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A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms

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Abstract

Almost 50% of individuals around the globe are unaware of diabetes and its complications. So, an early screening of diabetes is very important at this current situation. To overcome the difficulties such as pain and discomfort to the subjects obtained from the biochemical diagnostic procedures; an infrared thermography is the diagnostic technique which measures the skin surface temperature noninvasively. Thus, the aim of our proposed study was to evaluate the type II diabetes in facial thermograms and to develop a computer aided diagnosis (CAD) system to classify the normal and diabetes. The facial thermograms (n = 160) including male (n = 79) and female (n = 81) were captured using FLIR A 305sc infrared thermal camera. The Haralick textural features were extracted from the facial thermograms based on gray level co-occurrence matrix algorithm. The TROI, TMAX, and TTOT are the statistical temperature parameters exhibited a significant negative correlation with HbA1c (r = − 0.421, − 0.411, − 0.242, p < 0.01 (TROI); r = − 0.259, p < 0.01(TMAX) and − 0.173, p < 0.05 (TTOT)). An optimal regression equation has been constructed by using the significant facial variables and standard HbA1c values. The model has achieved sensitivity, specificity, and accuracy rate as 91.42%, 88.57%, and 90% respectively. The anthropometrical variables, extracted textural features and temperature parameters were fed into the classifiers and their performances were compared. The Support Vector Machine outperformed the Linear Discriminant Analysis (84.37%) and k-Nearest Neighbor (81.25%) classifiers with the maximum accuracy rate of 89.37%. The developed CAD system has achieved 89.37% of accuracy rate for the classification of diabetes. Thus, the facial thermography could be used as the basic non-invasive prognostic tool for the evaluation of type II diabetes mellitus.

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References

  1. World Health Organization (2019) Classification of diabetes mellitus. WHO, Geneva

    Google Scholar 

  2. American Diabetes Association (2019) Classification and diagnosis of diabetes mellitus: standards of medical care in diabetes. Diabetes Care 42:S13–S28

    Google Scholar 

  3. Swinburn BA, Caterson I, Seidell JC, James WP (2004) Diet, nutrition and the prevention of excess weight gain and obesity. Public Health Nutr 7:123–146

    CAS  PubMed  Google Scholar 

  4. Kaveeshwar SA, Cornwall J (2014) The current state of diabetes mellitus in India. Australas Med J 7:45–48

    PubMed  PubMed Central  Google Scholar 

  5. Roglic G (2016) WHO global report on diabetes: a summary. Int J Noncommun Dis 1:5

    Google Scholar 

  6. Mohan V, Sandeep S, Deepa R, Shah B, Varghese C (2007) Epidemiology of type 2 diabetes: Indian scenario. Indian J Med Res 125:217–230

    CAS  PubMed  Google Scholar 

  7. Ciudin A, Hernandez C, Simo R (2012) Noninvasive methods of glucose measurement: current status and future perspectives. Curr Diabetes Rev 8:48–54

    CAS  PubMed  Google Scholar 

  8. Cox ME, Edelman D (2009) Tests for screening and diagnosis of type 2 diabetes. Clin Diabetes 27:132–138

    Google Scholar 

  9. Solnica B, Naskalski JW, Sieradzi J (2003) Analytical performance of glucometers used for routine glucose self-monitoring of diabetic patients. Clin Chim Acta 331:29–35

    CAS  PubMed  Google Scholar 

  10. Ng EYK (2009) A review of thermography as promising non-invasive detection modality for breast tumor. Int J Therm Sci 48:849–859

    CAS  Google Scholar 

  11. Hardy JD (1934) The radiation of heat from the human body: I. An instrument for measuring the radiation and surface temperature of the skin. J Clin Invest 13:593–604

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Flesch U (1983) The application of infrared-sensors in medicine. Adv Infrared Sens Technol 0395:210–219

    Google Scholar 

  13. Faust O, Acharya UR, Ng EYK, Hong TJ, Yu W (2014) Application of infrared thermography in computer aided diagnosis. Infrared Phys Technol 66:160–175

    PubMed  PubMed Central  Google Scholar 

  14. Ring F (2010) Thermal imaging today and its relevance to diabetes. J Diabetes Sci Technol 4:857–862

    PubMed  PubMed Central  Google Scholar 

  15. Ring EFJ, Ammer K (2000) The technique of infrared imaging in medicine. Thermol Int 10:7–14

    Google Scholar 

  16. Formenti D, Ludwig N, Gargano M, Gondola M, Dellerma N, Caumo A, Alberti G (2013) Thermal imaging of exercise-associated skin temperature changes in trained and untrained female subjects. Ann Biomed Eng 41:863–871

    PubMed  Google Scholar 

  17. Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno J, Gonzalez-Bernal J (2016) Narrative review: diabetic foot and infrared thermography. Infrared Phys Technol 78:105–117

    Google Scholar 

  18. Pontes SMM, Melo LHP, Maia NPS, Nogueira ANC, Vasconcelos TB, Pereira EDB, Bastos VPD, Holanda MA (2017) Influence of the ventilator mode on acute adverse effects and facial thermography after noninvasive ventilation. J Bras Pneumol 43:87–94

    PubMed  PubMed Central  Google Scholar 

  19. Alan Weinstein S, Weinstein G, Weinstein EL, Gelb M (1991) Facial thermography, basis, protocol, and clinical value. J Craniomandib Sleep Pract 9:201–211

    Google Scholar 

  20. Sivanandam S, Anburajan M, Venkataraman B, Menaka M, Sharath D (2012) Medical thermography: a diagnostic approach for type 2 diabetes mellitus based on non-contact infrared thermal imaging. Endocrine 42:343–351

    CAS  PubMed  Google Scholar 

  21. Schulte BP, Bomhof MA, Aarts NJ (1975) Facial thermography in the diagnosis of cerebrovascular disease and in evaluation of carotid endarterectomy. Clin Neurol Neurosurg 78:118–130

    CAS  PubMed  Google Scholar 

  22. Thiruvengadam J, Anburajan M, Menaka M, Venkataraman B (2014) Potential of thermal imaging as a tool for prediction of cardiovascular disease. J Med Phys 39:98–105

    PubMed  PubMed Central  Google Scholar 

  23. Ludwig N, Formenti D, Rossi A (2016) Assessing facial skin temperature asymmetry in different methods. Proceedings of QIRT

  24. Haddad DS, Brioschi ML, Baladi MG, Arita ES (2016) A new evaluation of heat distribution on facial skin surface by infrared thermography. Dentomaxillofac Radiol 45:20150264

    PubMed  PubMed Central  Google Scholar 

  25. Nathan DM (2009) International expert committee report on the role of the A1C assay in the diagnosis of diabetes. Diabetes Care 32:1327–1334

    CAS  Google Scholar 

  26. American Diabetes Association (2018) Standards of medical care in diabetes-2018. Diabetes Care 41:S1–S118

    Google Scholar 

  27. Amalu W, Block J, Chaudhry A (2002) Standards and protocols in clinical thermographic imaging. Int Acad Clin Thermol 1–35

  28. Ludwig N, Formenti D, Gargano M, Alberti G (2014) Skin temperature evaluation by infrared thermography: comparison of image analysis methods. Infrared Phys Technol 62:1–6

    Google Scholar 

  29. Gomez W, Pereira WCA, Infantosi AFC (2012) Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans Med Imaging 31:1889–1899

    CAS  PubMed  Google Scholar 

  30. Haralick RM, Shanmugam K, Its'Hak D (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Google Scholar 

  31. Lo CS, Wang CM (2012) Support vector machine for breast MR image classification. Comput Math Appl 64:1153–1162

    Google Scholar 

  32. Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14:1724–1733

    Google Scholar 

  33. Ramteke RJ, Yashawant KM (2012) Automatic medical image classification and abnormality detection using k-nearest neighbour. Int J Adv Comput Res 4:190–196

    Google Scholar 

  34. Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39:561–577

    CAS  PubMed  Google Scholar 

  35. Bowers AJ, Zhou X (2019) Receiver operating characteristic (ROC) area under the curve (AUC): a diagnostic measure for evaluating the accuracy of predictors of education outcomes. J Educ Stud Placed Risk 24:20–46

    Google Scholar 

  36. Mungreiphy NK, Kapoor S, Sinha R (2011) Association between BMI, blood pressure, and age: study among Tangkhul Naga Tribal Males of Northeast India. J Anthropol. https://doi.org/10.1155/2011/748147

    Article  Google Scholar 

  37. Torchinsky MY, Gomez R, Rao J, Vargas A, Mercante DE, Chalew SA (2004) Poor glycemic control is associated with increased diastolic blood pressure and heart rate in children with Type 1 diabetes. J Diabetes Complicat 18:220–223

    PubMed  Google Scholar 

  38. Wang J, Liu L, Zhou Y, Wang C, Hu H, Hoff K, Guo Y, Gao X, Wang A, Wu S, Zhao X (2014) Increased fasting glucose and the prevalence of arterial stiffness: a cross-sectional study in Chinese adults. Neurol Res 36:427–433

    PubMed  Google Scholar 

  39. Jonasson H, Bergstrand S, Nystrom FH, Lanne T, Ostgren CJ, Bjarnegard NJ, Fredriksson I, Larsson M, Stromberg T (2017) Skin microvascular endothelial dysfunction is associated with type 2 diabetes independently of microalbuminuria and arterial stiffness. Diabetes Vasc Dis Res 1:1–9

    Google Scholar 

  40. Charkoudian N (2003) Skin blood flow in adult human thermoregulation: how it works, when it does not, and why. Mayo Clin Proc 78:603–612

    PubMed  Google Scholar 

  41. Irace C, Carallo C, Scavelli F, De MS, Esposito FT, Gnasso A (2014) Blood viscosity in subjects with normoglycemia and prediabetes. Diabet Care 37:488–492

    CAS  Google Scholar 

  42. Sivanandam S, Anburajan M, Venkataraman B, Menaka M, Sharath D (2013) Estimation of blood glucose by noninvasive infrared thermography for diagnosis of type 2 diabetes: an alternative for blood sample extraction. Mol Cell Endocrinol 367:57–63

    CAS  PubMed  Google Scholar 

  43. Kabeya Y, Kato K, Tomita M, Katsuki T, Oikawa Y, Shimada A (2015) Association between diabetes and increased prevalence of paranasal sinus disease: A cross sectional study in Japanese Adults. J Epidemio 25:297–302

    Google Scholar 

  44. Zhang M, Lin L, Xu X, Wu X, Jin Q, Liu H (2019) Noninvasive screening tool to detect undiagnosed diabetes among young and middle-aged people in Chinese community. Int J Diab Dev Count 39:458–462

    CAS  Google Scholar 

  45. Arora AS, Singh J (2015) Paranasal sinusitis detection using thermal imaging. Proceedings of the science and information conference, London, UK, July 28–30; pp 184–188

  46. Jonsson A, Wales JK (1976) Blood glycoprotein levels in diabetes. Diabetologia 12:245–250

    CAS  PubMed  Google Scholar 

  47. Adama M, Ng EYK, Oh SH, Heng ML, Hagiwara Y, Tan JH, Tong JWK, Acharya UR (2018) Automated characterization of diabetic foot using nonlinear features extracted from thermograms. Infrared Phys Technol 89:325–337

    Google Scholar 

  48. Mahendran G, Dhanasekaran R (2015) Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput Electr Eng 45:312–323

    Google Scholar 

  49. Murugeswari S, Sukanesh R (2017) Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms. Ir J Med Sci 4:929–938

    Google Scholar 

  50. Gopinath MP, Murali S (2017) Comparative study on classification algorithm foe diabetes dataset. International Journal of Pure and Applied Mathematics 117:47–52

    Google Scholar 

  51. Janardhanan P, Heena L, Sabika F (2015) Effectiveness of support vector machines in medical data mining. J Commun Softw Syst 11:25–30

    Google Scholar 

  52. Vardasca R, Vaz L, Mendes J (2018) Classification and decision making of medical infrared thermal images. Classification in BioApps. Springer, Cham, pp 79–104

    Google Scholar 

  53. Vardasca R, Magalhaes C, Mendes J (2019) Biomedical applications of infrared thermal imaging: current state of machine learning classification. In: The 15th international workshop on advanced infrared technology and applications (AITA2019), Florence, Italy, 17–19 September 2019, 27:46

  54. Bandalakunta Gururajarao S, Venkatappa U, Shivaram JM, Sikkandar MY, Al Amoudi A (2019) Infrared Thermography and Soft Computing for Diabetic Foot Assessment. Machine Learning in Bio-Signal Analysis and Diagnostic Imaging. 73–97

  55. Nowakowski A, Kaczmarek M (2011) Active dynamic thermography-problems of implementation in medical diagnostics. Quant InfraRed Thermogr J 8:89–106

    Google Scholar 

  56. Kaczmarek M, Nowakowski A (2016) Active IR-thermal imaging in medicine. J Non destruct Eval 35:19

    Google Scholar 

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Acknowledgements

The authors would like to express their sincere gratefulness to SRM Hospital and Research Centre, Kattankulathur, Tamilnadu, India for the facility provided in the hospital to acquire the data. And, also wish to thank Dr. Anburajan Mariamichael, Director at Directorate of Radiation Safety, AMTZ, Vishakhapatnam, Andhra Pradesh, India for his technical support and advice.

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This article does not receive funding from any agencies. All expenses have been managed by the first author.

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Correspondence to Snekhalatha Umapathy.

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Thirunavukkarasu, U., Umapathy, S., Janardhanan, K. et al. A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms. Phys Eng Sci Med 43, 871–888 (2020). https://doi.org/10.1007/s13246-020-00886-z

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