Herbst, S.R.; Pitchika, V.; Krois, J.; Krasowski, A.; Schwendicke, F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study. J. Clin. Med.2023, 12, 5464.
Herbst, S.R.; Pitchika, V.; Krois, J.; Krasowski, A.; Schwendicke, F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study. J. Clin. Med. 2023, 12, 5464.
Herbst, S.R.; Pitchika, V.; Krois, J.; Krasowski, A.; Schwendicke, F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study. J. Clin. Med.2023, 12, 5464.
Herbst, S.R.; Pitchika, V.; Krois, J.; Krasowski, A.; Schwendicke, F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study. J. Clin. Med. 2023, 12, 5464.
Abstract
(1) Background: We aimed to identify factors associated with the presence of apical lesions (AL) on panoramic radiographs and to evaluate the predictive value of the identified factors.
(2) Methodology: Panoramic radiographs from 1071 patients (age: 11-93 a, mean: 50.6 a ± 19.7 a) with 27532 teeth were included. Each radiograph was independently assessed by five experienced dentists for AL. A range of shallow machine learning algorithms (logistic regression, k-nearest neighbor, decision tree, random forest, support vector machine, adaptive and gradient boosting) were employed to identify factors on patient and tooth level associated with AL and to predict AL.
(3) Results: AL were detected in 522 patients (48.7%) and 1133 teeth (4.1%), whereas males showed a significantly higher prevalence than females (52.5%/44.8%; p<0.05). Logistic regression found that an existing root canal treatment was the most important risk factor (adjusted Odds Ratio 16.89; 95% CI: 13.98-20.41), followed by the tooth type ‘molar’ (2.54; 2.1-3.08) and the restoration with a crown (2.1; 1.67-2.63). Associations between factors and AL were stronger and accuracy higher when using less complex models like decision tree (F1 score: 0.9 (0.89-0.9)).
(4) Conclusions: The presence of AL was higher in root-canal treated teeth, those with crowns and molars. More complex machine learning models did not outperform less complex ones.
Medicine and Pharmacology, Dentistry and Oral Surgery
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