Evaluation of the Progression of Periodontitis with the Use of Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Patients’ Population
2.2. Clinical Periodontal Measurements
- Teeth with gingival recession of traumatic origin;
- Dental caries near cervical area of the tooth;
- The presence of CAL at the distal surface of second molar due to the malposition
- Extraction of third molar;
- An endodontic lesion in the marginal periodontium;
- Vertical root fracture [11].
2.3. Artificial Neural Network
2.3.1. ANN Construction
2.3.2. Input Signals for Artificial Neural Network
2.4. Software Simulation of ANN and the Statistical Analysis
3. Results
3.1. Basic Characteristics
3.2. Classification Assessment of the ANN
3.3. Sensitivity Analysis
3.4. Implementation of the Model into Clinical Practice
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Periodontitis Stage | Stage I | Stage II | Stage III | Stage IV | |
---|---|---|---|---|---|
Severity | Interdental CAL 1 at site of greatest loss | 1–2 mm | 3–4 mm | ≥5 mm | ≥5 mm |
Radiographic bone loss | <15% | 15–33% | Extending to mid-third of the root or beyond | Extending to mid-third of the root or beyond | |
Tooth loss | No tooths loss due to the periodontitis | No tooths loss due to the periodontitis | Tooth loss due to the periodontitis ≤ 4 | Tooth loss due to the periodontitis ≥ 5 | |
Complexity | Local | Probing depth ≤ 4 mm | Probing depth ≤ 5 mm | Probing depth ≥ 6 mm | Criteria as in III stage plus: |
Horizontal bone loss | Horizontal bone loss | Vertical bone loss ≥ 3 mm | Need for complex rehabilitation due to: | ||
Furcation II or III class | -masticatory dysfunction -secondary occlusal trauma | ||||
Moderate ridge defect | -severe occlusal defect -less than 10 opposing pairs of teeth | ||||
Extent and distribution | Localized (<30% teeth involved), generalized, molar/incisor pattern |
Periodontitis Grade | Grade A: Slow Progression | Grade B: Moderate Progression | Grade C: Rapid Progression | ||
---|---|---|---|---|---|
Primary criteria | Direct evidence of progression | Longitudinal data | Evidence of no loss over 5 years | <2 mm over 5 years | ≥2 mm over 5 years |
Indirect evidence of progression | % Bone loss/age | <0.25 | 0.25 to 1.0 | >1.0 | |
Phenotype | Heavy biofilm deposits and slow progression | Progression corresponding with biofilm deposits | Rapid progression which exceeds amount of biofilm, early onset of disease | ||
Grade modifiers | Risk factors | Smoking | Non-smoker | <10 cigarettes/day | ≥10 cigarettes/day |
Diabetes | Normoglycemic | Diabetes HbA1c < 7.0% | Diabetes HbA1c ≥ 7.0% |
Healthy (n = 10) | A (n = 15) | B (n = 43) | C (n = 42) | |
---|---|---|---|---|
Gender | ||||
Female | 6 (60.0%) | 12 (80.0%) | 30 (69.8%) | 24 (57.1%) |
Male | 4 (40.0%) | 3 (20.0%) | 13 (30.2%) | 18 (42.9%) |
Grade | ||||
gingivitis | 10 (100.0%) | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) |
1 | 0 (0.0%) | 12 (80.0%) | 0 (0.0%) | 0 (0.0%) |
2 | 0 (0.0%) | 3 (20.0%) | 12 (27.9%) | 4 (9.5%) |
3 | 0 (0.0%) | 0 (0.0%) | 28 (65.1%) | 14 (33.3%) |
Nicotinism | 1 (10.0%) | 1 (6.7%) | 2 (4.7%) | 21 (50.0%) |
Age | 33.1 (4.7) | 43.1 (5.4) | 48.1 (6.8) | 45.8 (6.5) |
API 1 (%) | 55.1 (27.1) | 64.7 (27.1) | 78.5 (21.3) | 87.3 (18.4) |
BoP 2 (%) | 40.3 (34.9) | 47.2 (25.2) | 62.5 (33.1) | 66.7 (36.2) |
PPD 3 (mm) | 2.1 (0.1) | 2.3 (0.1) | 2.8 (0.5) | 3.4 (0.9) |
CAL 4 (mm) | - | 1.7 (1.3) | 3.4 (1.8) | 4.6 (2.4) |
Training Group (n = 90) | Test Group (n = 20) | p-Value | |
---|---|---|---|
Gender | 0.5706 | ||
Female | 60 (66.7%) | 12 (60.0%) | |
Male | 30 (33.3%) | 8 (40.0%) | |
Age mean (SD) | 45.5 (7.2) | 43.9 (8.9) | 0.3849 |
Training Group (n = 90) | Test Group (n = 20) | p-Value | |
---|---|---|---|
API 1 | 79.8 (23.0) | 69.2 (25.7) | 0.0713 |
BoP 2 | 60.2 (35.0) | 59.2 (31.6) | 0.9051 |
PPD 3 | 2.9 (0.8) | 2.7 (0.7) | 0.1313 |
CAL 4 | 3.6 (2.2) | 4.1 (2.2) | 0.3952 |
Correctly% | |
---|---|
All | 84.2% |
healthy | 80.0% |
A | 100.0% |
B | 80.0% |
C | 80.0% |
Gender | |
Female | 90.9% |
Male | 75.0% |
Age (years) | |
20–30 | 100.0% |
30–40 | 80.0% |
40–50 | 83.3% |
50–60 | 85.7% |
Cigarettes | |
smoking | 100.0% |
no smoking | 83.3% |
Parameter | Correctly% |
---|---|
Cigarettes | 1.417 |
API 1 | 1.052 |
PPD 2 | 1.048 |
Age | 1.038 |
CAL 3 | 1.015 |
Gender | 1.0 |
BoP 4 | 0.994 |
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Ossowska, A.; Kusiak, A.; Świetlik, D. Evaluation of the Progression of Periodontitis with the Use of Neural Networks. J. Clin. Med. 2022, 11, 4667. https://doi.org/10.3390/jcm11164667
Ossowska A, Kusiak A, Świetlik D. Evaluation of the Progression of Periodontitis with the Use of Neural Networks. Journal of Clinical Medicine. 2022; 11(16):4667. https://doi.org/10.3390/jcm11164667
Chicago/Turabian StyleOssowska, Agata, Aida Kusiak, and Dariusz Świetlik. 2022. "Evaluation of the Progression of Periodontitis with the Use of Neural Networks" Journal of Clinical Medicine 11, no. 16: 4667. https://doi.org/10.3390/jcm11164667