Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review

Reyes, Lilian Toledo; Knorst, Jéssica Klöckner; Ortiz, Fernanda Ruffo; Ardenghi, Thiago Machado

doi:10.18053/jctres.07.202104.012

Cited by 5 publications

(2 citation statements)

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“…Additionally, a literature review of machine-learning-based diagnosis and prognosis in clinical dentistry found reports of the use of machine learning algorithms in periodontics and oral medicine [ 22 ]. Machine learning has been been used to integrate microbiome data with immune profiling to stratify peri-implantitis patients according to clinical outcomes [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Candida Carriers among Individuals with Tongue Piercing—A Real-Time PCR Study

et al. 2022

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Among the local factors for oral candidiasis, the piercing of the tongue is recognized by some authors as a risk factor for the colonization of Candida albicans. There are few case reports in which Candida spp. colonization and infection are associated with tongue piercing but only one microbiological study supports this hypothesis in general. The aim of this study was to examine this possible association between the presence of both tongue piercing and Candida spp. in healthy individuals. Positive results for tongue colonization with Candida spp. were found in four (12.9%) of the tongue-pierced subjects and in three (9.67%) subjects of the control group (p = 0.550). All samples were identified as Candida albicans. The univariate and logistic regression analyses of possible risk factors for tongue colonization revealed that gender (p = 0.024), smoking more than 10 cigarettes per day (p = 0.021), and improper hygiene (p = 0.028) were statistically significant influencing factors in the multivariate analysis. The results suggest that the piercing of the tongue is not a risk factor for colonization of Candida spp.

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Section: Discussionmentioning

confidence: 99%

Candida Carriers among Individuals with Tongue Piercing—A Real-Time PCR Study

et al. 2022

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“…For instance, in endodontics, periodontics and prosthodontics, AI is currently utilised for the detection of periapical radiolucencies, detection of vertical root fractures, assessment of profile stresses on the mandible during the implant process and to determine if teeth need to be restored, need a root canal therapy or need to be extracted (Bernauer et al, 2021; Boreak, 2020; Hung et al, 2020; Roy et al, 2018; Thurzo et al, 2022; Van Staden et al, 2008). Other AI models are able to computerise charting from radiographs, detect caries lesions and other oral lesions, such as dentigerous cysts and periapical cysts (Reyes et al, 2021). In addition, AI has been implemented in computational simulation via the finite element method to facilitate dental implant design optimisation and the prediction of success in implant dentistry (Revilla-León et al, 2021), as well as permit biomechanical simulations and tissue stress response evaluations in various medical and dental fields (Ammarullah et al, 2022; Jain et al, 2021; Phellan et al, 2021; Vurtur Badarinath et al, 2021; Xue et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

Salazar,

Rossouw,

Javed

et al. 2023

J Orthod

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Background: The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed. Objectives: To determine the accuracy of AI in treatment planning and soft tissue outcome prediction in OGT. Design: Systematic review. Data sources: Unrestricted search of indexed databases and reference lists of included studies. Data selection: Clinical studies that addressed the focused question ‘Is AI useful for treatment planning and soft tissue outcome prediction in OGT?’ were included. Data extraction: Study screening, selection and data extraction were performed independently by two authors. The risk of bias (RoB) was assessed using the Cochrane Collaboration’s RoB and ROBINS-I tools for randomised and non-randomised clinical studies, respectively. Data synthesis: Eight clinical studies (seven retrospective cohort studies and one randomised controlled study) were included. Four studies assessed the role of AI for treatment decision making; and four studies assessed the accuracy of AI in soft tissue outcome prediction after OGT. In four studies, the level of agreement between AI and non-AI decision making was found to be clinically acceptable (at least 90%). In four studies, it was shown that AI can be used for soft tissue outcome prediction after OGT; however, predictions were not clinically acceptable for the lip and chin areas. All studies had a low to moderate RoB. Limitations: Due to high methodological inconsistencies among the included studies, it was not possible to conduct a meta-analysis and reporting biases assessment. Conclusion: AI can be a useful aid to traditional treatment planning by facilitating clinical treatment decision making and providing a visualisation tool for soft tissue outcome prediction in OGT. Registration: PROSPERO CRD42022366864.

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Non Invasive Approach for Dental Caries Detection

Mehta,

Borse

2023

2023 IEEE Pune Section International Conference (PuneCon)

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Scope and challenges of machine learning-based diagnosis and prognosis in clinical dentistry: A literature review

Cited by 5 publications

References 99 publications

Candida Carriers among Individuals with Tongue Piercing—A Real-Time PCR Study

Candida Carriers among Individuals with Tongue Piercing—A Real-Time PCR Study

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review

Non Invasive Approach for Dental Caries Detection

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