Automating bone loss measurement on periapical radiographs for predicting the periodontitis stage and grade
Bibliographic record
Abstract
Background: The aim of this study was to develop and evaluate an automated approach for segmenting bone loss (BL) on periapical (PA) radiographs and predicting the stage and grade of periodontitis. Methods: One thousand PA radiographs obtained from 572 patients were utilized for training while a separate set of 1,582 images from 210 patients were used for testing. BL was segmented using a U-Net model, which was trained with augmented datasets to enhance generalizability. Apex detection was performed using YOLO-v9, focusing on identifying apexes of teeth to measure root length. Root length was calculated as the distance between the coordinates of detected apexes and center of cemento-enamel junction (CEJ), which was segmented utilizing a U-Net algorithm. BL percentage (ratio of BL to the root length) was used to predict the stage and grade of periodontitis. Evaluation metrics including accuracy, precision, recall, F1-score, Intersection over Union (IoU), mean absolute error (MAE), intraclass correlation coefficients (ICC), and root mean square error (RMSE) were used to evaluate the models' performance. Results: The U-Net model achieved high accuracy in segmenting BL with 94.9%, 92.9%, and 95.62% on training, validation, and test datasets, respectively. The YOLO-v9 model exhibited a mean Average Precision (mAP) of 66.7% for apex detection, with a precision of 79.6% and recall of 62.4%. The BL percentage calculated from the segmented images and detected apexes demonstrated excellent agreement with clinical assessments, with ICC exceeding 0.94. Stage and grade prediction for periodontitis showed robust performance specifically for advanced stages (III/IV) and grades (C) with an F1-score of 0.945 and 0.83, respectively. Conclusion: The integration of U-Net and YOLO-v9 models for BL segmentation and apex detection on PA radiographs proved effective in enhancing the accuracy and reliability of periodontitis diagnosis and grading.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".