Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review

Mohamed Zahoor Ul Huqh, Johari Yap Abdullah (), Ling Shing Wong (), Nafij Bin Jamayet, Mohammad Khursheed Alam, Qazi Farah Rashid, Adam Husein, Wan Muhamad Amir W. Ahmad, Sumaiya Zabin Eusufzai, Somasundaram Prasadh, Vetriselvan Subramaniyan, Neeraj Kumar Fuloria, Shivkanya Fuloria, Mahendran Sekar and Siddharthan Selvaraj ()
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Mohamed Zahoor Ul Huqh: Orthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
Johari Yap Abdullah: Craniofacial Imaging Lab, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
Ling Shing Wong: Faculty of Health and Life Sciences, INTI International University, Nilai 71800, Malaysia
Nafij Bin Jamayet: Division of Clinical Dentistry (Prosthodontics), School of Dentistry, International Medical University, Bukit Jalil, Kuala Lumpur 57000, Malaysia
Mohammad Khursheed Alam: Orthodontic Division, Preventive Dentistry Department, College of Dentistry, Jouf University, Sakaka 72345, Saudi Arabia
Qazi Farah Rashid: Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
Adam Husein: Prosthodontic Unit, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
Wan Muhamad Amir W. Ahmad: Department of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
Sumaiya Zabin Eusufzai: Department of Biostatistics, School of Dental Sciences, Health Campus, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Malaysia
Somasundaram Prasadh: National Dental Center Singapore, 5 Second Hospital Avenue, Singapore 168938, Singapore
Vetriselvan Subramaniyan: Faculty of Medicine, Bioscience and Nursing, MAHSA University, Kuala Lumpur 42610, Malaysia
Neeraj Kumar Fuloria: Faculty of Pharmacy, AIMST University, Bedong 08100, Malaysia
Shivkanya Fuloria: Faculty of Pharmacy, AIMST University, Bedong 08100, Malaysia
Mahendran Sekar: Department of Pharmaceutical Chemistry, Faculty of Pharmacy and Health Sciences, Royal College of Medicine Perak, Universiti Kuala Lumpur, Ipoh 30450, Malaysia
Siddharthan Selvaraj: Faculty of Dentistry, AIMST University, Bedong 08100, Malaysia

IJERPH, 2022, vol. 19, issue 17, 1-17

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.

Keywords: artificial intelligence; machine learning; diagnostic performance; treatment prediction; cleft lip and palate (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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