the difficulties in integrating AI tools into existing laboratory workflows. The review also identifies substantial opportunities that AI brings to cytopathology. These include the potential for improved diagnostic accuracy through enhanced detection capabilities and consistent, reproducible results, which can reduce observer variability. AI-driven automation of routine tasks can significantly increase efficiency, allowing cytopathologists to focus on more complex analyses. Furthermore, AI can serve as a valuable educational tool, augmenting the training of cytopathologists and facilitating global health initiatives by making high-quality diagnostics accessible in resource-limited settings. The review underscores the importance of addressing these challenges to harness the full potential of AI in cytopathology, ultimately improving patient care and outcomes.