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Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression

Ji-Ae Park, Jun-Ho Moon, Ju-Myung Lee

    The Angle Orthodontist
    |September 4, 2024
    PubMed
    Summary

    Artificial intelligence (AI) models show varied performance in predicting orthognathic surgery outcomes. Combining AI with conventional methods like partial least squares may offer improved prediction accuracy for surgical planning.

    Keywords:
    Artificial intelligenceDeep learningMachine learningPartial least squaresPredictionSurgery

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    Area of Science:

    • Orthodontics and Dentofacial Surgery
    • Medical Artificial Intelligence
    • Surgical Outcome Prediction

    Background:

    • Orthognathic surgery requires accurate outcome prediction for optimal results.
    • Conventional prediction methods, such as multivariate multiple linear regression (MLR) and partial least squares (PLS), have limitations.
    • The integration of artificial intelligence (AI) offers potential advancements in predictive modeling.

    Purpose of the Study:

    • To compare the predictive performance of an AI model (TabNet) against conventional methods (MLR, PLS) for orthognathic surgical outcomes.
    • To identify specific soft-tissue landmarks where AI or conventional methods excel in prediction.
    • To analyze factors influencing prediction accuracy in orthognathic surgery.

    Main Methods:

    • Analysis of lateral cephalograms from 705 patients undergoing combined surgical-orthodontic treatment.
    • Utilized 254 input variables including skeletal and soft-tissue characteristics, and surgical repositioning.
    • Compared prediction accuracy of MLR, PLS, and a TabNet-based AI model for 32 soft-tissue landmarks.

    Main Results:

    • Multivariate multiple linear regression (MLR) exhibited the lowest predictive performance.
    • Partial least squares (PLS) outperformed AI in predicting 16 soft-tissue landmarks above the upper lip.
    • AI demonstrated superior prediction for six landmarks in the lower mandible and neck region; 10 landmarks showed no significant difference.

    Conclusions:

    • AI prediction models do not universally surpass conventional methods in orthognathic surgery.
    • Partial least squares (PLS) showed better prediction for upper lip landmarks, while AI excelled in lower mandibular and neck areas.
    • A hybrid approach combining AI and conventional methods may enhance the prediction of orthognathic surgical outcomes.