Birth prevalence of orofacial cleft in a tertiary hospital in Riyadh, Saudi Arabia: A retrospective audit

Wasmiya A AlHayyan 1, Samar Al Hayek 2, Sarah S AlOtabi 3, Shahad A AlGhanim 4

Affiliations

12 January 2021

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doi: 10.1016/j.sdentj.2021.08.005

Abstract

Aim: The present study aimed to calculate the birth prevalence of orofacial cleft patients in King Abdulaziz Medical City (KAMC) Tertiary Care Hospital in Riyadh City, KSA.

Material and methods: The data utilized in this retrospective study were obtained from the birth data registry at the hospital covering the period between January 2014 and December 2018. The data collected for the orofacial cleft patients pertained to the birth year, type of cleft, gender, and associated syndromes.

Results: The total number of documented orofacial cleft cases born between 2014 and 2018 was 78, with a birth prevalence of 1.8 per 1000 live births and no gender bias. Cleft palate (CP) represented the most common type at 38.5% (n = 30), followed by cleft lip at 26.9% (n = 21) and unilateral cleft lip and palate at 24% (n = 19). The least common type was facial cleft, at 3.4% (n = 6). Syndrome association was seen in 15.4% (n = 12) of the cases.

Conclusion: The birth prevalence of orofacial clefts in KAMC Riyadh city is in accordance with the worldwide ratio and similar to the birth prevalence reported in the Middle East area. CP showed more prevalence than the other orofacial cleft types, and the association with syndromes was significantly low.

Keywords: Birth prevalence; Craniofacial Anomalies; Oro-Facial Cleft.

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1 (a) Transverse micro radiography image of a thin section of a tooth with a cavitated lesion. Reproduced from De Medeiros et al with permission from John Wiley and Sons. (b) Optical image of a ground section of a premolar tooth.

Figure 1 (a) Transverse micro radiography image of a thin section of a tooth with a cavitated lesion. Reproduced from De Medeiros et al with permission from John Wiley and Sons. (b) Optical image of a ground section of a premolar tooth.

Figure 2 Observations of an individual slice of a tooth with proximal enamel caries before and following processing using various filter options and colourization: (a, g) original image; (b, h) gaussian blur filtering; (c, i) block-matching and three-dimensional filtering; (d, j) median filtering; (e, k) total variation filtering; (f) regions of interest (ROIs).

Figure 2 Observations of an individual slice of a tooth with proximal enamel caries before and following processing using various filter options and colourization: (a, g) original image; (b, h) gaussian blur filtering; (c, i) block-matching and three-dimensional filtering; (d, j) median filtering; (e, k) total variation filtering; (f) regions of interest (ROIs).

Figure 3 (a) Contrast-to-noise ratio (CNR) value in the texture-free area (CNRTex-free) of 36 specimens before and after denoising using the four methods; (b) CNR value in the edge and textural area (CNREdge) of 36 specimens before and after denoising using the four methods.

Figure 3 (a) Contrast-to-noise ratio (CNR) value in the texture-free area (CNRTex-free) of 36 specimens before and after denoising using the four methods; (b) CNR value in the edge and textural area (CNREdge) of 36 specimens before and after denoising using the four methods.

Figure 3 (a) Contrast-to-noise ratio (CNR) value in the texture-free area (CNRTex-free) of 36 specimens before and after denoising using the four methods; (b) CNR value in the edge and textural area (CNREdge) of 36 specimens before and after denoising using the four methods.

Figure 3 (a) Contrast-to-noise ratio (CNR) value in the texture-free area (CNRTex-free) of 36 specimens before and after denoising using the four methods; (b) CNR value in the edge and textural area (CNREdge) of 36 specimens before and after denoising using the four methods.

Figure 4 (a) Edge preserving index value for 36 image specimens after denoising using the four methods. (b) Blurring index value for 36 image specimens after denoising using the four methods.

Figure 4 (a) Edge preserving index value for 36 image specimens after denoising using the four methods. (b) Blurring index value for 36 image specimens after denoising using the four methods.

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