Genetics of craniofacial malformations

Integrating Virtual Surgical Planning and 3D-Printed Tools with Iliac Bone Grafts for Orbital and Zygomatic Reconstruction in Hemifacial Microsomia Patients

Hemifacial Microsomia (HFM) is the second most common congenital craniofacial malformation syndrome, and the complexity of HFM makes its treatment challenging. The present study aimed to introduce a new approach of utilization of virtual surgical planning (VSP) and 3D-printed surgical adjuncts for maxillofacial reconstruction. Five HFM patients were included in this study. All participants were provided with a full VSP, including the design of osteotomy lines, the design and fabrication of 3D-printed cutting guides, fixation plates, and titanium mesh for implantation. With the assistance of 3D-printed cutting guides and fixation plates, the orbital deformities were corrected, and a 3D-printed titanium mesh combined with iliac cancellous bone graft was applied to reconstruct the zygomatic arch. The surgical accuracy, effectiveness, and bone absorption rate were evaluated. All patients completed the entirely digital treatment process without experiencing severe complications. The surgical adjuncts were effective in aligning the movement of the bone segments with the surgical plan, resulting in mean 3D deviations (1.0681 ± 0.15 mm) and maximum 3D deviations (3.1127 ± 0.44 mm). The image fusion results showed that the patients' postoperative position of the maxilla, zygoma, and orbital rim was consistent with the virtual surgical plan, with only a slight increase in the area of bone grafting. The postoperative measurements showed significant improvement in the asymmetry indices of Er (AI of Er: from 17.91 ± 3.732 to 5.427 ± 1.389 mm, p = 0.0001) and FZ (AI of FZ: from 7.581 ± 1.435 to 4.070 ± 1.028 mm, p = 0.0009) points. In addition, the observed bone resorption rate at the 6-month follow-up across the five patients was 45.24% ± 3.13%. In conclusion, the application of VSP and 3D-printed surgical adjuncts demonstrates significant value in enhancing the precision and effectiveness of surgical treatments for HFM. A 3D-printed titanium mesh combined with iliac cancellous bone graft can be considered an ideal alternative for the reconstruction of the zygomatic arch.

Foxi3GFP and Foxi3CreER mice allow identification and lineage labeling of pharyngeal arch ectoderm and endoderm, and tooth and hair placodes

BACKGROUND

FOXI3 is a forkhead family transcription factor that is expressed in the progenitors of craniofacial placodes, epidermal placodes, and the ectoderm and endoderm of the pharyngeal arch region. Loss of Foxi3 in mice and pathogenic Foxi3 variants in dogs and humans cause a variety of craniofacial defects including absence of the inner ear, severe truncations of the jaw, loss or reduction in external and middle ear structures, and defects in teeth and hair.

RESULTS

To allow for the identification, isolation, and lineage tracing of Foxi3-expressing cells in developing mice, we targeted the Foxi3 locus to create Foxi3GFP and Foxi3CreER mice. We show that Foxi3GFP mice faithfully recapitulate the expression pattern of Foxi3 mRNA at all ages examined, and Foxi3CreER mice can trace the derivatives of pharyngeal arch ectoderm and endoderm, the pharyngeal pouches and clefts that separate each arch, and the derivatives of hair and tooth placodes.

CONCLUSIONS

Foxi3GFP and Foxi3CreER mice are new tools that will be of use in identifying and manipulating pharyngeal arch ectoderm and endoderm and hair and tooth placodes.

Ultrasound measurements of fetal facial profile markers and their associations with congenital malformations during early pregnancy

BACKGROUND

Fetal facial profile could be measured during the early pregnancy. Its abnormalities might be associated with certain congenital malformations. We aimed to study the associations between fetal facial profile measurements with crown-rump length and congenital malformations (cleft lip and palate, micrognathia, and open spina bifida) during early pregnancy.

METHODS

We performed a prospective cross-sectional study between June 2019 and April 2022. Pregnant women at a gestational age between 11-13+ 6 weeks were enrolled. Two sonographers performed fetal facial profile measurements independently. The associations between these measurements with crown-rump length and congenital malformations were evaluated.

RESULTS

There were 406 and 25 fetuses without or with congenital malformations, respectively. Two sonographers showed satisfactory inter- and intra-observer agreements and reproducibility. The maxillary gap was only observed in 7.6% of normal fetuses, whereas all cleft lip and palate fetuses had a maxillary gap ≥ 0.8 mm. The crown-rump length was negatively correlated with frontomaxillary facial angle, inferior facial angle, and profile line distance but positively correlated with maxilla-nasion-mandible angle, facial maxillary angle, frontal space distance, and palatine maxillary diameter. These measurements showed various significant changes with different congenital malformations.

CONCLUSIONS

Measurements of fetal facial profile in early pregnancy were feasible with satisfactory reproducibility. These measurements correlated with crown-rump length and showed significant differences with certain fetal congenital malformations.

Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches

Fetal development is a critical phase in prenatal care, demanding the timely identification of anomalies in ultrasound images to safeguard the well-being of both the unborn child and the mother. Medical imaging has played a pivotal role in detecting fetal abnormalities and malformations. However, despite significant advances in ultrasound technology, the accurate identification of irregularities in prenatal images continues to pose considerable challenges, often necessitating substantial time and expertise from medical professionals. In this review, we go through recent developments in machine learning (ML) methods applied to fetal ultrasound images. Specifically, we focus on a range of ML algorithms employed in the context of fetal ultrasound, encompassing tasks such as image classification, object recognition, and segmentation. We highlight how these innovative approaches can enhance ultrasound-based fetal anomaly detection and provide insights for future research and clinical implementations. Furthermore, we emphasize the need for further research in this domain where future investigations can contribute to more effective ultrasound-based fetal anomaly detection.