Machine learning models for analysis of biomarkers of chronic periodontitis
https://doi.org/10.33667/2078-5631-2022-19-55-59
Abstract
Periodontitis is a multifactorial inflammatory chronic disease initiated by dysbiosis of the commensal microbiota of the oral cavity. With the advent of the multi-ohm approach, which creates datasets with multiple features, machine learning algorithms have become an important technique in translational research. The most effective methods for identifying highly specific interactions of biomarkers with clinical parameters and their implementation in interpretable accurate clinical diagnoses are machine learning algorithms.
The aim of the study was to evaluate laboratory diagnostic indicators that are significant for predicting the severity of periodontitis using machine learning models.
We examined 133 patients aged 22 to 73 years with a diagnosis of chronic periodontitis, as well as 53 people without periodontal pathology. After the examination and assessment of the condition of the periodontium of patients, a biological sample of the periodontal fluid was taken. Real-time PCR was used to evaluate periodontopathogenic microflora and mRNA of pro-inflammatory cytokines. Cells of innate and acquired immunity were evaluated in mixed saliva. Machine learning was performed using logistic regression algorithms, naive Bayes classifier, ‘the Random Forest’ with 25 % training. When trained to 25 %, the naive Bayes classifier showed an accuracy of 23 %, logistic regression – 29 %. ‘The Random Forest’ showed 100 % accuracy and selected the following biomarkers associated with periodontitis severity: Porphyromonas endodontalis; CD 3+, CD 14+, CD 19+5–B 27+ cells; mRNA IL-1β, IL-10, IL-18, GATA3, TNFa, TLR 4. In chronic periodontitis, there is a relationship between local indicators of the immune-inflammatory process, such as mRNA of pro-inflammatory cytokines, cells of the immune system, and the severity of pathology. In the analysis of a multifactorial disease such as chronic periodontitis, a machine learning model optimized for a large heterogeneous data set with a large patient sample should be used.
About the Author
V. P. Mudrov
Russian Medical Academy for Continuing Professional Education; Diagnostic Clinical Centre No. 1
Russian Federation
Russian Federation
Valery P. Mudrov, PhD Med, assistant, doctor of Clinical Laboratory Diagnostics
Academic Educational Centre for Fundamental and Translational Medicine
Dept of Medical Biochemistry and Immunopathology
Laboratory Dept
Moscow
References
1. Ahmed N., Abbasi M. S., Zuberi F., Qamar W., Halim M. S. B., Maqsood A., Alam M. K. Artificial intelligence techniques: analysis, applicationand outcome in dentistry – A systematic review. Biomed Research International. 2021; 2021: 9751564. DOI: 10.1155/2021/9751564.
2. Khanagar S. B., Al-Ehaideb A., Maganur P. C., Vishwanathaiah S., Patil S., Baeshen H. A., Sarode S. C., Bhandi S. Developments, application, and performance of artificial intelligence in dentistry – A systematic review.Journal of dental sciences. 2021; 16 (1): 508–522. DOI: 10.1016/j.jds.2020.06.019.
3. Бонгард М. М. Проблема узнавания / М. М. Бонгард. – М.: Наука, 1967. – С. 320. – Bongard M. M. The problem of recognition. M. Science. 1967. P. 320.
4. Patil S., Albogami S., Hosmani J., Mujoo S., Kamil M. A., Mansour M. A., Abdul H. N., Bhandi S., Ahmed S. S. S. J. Artificial intelligence in the diagnosis of oral diseases: applications and pitfalls. Diagnostics (Basel). 2022; 12 (5): 1029. DOI: 10.3390/diagnostics12051029.
5. Казаков С. П. Клинико-иммунологическая диагностика и терапия хронического пародонтита / С. П. Казаков [и др.] – Москва: Эко-Пресс, 2022. – С. 136. – Kazakov S. P., Ivanov S. Yu., Mudrov V. P., Muraev A. A., Yamurkova N. F., Skuin L. M., Stepanova E. N., Ryabova V. M., Ibragimova Yu. Sh., Antonov I. I. Clinical and immunological diagnosis and therapy of chronic periodontitis. Moscow, Eco-Press. 2022. P. 136.
6. Rakic M., Pejcic N., Perunovic N., Vojvodic D. A Roadmap towards precision periodontics. Medicina (Kaunas). 2021; 57 (3): 233–244. doi: 10.3390/medicina57030233
7. Kuo T.-R., Chen C.-H. Bone biomarker for the clinical assessment of osteoporosis: recent developments and future perspectives. Biomarker research. 2017; 5 (18): 1–9. DOI: 10.1186/s40364–017–0097–4.
8. Гизатуллин Ш. Х. Синдром костно-минеральных нарушений – начальные проявления остеопороза у больных нейрохирургического профиля (патогенез и иммунопатогенез, клиническая лабораторная диагностика, алгоритм лечения, хирургическая тактика) : Учебное пособие / Ш. Х. Гизатуллин, С. П. Казаков, В. Ю. Курносенко. – М.: Эко-Пресс, 2019. – 94 с. – Gizatullin Sh. Kh. The syndrome of bone and mineral disorders is the initial manifestations of osteoporosis in neurosurgical patients (pathogenesis and immunopathogenesis, clinical laboratory diagnostics, treatment algorithm, surgical tactics). Tutorial. Sh. Kh. Gizatullin, S. P. Kazakov, V. Yu. Kurnosenko. Moscow: Eco-Press. 2019. 94 p.
9. Smets J., Shevroja E., Hügle T., Leslie W. D., Hans D. Machine learning solutions for osteoporosis – A Review. Journal of bone and mineral research. 2021; 36 (5): 833–851. DOI: 10.1002/jbmr.4292.
10. Kinney J. S., Morelli T., Oh M., Braun T. M., Ramseier C. A., Sugai J. V., Giannobile W. V. Crevicular fluid biomarkers and periodontal disease progression. Journal of clinical periodontology. 2014; 41 (2): 113–120. DOI: 10.1111/jcpe.12194.
11. Мудров В. П. Машинное обучение в поиске прогностически значимых тестов для лабораторной диагностики остеопороза. Справочник заведующего КДЛ / В. П. Мудров, М. Йовичич. – 2020. – (1): 44–56. – Mudrov V. P., Jovicic M. Machine learning in the search for prognostically significant tests for laboratory diagnosis of osteoporosis. Directory of the head of the CDL. 2020; (1): 44–56.
12. Казаков С. П. Уровень цитокинов и молекул межклеточной адгезии в плазме крови и их диагностическая эффективность при аутоиммунных и онкологических заболеваниях щитовидной железы / С. П. Казаков. – Медицинская иммунология. – 2010. – 12 (6): 559–564. – Kazakov S. P. The level of cytokines and intercellular adhesion molecules in blood plasma and their diagnostic efficiency in autoimmune and oncological diseases of the thyroid gland. Medical Immunology. 2010; 12 (6): 559–564.
13. Казаков С. П. Соотношения (индексы) цитокинов, молекул межклеточной адгезии в плазме крови, их диагностическая эффективность у больных с аутоиммунными тиреоидитами, аденомами и раком щитовидной железы / С. П. Казаков, Т. Н. Заботина, Н. Е. Кушлинский // Медицинский вестник МВД. – 2010. – 6 (49): 25–29. – Kazakov S. P., Zabotina T. N., Kushlinsky N. E. Ratios (indices) of cytokines, intercellular adhesion molecules in blood plasma, their diagnostic efficiency in patients with autoimmune thyroiditis, adenomas and thyroid cancer. Medical Bulletin of the Ministry of Internal Affairs. 2010; 6 (49): 25–29.
Review
For citations:
Mudrov V.P. Machine learning models for analysis of biomarkers of chronic periodontitis. Medical alphabet. 2022;(19):55-59. (In Russ.) https://doi.org/10.33667/2078-5631-2022-19-55-59
Views:
262
Email this article 