Table 3.

Articles excluded and reason for exclusion after reading the full paper.

Author Name with Year of Publication	Title of the Article	Reason for Exclusion
Orozco-Arroyave et al. [32]	Characterization methods for the detection of multiple voice disorders: Neurological, functional, and laryngeal diseases	The authors did not use any of the AI or machine learning techniques in this study.
Dubey et al. [33]	Detection and assessment of hypernasality in repaired cleft palate speech using vocal tract and residual features	The authors used different methods for detection and assessment of hypernasality in children with CLP but no AI or machine learning methods involved in the study.
Phan et al. [34]	Tooth agenesis and orofacial clefting: genetic brothers in arms?	This is a review paper on tooth agenesis and orofacial clefting based on genetic loci but did not mention about any AI models.
Mathiyalagan et al. [35]	Meta-Analysis of Grainyhead-Like Dependent Transcriptional Networks: A Roadmap for Identifying Novel Conserved Genetic Pathways	The meta-analysis was done to identify the genes causing oral clefting but no AI or Machine learning techniques used in this study
Lim et al. [36]	Determination of prognostic factors for orthognathic surgery in children with cleft lip and/or palate	Unable to download the full content of this study.
Carvajal-Castaño and Orozco-Arroyave, [37]	Articulation Analysis in the Speech of Children with Cleft Lip and Palate	This article is a chapter from the book “Progress in Pattern Recognition Image Analysis, Computer Vision and Applications”.
Zhang et al. [38]	Cleft Volume Estimation and Maxilla Completion Using Cascaded Deep Neural Networks	This paper is a chapter from the book “Machine Learning in Medical Imaging”.
Tanikawa et al. [39]	Clinical applicability of automated cephalometric landmark identification: Part I—Patient-related identification errors	Unable to download the full text article.