ISSN :2582-9793
Niusha zare, Mahmood Dashti, Farshad Khosraviani, Tara Azimi, Mohammad Soroush Sehat, Ehsan Alekajbaf and Amir Fahimipour
Adv. Artif. Intell. Mach. Learn., 4 (3):2731-2745
Niusha zare : postgraduate student, Department of Operative Dentistry, University of Southern California, USA
Mahmood Dashti : Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farshad Khosraviani : Researcher, UCLA School of Dentistry, CA, USA.
Tara Azimi : Post Graduate Student, Orofacial Pain and Disfunction, UCLA School of Dentistry, CA, USA.
Mohammad Soroush Sehat : Research assistant, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
Ehsan Alekajbaf : Graduate MSc of Orthodontics, Centro Escolar University, 9 Mendiola St, San Miguel, Manila, 1008 Metro Manila, Philippines.
Amir Fahimipour : Discipline of Oral Surgery, Medicine and Diagnostics, Faculty of Medicine and Health, Westmead Hospital, The University of Sydney, NSW 2145, Australia.
DOI: https://dx.doi.org/10.54364/AAIML.2024.43159
Article History: Received on: 17-Jul-24, Accepted on: 23-Sep-24, Published on: 30-Sep-24
Corresponding Author: Niusha zare
Email: zareniusha@gmail.com
Citation: Mahmood Dashti, Farshad Khosraviani, Tara Azimi, Mohammad Soroush Sehat, Ehsan Alekajbaf, Amir Fahimipour, Niusha zare (2024). Predicting Mandibular Bone Growth Using Artificial Intelligence and Machine Learning: A Systematic Review. Adv. Artif. Intell. Mach. Learn., 4 (3 ):2731-2745
Introduction. The accurate prediction of mandibular bone growth is crucial in
orthodontics and maxillofacial surgery, impacting treatment planning and
patient outcomes. Traditional methods often fall short due to their reliance on
linear models and clinician expertise, which are prone to human error and
variability. Artificial intelligence (AI) and machine learning (ML) offer
advanced alternatives, capable of processing complex datasets to provide more
accurate predictions. This systematic review examines the efficacy of AI and ML
models in predicting mandibular growth compared to traditional methods.
Method. A
systematic review was conducted following the PRISMA guidelines, focusing on
studies published up to July 2024. Databases searched included PubMed, Embase,
Scopus, and Web of Science. Studies were selected based on their use of AI and
ML algorithms for predicting mandibular growth. A total of 31 studies were
identified, with 6 meeting the inclusion criteria. Data were extracted on study
characteristics, AI models used, and prediction accuracy. The risk of bias was
assessed using the QUADAS-2 tool.
Results. The
review found that AI and ML models generally provided high accuracy in
predicting mandibular growth. For instance, the LASSO model achieved an average
error of 1.41 mm for predicting skeletal landmarks. However, not all AI models
outperformed traditional methods; in some cases, deep learning models were less
accurate than conventional growth prediction models.
Discussion. The variability in datasets and study designs across the included
studies posed challenges for comparing AI models' effectiveness. Additionally,
the complexity of AI models may limit their clinical applicability. Despite
these challenges, AI and ML show significant promise in enhancing predictive
accuracy for mandibular growth.
Conclusion. AI and ML models have the potential to revolutionize mandibular growth
prediction, offering greater accuracy and reliability than traditional methods.
However, further research is needed to standardize methodologies, expand
datasets, and improve model interpretability for clinical integration.