# Machine Learning for Child Oral Health: A Scoping Review
## 4 Discussion  
This review showed that LMICs contribute the least to publications pertaining to ML for oral health diagnosing and predicting in children under 9 years of age. Throughout the literature there are many ideas pertaining to why the LMICs have low publishing rates. A cross-sectional study by [PERSON] sought to understand the participation of UMICs and LMICs in clinical trials done by HICs. In this study, it was concluded that HICs enrolled more participants in clinical trials from LMICs and UMICs [58]. This could explain the disproportion of more research completed in HIC compared to that of their counterparts due to the use of LMICs and UMICs participants in HIC. Since the HIC uses the LMIC and UMIC resources, these countries are limited in not only supply of resources such as funding, but participants as well. Although this alone could explain the result of low publication numbers for LMICs found in this review, another editorial completed by [PERSON] explained general barriers to which LMICs face when conducting research. A main conclusion made in that study is that these LMICs lack research funding in their national budget, in turn hindering the amount of research that can be produced [59]. LMICs can overcome this research funding disparity through the use of partnerships with HICs. Partnerships including co-funded projects or sharing of research facilities can serve as a powerful strategy to bring together complementary strengths and improve access to resources and training. If these LMICs had more budget for their nation, they would be able to gain the resources needed to complete their research. The results of this study indicate that the USA was the predominant contributor to the publications included in our analysis. However, it is essential to consider that the restriction to studies published in English may have influenced this conclusion. Additionally, the availability of funding and resources in the USA likely facilitates a higher volume of publications. This aligns with findings from a bibliometric analysis conducted by [PERSON], which also identified the USA as the leading contributor in terms of dental articles included in the study [60]. Together, these insights highlight the significant role of the USA in shaping dental research trends, while also suggesting potential limitations in the scope of the analysis. The increasing prevalence of ML algorithms in pediatric dentistry for prediction stems from their ability to assess the risk of oral health issues, anticipate the progression of conditions, and offer insights into long-term prognosis. These predictive models aid in tailoring treatment plans, guiding interventions, and optimizing resource allocation based on anticipated patient needs. By analyzing behavioral patterns, these algorithms provide valuable information for promoting preventive strategies and improving oral health habits in children. Additionally, the predictive capabilities support informed decision making by clinicians and caregivers, contributing to more personalized and effective approaches in pediatric dental care. The neural networks technique was originally the preferred ML algorithm used in research, and it has still been consistently used as time progresses. This technique is also a commonly used algorithm of ML when looking across different specialties. Neural networks are popular in their application to dentistry due to their domain largely being well adapted to the task of image classification. This technique is the most used algorithm for image recognition [61]. In dentistry, the patient exam consists of both a clinical and radiographic examination to make the appropriate conclusions and treatment plan for a patient. Numerous images and radiographs are taken at a patient exam and are stored over time to evaluate the progress of a patient. Radiographs allow a dentist to see changes in the hard and soft tissues such as caries, bone disease, infections, and tumors.