Oral surgery, oral medicine, oral pathology and oral radiology · 2026
Machine learning-based approach for predicting the extraction time of a mandibular third molar.
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Danjo Atsushi, Fukuda Motoki, Aijima Reona, Kamohara Asana, Kuwada Chiaki, Ariji Yoshiko, Ariji Eiichiro, Yamashita Yoshio
Аннотация
OBJECTIVE: Predicting the surgical time required for mandibular third molar extraction is challenging because patient-, tooth-, and operator-related factors interact in complex ways. This study aimed to develop an interpretable machine-learning model using patient demographics, panoramic radiographic features, and surgeon-related variables.
STUDY DESIGN: We retrospectively reviewed 713 mandibular third molar extractions at a single university hospital. Models were developed using Prediction One, a no-code machine-learning platform. Because continuous-time prediction showed limited performance, we reframed the primary task as binary classification at clinically relevant thresholds (≥31, ≥46, and ≥61 minutes). Performance was benchmarked against three traditional models and feature contribution scores were calculated to assess variable importance.
RESULTS AND DISCUSSION: The ≥31-minute model showed the best performance (accuracy, 0.75; area under the curve, 0.80), and outperformed the comparator models. Surgeon experience and specialist certification were the strongest contributors, followed by root-related radiographic morphology. In contrast, indices such as the Pell and Gregory classification and the number of roots, showed limited predictive value.
CONCLUSIONS: Minute-by-minute prediction was difficult, but threshold-based prediction was feasible, and we identified factors that contributed substantially to time prediction and those with minimal impact.
STUDY DESIGN: We retrospectively reviewed 713 mandibular third molar extractions at a single university hospital. Models were developed using Prediction One, a no-code machine-learning platform. Because continuous-time prediction showed limited performance, we reframed the primary task as binary classification at clinically relevant thresholds (≥31, ≥46, and ≥61 minutes). Performance was benchmarked against three traditional models and feature contribution scores were calculated to assess variable importance.
RESULTS AND DISCUSSION: The ≥31-minute model showed the best performance (accuracy, 0.75; area under the curve, 0.80), and outperformed the comparator models. Surgeon experience and specialist certification were the strongest contributors, followed by root-related radiographic morphology. In contrast, indices such as the Pell and Gregory classification and the number of roots, showed limited predictive value.
CONCLUSIONS: Minute-by-minute prediction was difficult, but threshold-based prediction was feasible, and we identified factors that contributed substantially to time prediction and those with minimal impact.