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Published in: Endocrine 2/2012

01-10-2012 | Original Article

Medical thermography: a diagnostic approach for type 2 diabetes based on non-contact infrared thermal imaging

Authors: S. Sivanandam, M. Anburajan, B. Venkatraman, M. Menaka, D. Sharath

Published in: Endocrine | Issue 2/2012

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Abstract

To test the potential of Infrared (IR) thermography in diagnosing as well as predicting type 2 diabetes and its complications compared with biochemical assay of HbA1c as standard. As per American Diabetes Association criteria, threshold for diagnosis of diabetes was set as HbA1c ≥ 6.5 % (7.7 mmol L−1). The total subjects (n = 62) were studied out of which control (n = 32) and diabetic subjects (n = 30). IR camera was used to capture the thermal images of the skin for diagnosis of the disease; receiver operating characteristic (ROC) curve was used to set temperature (°C) as threshold for statistically significant body regions under t test. In diabetic group, HbA1c showed negative correlation with carotid region (r = −0.471, p < 0.01) and the mean skin temperature was lower than the normal group at body regions namely knee (p = 0.002), tibia (p = 0.003), forehead (p = 0.014), and palm (p = 0.019). The palm region showed highest area under the curve of 0.711 (95 % CI: 0.581–0.842) and the threshold was set as ≤33.85 °C, thereby sensitivity (90 %) and specificity (56 %) was obtained in determining the undiagnosed diabetes with positive predictive value of 65 %, negative predictive value of 85 % and accuracy of 73 %. As HbA1c increases, skin temperature decreases. Skin temperature enables early detection of diabetes as compared to HbA1c. The decrease in skin temperature may be due to the decrease in the basal metabolic rate, poor blood perfusion and high insulin resistance. Thermography can be used as a diagnostic as well as prognostic tool for the diabetes.
Literature
1.
go back to reference American Diabetes Association, Diagnosis and classification of diabetes mellitus. Diabetes Care 33(1), S62–S69 (2010)CrossRef American Diabetes Association, Diagnosis and classification of diabetes mellitus. Diabetes Care 33(1), S62–S69 (2010)CrossRef
2.
go back to reference A. Boutayeb, E.H. Twizell, K. Achouayb, K. Chetouani, A mathematical model for the burden of diabetes and its complications. Biomed. Eng. Online 3, 20 (2004)PubMedCrossRef A. Boutayeb, E.H. Twizell, K. Achouayb, K. Chetouani, A mathematical model for the burden of diabetes and its complications. Biomed. Eng. Online 3, 20 (2004)PubMedCrossRef
3.
go back to reference B.K. Barbara, S.F. Jeffrey, Obesity and insulin resistance. J. Clin. Invest. 106, 473–481 (2000)CrossRef B.K. Barbara, S.F. Jeffrey, Obesity and insulin resistance. J. Clin. Invest. 106, 473–481 (2000)CrossRef
5.
go back to reference American Diabetes Association, Standards of medical care in diabetes. Diabetes Care 34(1), S11–S61 (2011)CrossRef American Diabetes Association, Standards of medical care in diabetes. Diabetes Care 34(1), S11–S61 (2011)CrossRef
6.
go back to reference V. Mohan, V. Vijayachandrika, V. Gokulakrishnan, R.M. Anjana, A. Ganesan, M.B. Weber et al., A1c cut points to define various glucose intolerance groups in Asian Indians. Diabetes Care 33(3), 515–519 (2010)PubMedCrossRef V. Mohan, V. Vijayachandrika, V. Gokulakrishnan, R.M. Anjana, A. Ganesan, M.B. Weber et al., A1c cut points to define various glucose intolerance groups in Asian Indians. Diabetes Care 33(3), 515–519 (2010)PubMedCrossRef
7.
go back to reference E.F.J. Ring, Thermal imaging today and its relevance to diabetes. J. Diabetes Sci. Technol. 4(4), 857–862 (2010)PubMed E.F.J. Ring, Thermal imaging today and its relevance to diabetes. J. Diabetes Sci. Technol. 4(4), 857–862 (2010)PubMed
8.
go back to reference B.F. Jones, P. Plassmann, Digital infrared thermal imaging of human skin. IEEE Eng. Med. Biol. 21(6), 41–48 (2002)CrossRef B.F. Jones, P. Plassmann, Digital infrared thermal imaging of human skin. IEEE Eng. Med. Biol. 21(6), 41–48 (2002)CrossRef
9.
go back to reference YuV Gulyaev, A.G. Markov, G. Koreneva, P.V. Zakharov, Dynamic infrared thermography in humans. IEEE Eng. Med. Biol. 14(6), 766–771 (1995)CrossRef YuV Gulyaev, A.G. Markov, G. Koreneva, P.V. Zakharov, Dynamic infrared thermography in humans. IEEE Eng. Med. Biol. 14(6), 766–771 (1995)CrossRef
10.
go back to reference N. Kakuta, S. Yokoyama, K. Mabuchi, Human thermal models for evaluating infrared images. IEEE Eng. Med. Biol. 21(6), 65–72 (2002)CrossRef N. Kakuta, S. Yokoyama, K. Mabuchi, Human thermal models for evaluating infrared images. IEEE Eng. Med. Biol. 21(6), 65–72 (2002)CrossRef
11.
go back to reference N.A. Diakides, Infrared Imaging: an emerging technology in medicine. IEEE Eng. Med. Biol. 17(4), 17–18 (1998)CrossRef N.A. Diakides, Infrared Imaging: an emerging technology in medicine. IEEE Eng. Med. Biol. 17(4), 17–18 (1998)CrossRef
12.
go back to reference R.J. Harding, Investigating deep venous thrombosis with infrared imaging. IEEE Eng. Med. Biol. 17(4), 43–46 (1998)CrossRef R.J. Harding, Investigating deep venous thrombosis with infrared imaging. IEEE Eng. Med. Biol. 17(4), 43–46 (1998)CrossRef
13.
go back to reference F. Al-Maskari, M. El-Sadig, Prevalence of risk factors for diabetic foot complications. BMC Fam. Pract. 8, 59 (2007)PubMedCrossRef F. Al-Maskari, M. El-Sadig, Prevalence of risk factors for diabetic foot complications. BMC Fam. Pract. 8, 59 (2007)PubMedCrossRef
14.
go back to reference R.M. Anjana, M.K. Ali, M. Deepa, M. Datta, R. Unnikrishnan, M. Rema, V. Mohan, The need for obtaining accurate nationwide estimates of diabetes prevalence in India—Rationale for a national study on diabetes. Indian J. Med. Res. 133, 369–380 (2011)PubMed R.M. Anjana, M.K. Ali, M. Deepa, M. Datta, R. Unnikrishnan, M. Rema, V. Mohan, The need for obtaining accurate nationwide estimates of diabetes prevalence in India—Rationale for a national study on diabetes. Indian J. Med. Res. 133, 369–380 (2011)PubMed
15.
go back to reference S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, Baldev Raj, R. Karunanithi et al., Investigation of peripheral vascular disorders using thermal imaging. Br. J. Diabetes. Vasc. Dis. 8(2), 102–104 (2008)CrossRef S. Bagavathiappan, T. Saravanan, J. Philip, T. Jayakumar, Baldev Raj, R. Karunanithi et al., Investigation of peripheral vascular disorders using thermal imaging. Br. J. Diabetes. Vasc. Dis. 8(2), 102–104 (2008)CrossRef
16.
go back to reference E.F.J. Ring, K. Ammer, The technique of infrared imaging in medicine. Thermol. Int. 10(1), 7–14 (2000) E.F.J. Ring, K. Ammer, The technique of infrared imaging in medicine. Thermol. Int. 10(1), 7–14 (2000)
17.
go back to reference A. Ramachandran, A.K. Das, S.R. Joshi, S. Shah, K.M. Prasanna Kumar, Current status of diabetes in India and need for novel therapeutic agents. J. Assoc. Physicians India 58, 7–9 (2010) A. Ramachandran, A.K. Das, S.R. Joshi, S. Shah, K.M. Prasanna Kumar, Current status of diabetes in India and need for novel therapeutic agents. J. Assoc. Physicians India 58, 7–9 (2010)
18.
go back to reference M.S. Amer, M.M. Maher, O.H. Omar, R.A. Reda, A.E. Elawam, H.S. Sweed, Carotid intima-media thickness can predict coronary atherosclerosis in diabetic elderly patients. Eur. J. Gen. Med. 7(3), 245–249 (2010) M.S. Amer, M.M. Maher, O.H. Omar, R.A. Reda, A.E. Elawam, H.S. Sweed, Carotid intima-media thickness can predict coronary atherosclerosis in diabetic elderly patients. Eur. J. Gen. Med. 7(3), 245–249 (2010)
19.
go back to reference T. Yoshimura, E. Suzuki, K. Egawa, Y. Nishio, H. Maegawa, S. Morikawa et al., Low blood flow estimates in lower-leg arteries predict cardiovascular events in Japanese patients with type 2 diabetes with normal ankle-brachial indexes. Diabetes Care 29, 1884–1890 (2006)PubMedCrossRef T. Yoshimura, E. Suzuki, K. Egawa, Y. Nishio, H. Maegawa, S. Morikawa et al., Low blood flow estimates in lower-leg arteries predict cardiovascular events in Japanese patients with type 2 diabetes with normal ankle-brachial indexes. Diabetes Care 29, 1884–1890 (2006)PubMedCrossRef
20.
go back to reference S. Shin, Y. Ku, N. Babu, M. Singh, Erythrocyte deformability and its variation in diabetes mellitus. Indian J. Exp. Biol. 45, 121–128 (2007)PubMed S. Shin, Y. Ku, N. Babu, M. Singh, Erythrocyte deformability and its variation in diabetes mellitus. Indian J. Exp. Biol. 45, 121–128 (2007)PubMed
21.
go back to reference K. Roback, An overview of temperature monitoring devices for early detection of diabetic ulcers. Expert Rev. Med. Devices 7(5), 711–718 (2010)PubMedCrossRef K. Roback, An overview of temperature monitoring devices for early detection of diabetic ulcers. Expert Rev. Med. Devices 7(5), 711–718 (2010)PubMedCrossRef
22.
go back to reference M. Bharara, J.E. Cobb, D.J. Claremont, Thermography and thermometry in the assessment of diabetic neuropathic foot: a case for furthering the role of thermal techniques. Int. J. Extrem Wounds 5(4), 250–260 (2006)CrossRef M. Bharara, J.E. Cobb, D.J. Claremont, Thermography and thermometry in the assessment of diabetic neuropathic foot: a case for furthering the role of thermal techniques. Int. J. Extrem Wounds 5(4), 250–260 (2006)CrossRef
23.
go back to reference A.D. Baron, G. Brechtel-Hook, A. Johnson, D. Hardind, Skeletal muscle blood flow. A possible link between insulin resistance and blood pressure. Hypertension 21(2), 129–135 (1993)PubMedCrossRef A.D. Baron, G. Brechtel-Hook, A. Johnson, D. Hardind, Skeletal muscle blood flow. A possible link between insulin resistance and blood pressure. Hypertension 21(2), 129–135 (1993)PubMedCrossRef
24.
go back to reference M. Juonala, J.S.A. Viikari, T. Ronnemaa, H. Helenius, L. Taittonen, O.T. Raitakari, Elevated blood pressure in adolescent boys predicts endothelial dysfunction. Hypertension 48, 424–430 (2006)PubMedCrossRef M. Juonala, J.S.A. Viikari, T. Ronnemaa, H. Helenius, L. Taittonen, O.T. Raitakari, Elevated blood pressure in adolescent boys predicts endothelial dysfunction. Hypertension 48, 424–430 (2006)PubMedCrossRef
25.
go back to reference Y. Chen, Y. Huang, X. Li, M. Xu, Y. Bi, Y. Zhang et al., Association of arterial stiffness with HbA1c in 1,000 type 2 diabetic patients with or without hypertension. Endocrine 36, 262–267 (2009)PubMedCrossRef Y. Chen, Y. Huang, X. Li, M. Xu, Y. Bi, Y. Zhang et al., Association of arterial stiffness with HbA1c in 1,000 type 2 diabetic patients with or without hypertension. Endocrine 36, 262–267 (2009)PubMedCrossRef
26.
go back to reference D. Umpierre, P. Ribiero, C.K. Cramer, C.B. Leitao, T.N. Zucatti, M.J. Azevedo et al., Physical activity advice only or structured exercise training and association with HbA1c levels in type 2 diabetes. JAMA 305(17), 1790–1799 (2011)PubMedCrossRef D. Umpierre, P. Ribiero, C.K. Cramer, C.B. Leitao, T.N. Zucatti, M.J. Azevedo et al., Physical activity advice only or structured exercise training and association with HbA1c levels in type 2 diabetes. JAMA 305(17), 1790–1799 (2011)PubMedCrossRef
27.
go back to reference M.P. Bahillo-Curieses, F. Hermoso-Lopez, M.J. Martinez-Sopena, P. Cobreros-Garcia, P. Garcia-Saeta, M. Triguez-Garcia et al., Prevalence of insulin resistance and impaired glucose tolerance in a sample of obese Spanish children and adolescents. Endocrine (2011). doi:10.1007/s12020-011-9540-8 M.P. Bahillo-Curieses, F. Hermoso-Lopez, M.J. Martinez-Sopena, P. Cobreros-Garcia, P. Garcia-Saeta, M. Triguez-Garcia et al., Prevalence of insulin resistance and impaired glucose tolerance in a sample of obese Spanish children and adolescents. Endocrine (2011). doi:10.​1007/​s12020-011-9540-8
28.
go back to reference F. Amati, J.J. Dube, P.M. Coen, M. Stefanovic-Racic, F.G.S. Toledo, B.H. Goodpaster, Physical inactivity and obesity underlie the insulin resistance of aging. Diabetes Care 32(8), 1547–1549 (2009)PubMedCrossRef F. Amati, J.J. Dube, P.M. Coen, M. Stefanovic-Racic, F.G.S. Toledo, B.H. Goodpaster, Physical inactivity and obesity underlie the insulin resistance of aging. Diabetes Care 32(8), 1547–1549 (2009)PubMedCrossRef
Metadata
Title
Medical thermography: a diagnostic approach for type 2 diabetes based on non-contact infrared thermal imaging
Authors
S. Sivanandam
M. Anburajan
B. Venkatraman
M. Menaka
D. Sharath
Publication date
01-10-2012
Publisher
Springer US
Published in
Endocrine / Issue 2/2012
Print ISSN: 1355-008X
Electronic ISSN: 1559-0100
DOI
https://doi.org/10.1007/s12020-012-9645-8

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