Low Complexity Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease

Anurag       Satpathy; Ganapati       Panda; Rajasekhar       Gogula; Renu       Sharma

doi:10.2174/2210327909666191211125358

Abstract

Background / Objective: The paper addresses a specific clinical problem of diagnosis of periodontal disease with an objective to develop and evaluate the performance of low complexity Adaptive Nonlinear Models (ANM) using nonlinear expansion schemes and describes the basic structure and development of ANMs in detail.

Methods: Diagnostic data pertaining to periodontal findings of teeth obtained from patients have been used as inputs to train and validate the proposed models.

Result: Results obtained from simulations experiments carried out using various nonlinear expansion schemes have been compared in terms of various performance measures such as Mean Absolute Percentage Error (MAPE), matching efficiency, sensitivity, specificity, false positive rate, false negative rate and diagnostic accuracy.

Conclusion: The ANM with seven trigonometric expansion scheme demonstrates the best performance in terms of all measures yielding a diagnostic accuracy of 99.11% compared to 94.64% provided by adaptive linear model.

Keywords: Periodontal disease, gingivitis, chronic periodontitis, diagnosis, low complexity, adaptive nonlinear model, neural networks, decision support system, soft computing.

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Adv Exp Med Biol Bascones-Martínez A. 771 76 2012 10.1007/978-1-4614-5441-0_9 Bascones-Martínez A.; Arias-Herrera S.; Criado-Cámara E.; Bascones-Ilundáin J.; Bascones-Ilundáin C.; Periodontal disease and diabetes. Adv Exp Med Biol 2012,771,76-87 J Clin Periodontol Borgnakke W.S. 40 S135 2013 10.1111/jcpe.12080 Borgnakke W.S.; Ylostalo P.V.; Taylor G.W.; Genco R.J.; Effect of periodontal disease on diabetes: Systematic review of epidemiologic observational evidence. J Clin Periodontol 2013,40(14),S135-S152 J Clin Periodontol Chapple I.L. 40 S106 2013 10.1111/jcpe.12077 Chapple I.L.; Genco R.; Diabetes and periodontal diseases: Consensus report of the Joint EFP/AAP Workshop on Periodontitis and Systemic Diseases. J Clin Periodontol 2013,40(14),S106-S112 Obes Res Clin Pract Satpathy A. 9 513 2015 10.1016/j.orcp.2015.01.005 Satpathy A.; Ravindra S.; Thakur S.; Kulkarni S.; Porwal A.; Panda S.; Serum interleukin-1β in subjects with abdominal obesity and periodontitis. Obes Res Clin Pract 2015,9(5),513-521 J Periodontol Harris R.J. 74 672 2003 10.1902/jop.2003.74.5.672 Harris R.J.; Untreated periodontal disease: A follow-up on 30 cases. J Periodontol 2003,74(5),672-678 J Periodontal Res Ferreira M.C. 52 651 2017 10.1111/jre.12436 Ferreira M.C.; Dias-Pereira A.C.; Branco-de-Almeida L.S.; Martins C.C.; Paiva S.M.; Impact of periodontal disease on quality of life: A systematic review. J Periodontal Res 2017,52(4),651-665 J Indian Soc Periodontol Baishya B. 23 163 2019 10.4103/jisp.jisp_383_18 Baishya B.; Satpathy A.; Nayak R.; Mohanty R.; Oral hygiene status, oral hygiene practices and periodontal health of brick kiln workers of Odisha. J Indian Soc Periodontol 2019,23(2),163-167 Exp Clin Cardiol Kannathal N. 8 206 2003 Kannathal N.; Acharya U.R.; Lim C.M.; Sadasivan P.; Krishnan S.; Classification of cardiac patient states using artificial neural networks. Exp Clin Cardiol 2003,8(4),206-211 Conf Proc IEEE Eng Med Biol Soc Zhang F. 2011 7111 2011 10.1109/IEMBS.2011.6091797 Zhang F.; Feng M.; Pan S.J.; Artificial neural network based intracranial pressure mean forecast algorithm for medical decision support. Conf Proc IEEE Eng Med Biol Soc 2011,2011,7111-7114 J Epidemiol Community Health Vineis P. 62 273 2008 10.1136/jech.2007.063644 Vineis P.; Methodological insights: Fuzzy sets in medicine. J Epidemiol Community Health 2008,62(3),273-278 IEEE Trans Inf Technol Biomed Manzi de Arantes W. 14 916 2010 10.1109/TITB.2009.2020063 Manzi de Arantes W.; Verdier C.; Defining quality-measurable medical alerts from incomplete data through fuzzy linguistic variables and modifiers. IEEE Trans Inf Technol Biomed 2010,14,916-922 Crit Care Med Clermont G. 29 291 2001 10.1097/00003246-200102000-00012 Clermont G.; Angus D.C.; DiRusso S.M.; Griffin M.; Linde-Zwirble W.T.; Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models. Crit Care Med 2001,29(2),291-296 Ann Intern Med Baxt W.G. 115 843 1991 10.7326/0003-4819-115-11-843 Baxt W.G.; Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991,115(11),843-848 Am J Cardiol Hedén B. 74 5 1994 10.1016/0002-9149(94)90482-0 Hedén B.; Edenbrandt L.; Haisty W.K.; Pahlm O.; Artificial neural networks for the electrocardiographic diagnosis of healed myocardial infarction. Am J Cardiol 1994,74(1),5-8 J Med Syst Er O. 34 299 2010 10.1007/s10916-008-9241-x Er O.; Temurtas F.; Tanrikulu A.C.; Tuberculosis disease diagnosis using artificial neural networks. J Med Syst 2010,34(3),299-302 J Med Syst Elveren E. 35 329 2011 10.1007/s10916-009-9369-3 Elveren E.; Yumuşak N.; Tuberculosis disease diagnosis using artificial neural network trained with genetic algorithm. J Med Syst 2011,35(3),329-332 Growth Dev Aging Lux C.J. 62 95 1998 Lux C.J.; Stellzig A.; Volz D.; Jäger W.; Richardson A.; Komposch G.; A neural network approach to the analysis and classification of human craniofacial growth. Growth Dev Aging 1998,62(3),95-106 Am Soc Cytol Dey P. 33 335 2011 Dey P.; Lamba A.; Kumari S.; Marwaha N.; Application of an artificial neural network in the prognosis of chronic myeloid leukemia. Am Soc Cytol 2011,33,335-339 Curr Opin Biotechnol Arnold M.A. 7 46 1996 10.1016/S0958-1669(96)80093-0 Arnold M.A.; Non-invasive glucose monitoring. Curr Opin Biotechnol 1996,7(1),46-49 J Med Syst Uğuz H. 36 61 2012 10.1007/s10916-010-9446-7 Uğuz H.; A biomedical system based on artificial neural network and principal component analysis for diagnosis of the heart valve diseases. J Med Syst 2012,36(1),61-72 J Cardiol Atkov O.Y. 59 190 2012 10.1016/j.jjcc.2011.11.005 Atkov O.Y.; Gorokhova S.G.; Sboev A.G.; Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. J Cardiol 2012,59(2),190-194 Biomed Eng Online Barbosa D.C. 11 3 2012 10.1186/1475-925X-11-3 Barbosa D.C.; Roupar D.B.; Ramos J.C.; Tavares A.C.; Lima C.S.; Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images. Biomed Eng Online 2012,11,3 Oral Surg Oral Med Oral Pathol Oral Radiol Endod Devito K.L. 106 879 2008 10.1016/j.tripleo.2008.03.002 Devito K.L.; de Souza Barbosa F.; Felippe F.W.N.; An artificial multilayer perceptron neural network for diagnosis of proximal dental caries. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008,106(6),879-884 Dent Traumatol Kositbowornchai S. 29 151 2013 Kositbowornchai S.; Plermkamon S.; Tangkosol T.; Performance of an artificial neural network for vertical root fracture detection: An ex vivo study. Dent Traumatol 2013,29,151-155 Bas B 70 51 2012 10.1016/j.joms.2011.03.069 Bas B; Ozgonenel O; Ozden B; Bekcioglu B; Bulut E; Kurt M; Use of artificial neural network in differentiation of subgroups of temporomandibular internal derangements: A preliminary study. J Oral and maxillofac 2012,70,51-9 Br Dent J Speight P.M. 179 382 1995 10.1038/sj.bdj.4808932 Speight P.M.; Elliott A.E.; Jullien J.A.; Downer M.C.; Zakzrewska J.M.; The use of artificial intelligence to identify people at risk of oral cancer and precancer. Br Dent J 1995,179(10),382-387 Angle Orthod Xie X. 80 262 2010 10.2319/111608-588.1 Xie X.; Wang L.; Wang A.; Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod 2010,80(2),262-266 Stud Health Technol Inform Kim E.Y. 146 745 2009 Kim E.Y.; Lim K.O.; Rhee H.S.; Predictive modeling of dental pain using neural network. Stud Health Technol Inform 2009,146,745-746 Niger J Clin Pract Ozden F.O. 18 416 2015 10.4103/1119-3077.151785 Ozden F.O.; Özgönenel O.; Özden B.; Aydogdu A.; Diagnosis of periodontal diseases using different classification algorithms: A preliminary study. Niger J Clin Pract 2015,18(3),416-421 Development of a classification system for periodontal diseases and conditions Annals of periodontology / the American Academy of Periodontology Armitage GC 1999 10.1902/annals.1999.4.1.1 Armitage GC; Development of a classification system for periodontal diseases and conditions Annals of periodontology / the American Academy of Periodontology 1999 J Periodontol Kornman K.S. 79 1560 2008 10.1902/jop.2008.080213 Kornman K.S.; Mapping the pathogenesis of periodontitis: A new look. J Periodontol 2008,79(8),1560-1568 Int J Dent Hyg Hatti S. 5 218 2007 10.1111/j.1601-5037.2007.00249.x Hatti S.; Ravindra S.; Satpathy A.; Kulkarni R.D.; Parande M.V.; Biofilm inhibition and antimicrobial activity of a dentifrice containing salivary substitutes. Int J Dent Hyg 2007,5(4),218-224 J Periodontol Socransky S.S. 48 497 1977 10.1902/jop.1977.48.9.497 Socransky S.S.; Microbiology of periodontal disease- Present status and future considerations. J Periodontol 1977,48(9),497-504 Curr Opin Dent Caton J.G. 1 17 1991 Caton J.G.; Quiñones C.R.; Etiology of periodontal diseases. Curr Opin Dent 1991,1(1),17-28 Arch Oral Biol Heller D. 57 973 2012 10.1016/j.archoralbio.2012.02.003 Heller D.; Silva-Boghossian C.M.; do Souto R.M.; Colombo A.P.; Subgingival microbial profiles of generalized aggressive and chronic periodontal diseases. Arch Oral Biol 2012,57(7),973-980 J Clin Periodontol Slots J. 12 540 1985 10.1111/j.1600-051X.1985.tb01388.x Slots J.; Emrich L.J.; Genco R.J.; Rosling B.G.; Relationship between some subgingival bacteria and periodontal pocket depth and gain or loss of periodontal attachment after treatment of adult periodontitis. J Clin Periodontol 1985,12(7),540-552 J Istanb Univ Fac Dent Meseli S.E. 51 11 2017 10.17096/jiufd.40993 Meseli S.E.; Kuru B.; Kuru L.; Relationships between initial probing depth and changes in the clinical parameters following non-surgical periodontal treatment in chronic periodontitis. J Istanb Univ Fac Dent 2017,51(3),11-17 Eur J Clin Microbiol Pattnaik S. 34 2103 2015 10.1007/s10096-015-2459-x Pattnaik S.; Anand N.; Chandrasekaran S.C.; Chandrashekar L.; Mahalakshmi K.; Satpathy A.; Clinical and antimicrobial efficacy of a controlled-release device containing chlorhexidine in the treatment of chronic periodontitis. Eur J Clin Microbiol 2015,34,2103-2110 Biomed Res Tomofuji T. 32 343 2011 10.2220/biomedres.32.343 Tomofuji T.; Ekuni D.; Irie K.; Relationships between periodontal inflammation, lipid peroxide and oxidative damage of multiple organs in rats. Biomed Res 2011,32(5),343-349 J Clin Periodontol Joss A. 21 402 1994 10.1111/j.1600-051X.1994.tb00737.x Joss A.; Adler R.; Lang N.P.; Bleeding on probing. A parameter for monitoring periodontal conditions in clinical practice. J Clin Periodontol 1994,21(6),402-408 Evaluation of bleeding on probing and gingival crevicular fluid enzyme activity for detection of periodontally active sites during supportive periodontal therapy Ito H. 2012 Ito H.; Numabe Y.; Sekino S.; Murakashi E.; Iguchi H.; Hashimoto S.; Evaluation of bleeding on probing and gingival crevicular fluid enzyme activity for detection of periodontally active sites during supportive periodontal therapy 2012 J Clin Periodontol Polson A.M. 7 351 1980 10.1111/j.1600-051X.1980.tb02008.x Polson A.M.; Interrelationship of inflammation and tooth mobility (trauma) in pathogenesis of periodontal disease. J Clin Periodontol 1980,7(5),351-360 J Clin Periodontol Giargia M. 24 785 1997 10.1111/j.1600-051X.1997.tb01190.x Giargia M.; Lindhe J.; Tooth mobility and periodontal disease. J Clin Periodontol 1997,24(11),785-795 Quintessence Int Harrison J.W. 30 846 1999 Harrison J.W.; Svec T.A.; The hopeless tooth: When is treatment futile? Quintessence Int 1999,30(12),846-850 J Prosthodont Porwal A. 27 232 2018 Porwal A.; Satpathy A.; Jain P.; Ponnanna A.A.; Association of neutral zone position with age, gender, and period of edentulism. J Prosthodont 2018,27(3),232-239 J Am Dent Assoc Kassab M.M. 134 220 2003 10.14219/jada.archive.2003.0137 Kassab M.M.; Cohen R.E.; The etiology and prevalence of gingival recession. J Am Dent Assoc 2003,134(2),220-225 Dent Res J Chrysanthakopoulos N.A. 8 64 2011 Chrysanthakopoulos N.A.; Aetiology and severity of gingival recession in an adult population sample in Greece. Dent Res J 2011,8(2),64-70 J Indian Soc Periodontol Panda S. 20 216 2016 Panda S.; Del Fabbro M.; Satpathy A.; Das A.C.; Pedicled buccal fat pad graft for root coverage in severe gingival recession defect. J Indian Soc Periodontol 2016,20(2),216-219 J Periodontol Svärdström G. 71 579 2000 10.1902/jop.2000.71.4.579 Svärdström G.; Wennström J.L.; Periodontal treatment decisions for molars: An analysis of influencing factors and long-term outcome. J Periodontol 2000,71(4),579-585 Med Oral Patol Oral Cir Bucal Sánchez-Pérez A. 14 e554 2009 10.4317/medoral.14.e554 Sánchez-Pérez A.; Moya-Villaescusa M.J.; Periodontal disease affecting tooth furcations- A review of the treatments available. Med Oral Patol Oral Cir Bucal 2009,14(10),e554-e557 J Periodontol Taubman M.A. 76 2033 2005 10.1902/jop.2005.76.11-S.2033 Taubman M.A.; Valverde P.; Han X.; Kawai T.; Immune response: The key to bone resorption in periodontal disease. J Periodontol 2005,76,2033-2041 Interface Oral Health Sci Kanzaki H. 2011 173 2011 Kanzaki H.; Han X.; Asami Y.; Suzuki M.; Kawai T.; Taubman M.; Inhibition of T-Cell-Mediated and Infection-Induced Periodontal Bone Resorption by TACE Blockade. Interface Oral Health Sci 2011,2011,173-175 Clin Dev Immunol Di Benedetto A. 2013 2013 10.1155/2013/503754 Di Benedetto A.; Gigante I.; Colucci S.; Grano M.; Periodontal disease: Linking the primary inflammation to bone loss. Clin Dev Immunol 2013,2013 J Periodontol Adriaens P.A. 59 222 1988 10.1902/jop.1988.59.4.222 Adriaens P.A.; De Boever J.A.; Loesche W.J.; Bacterial invasion in root cementum and radicular dentin of periodontally diseased teeth in humans. A reservoir of periodontopathic bacteria. J Periodontol 1988,59(4),222-230 J Clin Periodontol Giuliana G. 24 478 1997 10.1111/j.1600-051X.1997.tb00215.x Giuliana G.; Ammatuna P.; Pizzo G.; Capone F.; D’Angelo M.; Occurrence of invading bacteria in radicular dentin of periodontally diseased teeth: Microbiological findings. J Clin Periodontol 1997,24(7),478-485 J Oral Sci Satpathy A. 55 99 2013 10.2334/josnusd.55.99 Satpathy A.; Ravindra S.; Porwal A.; Das A.C.; Kumar M.; Mukhopadhyay I.; Effect of alcohol consumption status and alcohol concentration on oral pain induced by alcohol-containing mouthwash. J Oral Sci 2013,55(2),99-105 Oral Surg Oral Med Oral Pathol Ramfjord S. 6 516 1953 10.1016/0030-4220(53)90117-0 Ramfjord S.; The histopathology of inflammatory gingival enlargement. Oral Surg Oral Med Oral Pathol 1953,6(4),516-535 Case Rep Dent Pattnaik N. 2015 2015 10.1155/2015/678504 Pattnaik N.; Satpathy A.; Mohanty R.; Nayak R.; Sahoo S.; Interdisciplinary Management of Gingivitis Artefacta Major: A Case Series. Case Rep Dent 2015,2015 Chemom Intell Lab Syst Svozil D. 39 43 1997 10.1016/S0169-7439(97)00061-0 Svozil D.; Kvasnicka V.; Pospichal Jí. Introduction to multi-layer feed-forward neural networks. Chemom Intell Lab Syst 1997,39,43-62 J Can Chiropr Assoc Bruno P. 55 69 2011 Bruno P.; The importance of diagnostic test parameters in the interpretation of clinical test findings: The prone hip extension test as an example. J Can Chiropr Assoc 2011,55(2),69-75 Northwest Dent Res Bains R. 4 2 1994 Bains R.; Turner D.W.; Greener E.H.; Comparison of statistical and neural network analysis of periodontal data. Northwest Dent Res 1994,4(2),2-3 Kabari L.G. 2009 Kabari L.G.; Bakpo F.S.; Diagnosing skin diseases using an artificial neural network. 2nd International Conference on Adaptive Science Appl Artif Intell Devito K.L. 23 872 2009 10.1080/08839510903246757 Devito K.L.; Felippe F.W.N.; Using a neural network for supporting radiographic diagnosis of dental caries. Appl Artif Intell 2009,23,872-882 J Periodontal Implant Sci Lee J.H. 48 114 2018 10.5051/jpis.2018.48.2.114 Lee J.H.; Kim D.H.; Jeong S.N.; Choi S.H.; Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. J Periodontal Implant Sci 2018,48(2),114-123 IEEE Trans Syst Man Cybern B Cybern Patra J.C. 29 254 1999 10.1109/3477.752797 Patra J.C.; Pal R.N.; Chatterji B.N.; Panda G.; Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans Syst Man Cybern B Cybern 1999,29(2),254-262 BMC Cardiovasc Disord Grossi E. 6 20 2006 10.1186/1471-2261-6-20 Grossi E.; How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: Problems with the current methods. BMC Cardiovasc Disord 2006,6,20 J Dent Lee J.H. 77 106 2018 10.1016/j.jdent.2018.07.015 Lee J.H.; Kim D.H.; Jeong S.N.; Choi S.H.; Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent 2018,77,106-111

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DOI https://dx.doi.org/10.2174/2210327909666191211125358	Print ISSN 2210-3279
Publisher Name Bentham Science Publisher	Online ISSN 2210-3287

International Journal of Sensors, Wireless Communications and Control

Low Complexity Adaptive Nonlinear Models for the Diagnosis of Periodontal Disease

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