Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Image Data Set
3.2. Ground-Truth Evaluation
3.3. Automated Evaluation
4. Results
- Twelve true positives (TP) (teeth with apical periodontitis, unhealthy);
- One false positive (FP) (tooth with no signs of apical periodontitis was classified as unhealthy, over-diagnosed by the tool);
- Forty-six true negatives (TN) (healthy teeth, no signs of any apical periodontitis);
- One false negative (FN) (misdiagnosed by the tool; a tooth with apical periodontitis was classified as a healthy tooth).
- Sensitivity = TP/FP + TP
- Specificity = TN/FN + TN
- Accuracy = TP + TN/TP + TN + FP + TP
- F1 score = 2 ∗ TP / (2 ∗ TP + FP + FN)
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Author, Study Location, and Year of Publication | Sample Size (Periapical Radiographs) | X-ray Technology | AI Model | Sensitivity | Specificity | Accuracy | F1 Score |
---|---|---|---|---|---|---|---|
Hamdan et al., United States, 2022 [28] | 68 | Photostimulable phosphor (PSP) plates scanned using ScanX (Air Techniques, Hicksville, NY, USA), Soredex Digora Optime (Kavo Dental, Charlotte, NC, USA) Sirona Schick33 Direct Digital Sensor (Dentsply Sirona, Charlotte, NC, USA) XDR Anatomic Sensor (Cyber Medical Imaging, Los Angeles, CA, USA). | CNN (Denti.AI) | 93.1% (by case) 88.8% (by lesion) | 73.3% (by case) | N/A | N/A |
Li et al., China, 2022 [29] | 4129 | N/A | ResNet-18 | 82% | 84% | N/A | 0.82 |
Moidu et al., India, 2022 [30] | 1950 | Size-2 CMOS RVG sensor (Kodak RVG 5100, Eastman Kodak Company, France) | YOLO (You Only Look Once) version 3 | 92.1% | 76% | 86.3% | 0.89 |
Chen et al., China, 2021 [31] | 2900 | Digital | Fast-R-CNN (Fast Region-based convolutional neural network) | N/A | N/A | N/A | N/A |
Li et al., Taiwan, 2021 [32] | 476 | N/A | CNN | 94.87% | 90% | 92.75% | N/A |
Li et al., Canada, 2007 [33] | 60 | N/A | SVM (support vector machine) | N/A | N/A | N/A | N/A |
Caputo et al., Italy, 2000. [34] | 54 | Digital RadioVisioGraphy (RVG) | Neural network | N/A | N/A | N/A | N/A |
Healthy | Unhealthy | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
AI-based method | False Negative | False Positive | ||||||||
46 | 47 | 1 | 12 | 13 | 1 | |||||
True Negative | True Positive | |||||||||
Ground truth method | 47 | 13 |
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Issa, J.; Jaber, M.; Rifai, I.; Mozdziak, P.; Kempisty, B.; Dyszkiewicz-Konwińska, M. Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review. Medicina 2023, 59, 768. https://doi.org/10.3390/medicina59040768
Issa J, Jaber M, Rifai I, Mozdziak P, Kempisty B, Dyszkiewicz-Konwińska M. Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review. Medicina. 2023; 59(4):768. https://doi.org/10.3390/medicina59040768
Chicago/Turabian StyleIssa, Julien, Mouna Jaber, Ismail Rifai, Paul Mozdziak, Bartosz Kempisty, and Marta Dyszkiewicz-Konwińska. 2023. "Diagnostic Test Accuracy of Artificial Intelligence in Detecting Periapical Periodontitis on Two-Dimensional Radiographs: A Retrospective Study and Literature Review" Medicina 59, no. 4: 768. https://doi.org/10.3390/medicina59040768