Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning

Hung, Man; Hon, Eric S.; Ruiz‐Negrón, Bianca; Lauren, Evelyn; Moffat, Ryan; Su, Weicong; Xu, Julie; Park, Jungweon; Prince, David; Cheever, Joseph; Licari, Frank W.

doi:10.3390/ijerph17197286

Cited by 15 publications

(16 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For clinical AI applications in dentistry, a full digital workflow transformation is necessary [ 16 , 17 , 18 , 19 , 20 , 21 ]. Currently, various examples of advanced dental technologies can be found based on digital workflows [ 7 , 16 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Their goal is to enhance the efficiency and quality of delivered services.…”

Section: Introductionmentioning

confidence: 99%

“…They often overlap various dental specialties and categorizations. Currently, they mostly include: AI in X-ray and other diagnostics, caries [ 29 , 30 , 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ] AI in implant dentistry [ 26 , 33 , 45 , 46 ] AI in photography analysis [ 27 , 28 , 29 , 47 ] AI in practice management, tele-dentistry, patient coaching [ 44 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ] AI in clinical predictions (virtual simulation, aging, growth) [ 5 , 55 , 56 , 57 , 58 , 59 ] …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

View full text Add to dashboard Cite

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Several factors could lead to the underreporting of discrimination [17,18]. One factor is social desirability, which is the tendency for survey participants to answer questions in a way that they deem socially acceptable [18,19]. Another relevant factor is 'internalized oppression,' in which people in subordinate groups accept their social status and internalize negative attitudes towards them, thus perceiving their mistreatment experience as being deserved [18].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning algorithms have facilitated research on the role of social factors in predicting individual health conditions. These factors include community-level socioeconomic status (SES) [ 25 , 26 ], individual-level SES [ 23 , 27 ], language skill [ 28 ], and insurance coverage [ 23 , 28 ]. For example, a recent study of 66 low-income and middle-income countries explored the ability of SES indicators, including household wealth, educational attainment, and occupation, to predict women’s height, which could be an indicator of human welfare at the population level [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach

Yoon¹,

Kim²,

Chung³

et al. 2021

Epidemiol Health

View full text Add to dashboard Cite

OBJECTIVES This study was conducted to examine gender differences in under-reporting hiring discrimination by building a prediction model for workers who responded “not applicable (NA)” to a question about hiring discrimination despite being eligible to answer. METHODS Using data from 3,576 wage workers in the seventh wave (2004) of the Korea Labor and Income Panel Study, we trained and tested 9 machine learning algorithms using “yes” or “no” responses regarding the lifetime experience of hiring discrimination. We then applied the best-performing model to estimate the prevalence of experiencing hiring discrimination among those who answered “NA.” Under-reporting of hiring discrimination was calculated by comparing the prevalence of hiring discrimination between the “yes” or “no” group and the “NA” group. RESULTS Based on the predictions from the random forest model, we found that 58.8% of the “NA” group were predicted to have experienced hiring discrimination, while 19.7% of the “yes” or “no” group reported hiring discrimination. Among the “NA” group, the predicted prevalence of hiring discrimination for men and women was 45.3% and 84.8%, respectively. CONCLUSIONS This study introduces a methodological strategy for epidemiologic studies to address the under-reporting of discrimination by applying machine learning algorithms.

show abstract

“…Therefore, recognizing that the social determinants of health have a strong relationship with unmet dental care needs, dental care services must be planned to be more responsive to the needs of their local populations. Thus, The system will be able to prioritize the care of groups with high need for care, such as vulnerable groups, including those with multiple morbidities [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Encouraging the work process of oral health teams can favour the planning of their actions in Primary Health Care

Probst

Melo

Tagliaferro

et al. 2021

Preprint

View full text Add to dashboard Cite

Background The social determinants of health are strongly related to dental care needs. Therefore, oral health services in primary health care (PHC) must be planned to be more responsive to the needs of their local populations. The aim of this study was to evaluate the variables that are associated with planning actions by oral health teams (OHTs) in PHC. Methods This analytical, cross-sectional study used data from the external evaluation of the third cycle of the National Program for Improving Access and Quality of Primary Care (PMAQ-AB), carried out between 2017 and 2018, in Brazil. The final sample consisted of 22,268 OHTs from 4,679 Brazilian municipalities. The outcome variable was action planning by the OHTs and the independent variables included characteristics of the OHTs and municipal PHC Management, in addition to municipal socioeconomic indicators. Five models were sequentially built for statistical analysis, namely: an empty model (with the intercept, only), a multiple model with variables related to OHTs, a multiple model with variables related to municipal management in PHC, a multiple model with the municipal indicators, and a multilevel logistic regression model with variables at the level of OHTs and municipalities. Results It was observed that 32.5% of the OHTs did not plan their actions. In the final adjusted model the characteristics of OHTs were more significant for the outcome than the variables related to municipal PHC Management and municipal socioeconomic indicators. Moreover, participating in meetings together with the PHC team and carrying out case and therapeutic project discussions were the variables with the greatest effect on the outcome, that is, adjusted OR of 3.75 (95%CI: 3.42–4.10) and adjusted OR of 3.74 (95%CI: 3.46–4.05), respectively. Conclusions It is concluded that the indicators complicating the planning of actions by the OHTs were mainly the characteristics inherent to the work process of the OHTs, particularly the failure to hold meetings together with the PHC team, case and therapeutic project discussions, self-assessment, as well as the embracement protocols.

show abstract

Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning

Cited by 15 publications

References 21 publications

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

Gender differences in under-reporting hiring discrimination in Korea: a machine learning approach

Encouraging the work process of oral health teams can favour the planning of their actions in Primary Health Care

Contact Info

Product

Resources

About