Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/30614
Title: Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review
Authors: Huqh, Mohamed Zahoor Ul
Abdullah, Johari Yap
Wong, Ling Shing
Jamayet, Nafij
Alam, Mohammad Khursheed
Rashid, Qazi Farah
Adam Husein
Wan Muhamad Amir W. Ahmad
Eusufzai, Sumaiya Zabin
Prasadh, Somasundaram
Subramaniyan, Vetriselvan
Fuloria, Neeraj Kumar
Fuloria, Shivkanya
Sekar, Mahendran
Selvaraj, Siddharthan
(UniKL RCMP)
Keywords: Artificial intelligence
Cleft lip and palate
Diagnostic performance
Machine learning
Treatment prediction
Issue Date: Sep-2022
Publisher: MDPI
Citation: Huqh, M. Z. U., Abdullah, J. Y., Wong, L. S., Jamayet, N., Alam, M. K., Rashid, Q. F., Adam Husein, Wan Muhamad Amir W. Ahmad, Eusufzai, S. Z., Prasadh, S., Subramaniyan, V., Fuloria, N. K., Fuloria, S., Sekar, M., & Selvaraj, S. (2022). Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review. International Journal of Environmental Research and Public Health, 19(17), 10860. https://doi.org/10.3390/ijerph191710860
Abstract: Objective: The objective of this systematic review was (a) to explore the current clinical applications of AI/ML (Artificial intelligence and Machine learning) techniques in diagnosis and treatment prediction in children with CLP (Cleft lip and palate), (b) to create a qualitative summary of results of the studies retrieved. Materials and methods: An electronic search was carried out using databases such as PubMed, Scopus, and the Web of Science Core Collection. Two reviewers searched the databases separately and concurrently. The initial search was conducted on 6 July 2021. The publishing period was unrestricted; however, the search was limited to articles involving human participants and published in English. Combinations of Medical Subject Headings (MeSH) phrases and free text terms were used as search keywords in each database. The following data was taken from the methods and results sections of the selected papers: The amount of AI training datasets utilized to train the intelligent system, as well as their conditional properties; Unilateral CLP, Bilateral CLP, Unilateral Cleft lip and alveolus, Unilateral cleft lip, Hypernasality, Dental characteristics, and sagittal jaw relationship in children with CLP are among the problems studied. Results: Based on the predefined search strings with accompanying database keywords, a total of 44 articles were found in Scopus, PubMed, and Web of Science search results. After reading the full articles, 12 papers were included for systematic analysis. Conclusions: Artificial intelligence provides an advanced technology that can be employed in AI-enabled computerized programming software for accurate landmark detection, rapid digital cephalometric analysis, clinical decision-making, and treatment prediction. In children with corrected unilateral cleft lip and palate, ML can help detect cephalometric predictors of future need for orthognathic surgery.
URI: https://ir.unikl.edu.my/jspui/handle/123456789/30614
ISSN: 16617827
Appears in Collections:Journal Articles



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