Journal of orthodontics · 2023
Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review.
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Salazar Daisy, Rossouw Paul Emile, Javed Fawad, Michelogiannakis Dimitrios
Аннотация
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.
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.