Abstract

Although blood hemoglobin (Hgb) testing is a routine procedure in a variety of clinical situations, noninvasive, continuous, and real-time blood Hgb measurements are still challenging. Optical spectroscopy can offer noninvasive blood Hgb quantification, but requires bulky optical components that intrinsically limit the development of mobile health (mHealth) technologies. Here, we report spectral super-resolution (SSR) spectroscopy that virtually transforms the built-in camera (RGB sensor) of a smartphone into a hyperspectral imager for accurate and precise blood Hgb analyses. Statistical learning of SSR enables us to reconstruct detailed spectra from three color RGB data. Peripheral tissue imaging with a mobile application is further combined to compute exact blood Hgb content without a priori personalized calibration. Measurements over a wide range of blood Hgb values show reliable performance of SSR blood Hgb quantification. Given that SSR does not require additional hardware accessories, the mobility, simplicity, and affordability of conventional smartphones support the idea that SSR blood Hgb measurements can be used as an mHealth method.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (8)

S. E. Juul, R. J. Derman, and M. Auerbach, “Perinatal iron deficiency: implications for mothers and infants,” Neonatology 115, 269–274 (2019).
[Crossref]

A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. M. Sauer, D. Ramazzotti, M. Bonvini, L. A. Celi, and M. J. Douglas, “Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients,” NPJ Digit. Med. 2, 116 (2019).
[Crossref]

S. C. Mathews, M. J. McShea, C. L. Hanley, A. Ravitz, A. B. Labrique, and A. B. Cohen, “Digital health: a path to validation,” NPJ Digit. Med. 2, 38 (2019).
[Crossref]

G. Dimauro, A. Guarini, D. Caivano, F. Girardi, C. Pasciolla, and A. Iacobazzi, “Detecting clinical signs of anaemia from digital images of the palpebral conjunctiva,” IEEE Access 7, 113488 (2019).
[Crossref]

A. Signoroni, M. Savardi, A. Baronio, and S. Benini, “Deep learning meets hyperspectral image analysis: a multidisciplinary review,” J. Imaging 5, 52 (2019).
[Crossref]

D. Gedalin, Y. Oiknine, and A. Stern, “DeepCubeNet: reconstruction of spectrally compressive sensed hyperspectral images with deep neural networks,” Opt. Express 27, 35811–35822 (2019).
[Crossref]

T. R. Liu, Z. S. Wei, Y. Rivenson, K. de Haan, Y. B. Zhang, Y. C. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophoton. 12, e201900107 (2019).
[Crossref]

U.S. Government, “Clinical laboratory improvement amendments of 1988 (CLIA) proficiency testing regulations related to analytes and acceptable performance,” Fed. Regist. 84, 1536–1567 (2019).

2018 (9)

J. Shuren, B. Patel, and S. Gottlieb, “FDA regulation of mobile medical apps,” J. Am. Med. Assoc. 320, 337–338 (2018).
[Crossref]

S. R. Pasricha, K. Colman, E. Centeno-Tablante, M. N. Garcia-Casal, and J. P. Pena-Rosas, “Revisiting WHO haemoglobin thresholds to define anaemia in clinical medicine and public health,” Lancet Haematol. 5, E60–E62 (2018).
[Crossref]

M. M. Khansari, M. Tan, P. Karamian, and M. Shahidi, “Inter-visit variability of conjunctival microvascular hemodynamic measurements in healthy and diabetic retinopathy subjects,” Microvasc. Res. 118, 7–11 (2018).
[Crossref]

L. R. Smart, E. E. Ambrose, K. C. Raphael, A. Hokororo, E. Kamugisha, E. A. Tyburski, W. A. Lam, R. E. Ware, and P. T. McGann, “Simultaneous point-of-care detection of anemia and sickle cell disease in Tanzania: the rapid study,” Ann. Hematol. 97, 239–246 (2018).
[Crossref]

J. W. Cannon, “Hemorrhagic shock,” N. Engl. J. Med. 378, 370–379 (2018).
[Crossref]

M. N. Hasan, A. Fraiwan, P. Thota, T. Oginni, G. M. Olanipekun, F. Hassan-Hanga, J. Little, S. K. Obaro, and U. A. Gurkan, “Clinical testing of hemechip in Nigeria for point-of-care screening of sickle cell disease,” Blood 132, 1095 (2018).
[Crossref]

K. Vishwanath, R. Gurjar, D. Wolf, S. Riccardi, M. Duggan, and D. King, “Diffuse optical monitoring of peripheral tissues during uncontrolled internal hemorrhage in a porcine model,” Biomed. Opt. Express 9, 569–580 (2018).
[Crossref]

S. Figueiredo, C. Taconet, A. Harrois, S. Hamada, T. Gauss, M. Raux, and J. Duranteau, “How useful are hemoglobin concentration and its variations to predict significant hemorrhage in the early phase of trauma? A multicentric cohort study,” Ann. Intensive Care 8, 76 (2018).
[Crossref]

R. G. Mannino, D. R. Myers, E. A. Tyburski, C. Caruso, J. Boudreaux, T. Leong, G. D. Clifford, and W. A. Lam, “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun. 9, 4924 (2018).
[Crossref]

2017 (3)

2016 (7)

M. A. Visbal-Onufrak, R. L. Konger, and Y. L. Kim, “Telecentric suppression of diffuse light in imaging of highly anisotropic scattering media,” Opt. Lett. 41, 143–146 (2016).
[Crossref]

T. K. Koo and M. Y. Li, “A guideline of selecting and reporting intraclass correlation coefficients for reliability research,” J. Chiropr. Med. 15, 155–163 (2016).
[Crossref]

G. Finlayson, M. M. Darrodi, and M. Mackiewicz, “Rank-based camera spectral sensitivity estimation,” J. Opt. Soc. Am. A 33, 589–599 (2016).
[Crossref]

M. Jaggernath, R. Naicker, S. Madurai, M. A. Brockman, T. Ndung’u, and H. C. Gelderblom, “Diagnostic accuracy of the HemoCue HB 301, STAT-Site MHgb and URIT-12 point-of-care hemoglobin meters in a central laboratory and a community based clinic in Durban, South Africa,” Plos One 11, e0152184 (2016).
[Crossref]

T. Kim, S. H. Choi, N. Lambert-Cheatham, Z. Xu, J. E. Kritchevsky, F. R. Bertin, and Y. L. Kim, “Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology,” J. Biomed. Opt. 21, 107001 (2016).
[Crossref]

S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, and R. Weinkove, “Non-invasive detection of anaemia using digital photographs of the conjunctiva,” PLOS One 11, e0153286 (2016).
[Crossref]

S. Hutchings, D. N. Naumann, T. Harris, J. Wendon, and M. J. Midwinter, “Observational study of the effects of traumatic injury, haemorrhagic shock and resuscitation on the microcirculation: a protocol for the microshock study,” BMJ Open 6, e010893 (2016).
[Crossref]

2015 (6)

R. Hiscock, D. Kumar, and S. W. Simmons, “Systematic review and meta-analysis of method comparison studies of masimo pulse co-oximeters (Radical-7 or Pronto-7) and HemoCue absorption spectrometers (B-hemoglobin or 201+) with laboratory haemoglobin estimation,” Anaesth. Intensive Care 43, 341–350 (2015).
[Crossref]

T. Guo, R. Patnaik, K. Kuhlmann, A. J. Rai, and S. K. Sia, “Smartphone dongle for simultaneous measurement of hemoglobin concentration and detection of HIV antibodies,” Lab Chip 15, 3514–3520 (2015).
[Crossref]

D. Giavarina, “Understanding Bland Altman analysis,” Biochem. Med. 25, 141–151 (2015).
[Crossref]

R. P. McNabb, P. Challa, A. N. Kuo, and J. A. Izatt, “Complete 360 degrees circumferential gonioscopic optical coherence tomography imaging of the iridocorneal angle,” Biomed. Opt. Express 6, 1376–1391 (2015).
[Crossref]

K. Yoshida, I. Nishidate, T. Ishizuka, S. Kawauchi, S. Sato, and M. Satoc, “Multispectral imaging of absorption and scattering properties of in vivo exposed rat brain using a digital red-green-blue camera,” J. Biomed. Opt. 20, 051026 (2015).
[Crossref]

S. C. Yoon, T. S. Shin, K. C. Lawrence, G. W. Heitschmidt, B. Park, and G. R. Gamble, “Hyperspectral imaging using RGB color for foodborne pathogen detection,” J. Electron. Imaging 24, 043008 (2015).
[Crossref]

2014 (3)

O. Kim, J. McMurdy, G. Jay, C. Lines, G. Crawford, and M. Alber, “Combined reflectance spectroscopy and stochastic modeling approach for noninvasive hemoglobin determination via palpebral conjunctiva,” Physiol. Rep. 2, e00192 (2014).
[Crossref]

A. Doblas, E. Sanchez-Ortiga, M. Martinez-Corral, G. Saavedra, and J. Garcia-Sucerquia, “Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy,” J. Biomed. Opt. 19, 46022 (2014).
[Crossref]

S. H. Kim, M. Lilot, L. S. L. Murphy, K. S. Sidhu, Z. X. Yu, J. Rinehart, and M. Cannesson, “Accuracy of continuous noninvasive hemoglobin monitoring: a systematic review and meta-analysis,” Anesth. Analg. 119, 332–346 (2014).
[Crossref]

2013 (3)

L. J. Moore, C. E. Wade, L. Vincent, J. Podbielski, E. Camp, D. del Junco, H. Radhakrishnan, J. McCarthy, B. Gill, and J. B. Holcomb, “Evaluation of noninvasive hemoglobin measurements in trauma patients,” Am. J. Surg. 206, 1041–1047 (2013).
[Crossref]

C. M. Thorson, M. L. Ryan, R. M. Van Haren, R. Pereira, J. Olloqui, C. A. Otero, C. I. Schulman, A. S. Livingstone, and K. G. Proctor, “Change in hematocrit during trauma assessment predicts bleeding even with ongoing fluid resuscitation,” Am. Surg. 79, 398–406 (2013).

I. Nishidate, T. Maeda, K. Niizeki, and Y. Aizu, “Estimation of melanin and hemoglobin using spectral reflectance images reconstructed from a digital RGB image by the Wiener estimation method,” Sensors 13, 7902–7915 (2013).
[Crossref]

2012 (3)

M. L. Ryan, C. M. Thorson, C. A. Otero, T. Vu, C. I. Schulman, A. S. Livingstone, and K. G. Proctor, “Initial hematocrit in trauma: a paradigm shift?” J. Trauma Acute Care Surg. 72, 54–60 (2012).
[Crossref]

C. Briggs, S. Kimber, and L. Green, “Where are we at with point-of-care testing in haematology?” Br. J. Haematol. 158, 679–690 (2012).
[Crossref]

S. Chen and Q. Liu, “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new colors for tissue measurements,” J. Biomed. Opt. 17, 030501 (2012).
[Crossref]

2011 (2)

A. Thomas, J. Newton, and M. Oldham, “A method to correct for stray light in telecentric optical-CT imaging of radiochromic dosimeters,” Phys. Med. Biol. 56, 4433–4451 (2011).
[Crossref]

A. Krishnan, L. J. Williams, A. R. McIntosh, and H. Abdi, “Partial least squares (PLS) methods for neuroimaging: a tutorial and review,” Neuroimage 56, 455–475 (2011).
[Crossref]

2010 (1)

A. Kalantri, M. Karambelkar, R. Joshi, S. Kalantri, and U. Jajoo, “Accuracy and reliability of pallor for detecting anaemia: a hospital-based diagnostic accuracy study,” PLOS One 5, e8545 (2010).
[Crossref]

2009 (1)

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2007 (3)

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2005 (1)

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2001 (1)

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2000 (1)

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1998 (1)

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1997 (1)

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1985 (1)

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A. Lima and J. Bakker, “Noninvasive monitoring of peripheral perfusion,” Intensive Care Med. 31, 1316–1326 (2005).
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T. Kim, S. H. Choi, N. Lambert-Cheatham, Z. Xu, J. E. Kritchevsky, F. R. Bertin, and Y. L. Kim, “Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology,” J. Biomed. Opt. 21, 107001 (2016).
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Choudhry, N. K.

T. N. Sheth, N. K. Choudhry, M. Bowes, and A. S. Detsky, “The relation of conjunctival pallor to the presence of anemia,” J. Gen. Intern. Med. 12, 102–106 (1997).
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Clifford, G. D.

R. G. Mannino, D. R. Myers, E. A. Tyburski, C. Caruso, J. Boudreaux, T. Leong, G. D. Clifford, and W. A. Lam, “Smartphone app for non-invasive detection of anemia using only patient-sourced photos,” Nat. Commun. 9, 4924 (2018).
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S. C. Mathews, M. J. McShea, C. L. Hanley, A. Ravitz, A. B. Labrique, and A. B. Cohen, “Digital health: a path to validation,” NPJ Digit. Med. 2, 38 (2019).
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S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, and R. Weinkove, “Non-invasive detection of anaemia using digital photographs of the conjunctiva,” PLOS One 11, e0153286 (2016).
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S. R. Pasricha, K. Colman, E. Centeno-Tablante, M. N. Garcia-Casal, and J. P. Pena-Rosas, “Revisiting WHO haemoglobin thresholds to define anaemia in clinical medicine and public health,” Lancet Haematol. 5, E60–E62 (2018).
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Crawford, G.

O. Kim, J. McMurdy, G. Jay, C. Lines, G. Crawford, and M. Alber, “Combined reflectance spectroscopy and stochastic modeling approach for noninvasive hemoglobin determination via palpebral conjunctiva,” Physiol. Rep. 2, e00192 (2014).
[Crossref]

J. McMurdy, G. Jay, S. Suner, and G. Crawford, “Photonics-based in vivo total hemoglobin monitoring and clinical relevance,” J. Biophoton. 2, 277–287 (2009).
[Crossref]

S. Suner, G. Crawford, J. McMurdy, and G. Jay, “Non-invasive determination of hemoglobin by digital photography of palpebral conjunctiva,” J. Emerg. Med. 33, 105–111 (2007).
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Darrodi, M. M.

Dauvin, A.

A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. M. Sauer, D. Ramazzotti, M. Bonvini, L. A. Celi, and M. J. Douglas, “Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients,” NPJ Digit. Med. 2, 116 (2019).
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T. R. Liu, Z. S. Wei, Y. Rivenson, K. de Haan, Y. B. Zhang, Y. C. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophoton. 12, e201900107 (2019).
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L. J. Moore, C. E. Wade, L. Vincent, J. Podbielski, E. Camp, D. del Junco, H. Radhakrishnan, J. McCarthy, B. Gill, and J. B. Holcomb, “Evaluation of noninvasive hemoglobin measurements in trauma patients,” Am. J. Surg. 206, 1041–1047 (2013).
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Derman, R. J.

S. E. Juul, R. J. Derman, and M. Auerbach, “Perinatal iron deficiency: implications for mothers and infants,” Neonatology 115, 269–274 (2019).
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Detsky, A. S.

T. N. Sheth, N. K. Choudhry, M. Bowes, and A. S. Detsky, “The relation of conjunctival pallor to the presence of anemia,” J. Gen. Intern. Med. 12, 102–106 (1997).
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P. J. Diggle, P. Heagerty, K.-Y. Liang, and S. L. Zeger, Analysis of Longitudinal Data (Oxford University, 2002).

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G. Dimauro, A. Guarini, D. Caivano, F. Girardi, C. Pasciolla, and A. Iacobazzi, “Detecting clinical signs of anaemia from digital images of the palpebral conjunctiva,” IEEE Access 7, 113488 (2019).
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Doblas, A.

A. Doblas, E. Sanchez-Ortiga, M. Martinez-Corral, G. Saavedra, and J. Garcia-Sucerquia, “Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy,” J. Biomed. Opt. 19, 46022 (2014).
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Donado, C.

A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. M. Sauer, D. Ramazzotti, M. Bonvini, L. A. Celi, and M. J. Douglas, “Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients,” NPJ Digit. Med. 2, 116 (2019).
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C. E. Shulman, M. Levene, L. Morison, E. Dorman, N. Peshu, and K. Marsh, “Screening for severe anaemia in pregnancy in Kenya, using pallor examination and self-reported morbidity,” Trans. R. Soc. Trop. Med. Hyg. 95, 250–255 (2001).
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A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. M. Sauer, D. Ramazzotti, M. Bonvini, L. A. Celi, and M. J. Douglas, “Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients,” NPJ Digit. Med. 2, 116 (2019).
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Duranteau, J.

S. Figueiredo, C. Taconet, A. Harrois, S. Hamada, T. Gauss, M. Raux, and J. Duranteau, “How useful are hemoglobin concentration and its variations to predict significant hemorrhage in the early phase of trauma? A multicentric cohort study,” Ann. Intensive Care 8, 76 (2018).
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A. R. Kent, S. H. Elsing, and R. L. Hebert, “Conjunctival vasculature in the assessment of anemia,” Ophthalmology 107, 274–277 (2000).
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S. Figueiredo, C. Taconet, A. Harrois, S. Hamada, T. Gauss, M. Raux, and J. Duranteau, “How useful are hemoglobin concentration and its variations to predict significant hemorrhage in the early phase of trauma? A multicentric cohort study,” Ann. Intensive Care 8, 76 (2018).
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E. Abraham, S. E. Fink, D. R. Markle, G. Pinholster, and M. Tsang, “Continuous monitoring of tissue pH with a fiberoptic conjunctival sensor,” Ann. Emerg. Med. 14, 840–844 (1985).
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Finlayson, G.

Fisher, D. E.

J. Y. Lin and D. E. Fisher, “Melanocyte biology and skin pigmentation,” Nature 445, 843–850 (2007).
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M. N. Hasan, A. Fraiwan, P. Thota, T. Oginni, G. M. Olanipekun, F. Hassan-Hanga, J. Little, S. K. Obaro, and U. A. Gurkan, “Clinical testing of hemechip in Nigeria for point-of-care screening of sickle cell disease,” Blood 132, 1095 (2018).
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S. Galliani, C. Lanaras, D. Marmanis, E. Baltsavias, and K. Schindler, “Learned spectral super-resolution,” arXiv:1703.09470 (2017).

Gamble, G. R.

S. C. Yoon, T. S. Shin, K. C. Lawrence, G. W. Heitschmidt, B. Park, and G. R. Gamble, “Hyperspectral imaging using RGB color for foodborne pathogen detection,” J. Electron. Imaging 24, 043008 (2015).
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Garcia-Casal, M. N.

S. R. Pasricha, K. Colman, E. Centeno-Tablante, M. N. Garcia-Casal, and J. P. Pena-Rosas, “Revisiting WHO haemoglobin thresholds to define anaemia in clinical medicine and public health,” Lancet Haematol. 5, E60–E62 (2018).
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Garcia-Sucerquia, J.

A. Doblas, E. Sanchez-Ortiga, M. Martinez-Corral, G. Saavedra, and J. Garcia-Sucerquia, “Accurate single-shot quantitative phase imaging of biological specimens with telecentric digital holographic microscopy,” J. Biomed. Opt. 19, 46022 (2014).
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Gauss, T.

S. Figueiredo, C. Taconet, A. Harrois, S. Hamada, T. Gauss, M. Raux, and J. Duranteau, “How useful are hemoglobin concentration and its variations to predict significant hemorrhage in the early phase of trauma? A multicentric cohort study,” Ann. Intensive Care 8, 76 (2018).
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Gelderblom, H. C.

M. Jaggernath, R. Naicker, S. Madurai, M. A. Brockman, T. Ndung’u, and H. C. Gelderblom, “Diagnostic accuracy of the HemoCue HB 301, STAT-Site MHgb and URIT-12 point-of-care hemoglobin meters in a central laboratory and a community based clinic in Durban, South Africa,” Plos One 11, e0152184 (2016).
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George, A.

S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, and R. Weinkove, “Non-invasive detection of anaemia using digital photographs of the conjunctiva,” PLOS One 11, e0153286 (2016).
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K. Gwn Lore, K. K. Reddy, M. Giering, and E. A. Bernal, “Generative adversarial networks for spectral super-resolution and bidirectional RGB-to-multispectral mapping,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019).

Gill, B.

L. J. Moore, C. E. Wade, L. Vincent, J. Podbielski, E. Camp, D. del Junco, H. Radhakrishnan, J. McCarthy, B. Gill, and J. B. Holcomb, “Evaluation of noninvasive hemoglobin measurements in trauma patients,” Am. J. Surg. 206, 1041–1047 (2013).
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Girardi, F.

G. Dimauro, A. Guarini, D. Caivano, F. Girardi, C. Pasciolla, and A. Iacobazzi, “Detecting clinical signs of anaemia from digital images of the palpebral conjunctiva,” IEEE Access 7, 113488 (2019).
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Goossens, L.

S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, and R. Weinkove, “Non-invasive detection of anaemia using digital photographs of the conjunctiva,” PLOS One 11, e0153286 (2016).
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C. Briggs, S. Kimber, and L. Green, “Where are we at with point-of-care testing in haematology?” Br. J. Haematol. 158, 679–690 (2012).
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G. Dimauro, A. Guarini, D. Caivano, F. Girardi, C. Pasciolla, and A. Iacobazzi, “Detecting clinical signs of anaemia from digital images of the palpebral conjunctiva,” IEEE Access 7, 113488 (2019).
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K. Gwn Lore, K. K. Reddy, M. Giering, and E. A. Bernal, “Generative adversarial networks for spectral super-resolution and bidirectional RGB-to-multispectral mapping,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019).

Hamada, S.

S. Figueiredo, C. Taconet, A. Harrois, S. Hamada, T. Gauss, M. Raux, and J. Duranteau, “How useful are hemoglobin concentration and its variations to predict significant hemorrhage in the early phase of trauma? A multicentric cohort study,” Ann. Intensive Care 8, 76 (2018).
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S. C. Mathews, M. J. McShea, C. L. Hanley, A. Ravitz, A. B. Labrique, and A. B. Cohen, “Digital health: a path to validation,” NPJ Digit. Med. 2, 38 (2019).
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Harrois, A.

S. Figueiredo, C. Taconet, A. Harrois, S. Hamada, T. Gauss, M. Raux, and J. Duranteau, “How useful are hemoglobin concentration and its variations to predict significant hemorrhage in the early phase of trauma? A multicentric cohort study,” Ann. Intensive Care 8, 76 (2018).
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Hasan, M. N.

M. N. Hasan, A. Fraiwan, P. Thota, T. Oginni, G. M. Olanipekun, F. Hassan-Hanga, J. Little, S. K. Obaro, and U. A. Gurkan, “Clinical testing of hemechip in Nigeria for point-of-care screening of sickle cell disease,” Blood 132, 1095 (2018).
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Hassan-Hanga, F.

M. N. Hasan, A. Fraiwan, P. Thota, T. Oginni, G. M. Olanipekun, F. Hassan-Hanga, J. Little, S. K. Obaro, and U. A. Gurkan, “Clinical testing of hemechip in Nigeria for point-of-care screening of sickle cell disease,” Blood 132, 1095 (2018).
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Heagerty, P.

P. J. Diggle, P. Heagerty, K.-Y. Liang, and S. L. Zeger, Analysis of Longitudinal Data (Oxford University, 2002).

Hebert, R. L.

A. R. Kent, S. H. Elsing, and R. L. Hebert, “Conjunctival vasculature in the assessment of anemia,” Ophthalmology 107, 274–277 (2000).
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Heitschmidt, G. W.

S. C. Yoon, T. S. Shin, K. C. Lawrence, G. W. Heitschmidt, B. Park, and G. R. Gamble, “Hyperspectral imaging using RGB color for foodborne pathogen detection,” J. Electron. Imaging 24, 043008 (2015).
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Hirst, E.

S. Collings, O. Thompson, E. Hirst, L. Goossens, A. George, and R. Weinkove, “Non-invasive detection of anaemia using digital photographs of the conjunctiva,” PLOS One 11, e0153286 (2016).
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Am. J. Surg. (1)

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Supplementary Material (1)

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» Supplement 1       Supplementary Figures, Supplementary Tables, Supplementary Methods

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Figures (5)

Fig. 1.
Fig. 1. Spectral super-resolution (SSR) spectroscopy for mobile health (mHealth) hemoglobin (Hgb) analyses. (a) The inner eyelid (i.e., palpebral conjunctiva) is used as an accessible sensing site for noninvasive blood Hgb quantification. An RGB image of the inner eyelid is conveniently captured using the built-in camera of a smartphone. The subject simply pulls down the eyelid to expose the conjunctiva, and a healthcare professional takes a photograph of the eyelid. The mobile application collects red (R), green (G), and blue (B) color information from the eyelid image and applies SSR to mathematically reconstruct spectra in the visible wavelength range. The spectral intensity reflected from the inner eyelid is sensitive to changes in Hgb content in the blood. The reconstructed spectrum of the acquired eyelid image is then processed to accurately and precisely predict the amount of total blood Hgb content. The result displays the blood Hgb count in units of ${\rm g}\;{{\rm dL}^{- 1}}$ in the same manner of clinical laboratory Hgb tests. (b) Statistical learning in the mHematology for SSR blood Hgb computation developed using separate training and validation datasets. The first step is to apply SSR to the eyelid portion of the RGB image. The second step is to compute blood Hgb content in ${\rm g}\;{{\rm dL}^{- 1}}$ using the spectroscopic model of blood Hgb, which is also validated by the clinical laboratory blood Hgb tests (i.e., the gold standard).
Fig. 2.
Fig. 2. High-quality spectra acquired by the image-guided hyperspectral line-scanning system and the mHematology mobile application. (a) Photograph of the image-guided hyperspectral line-scanning system for imaging the exact portion of the inner eyelid. The participant sits in front of the system, facing the telecentric lens, places the chin on the chinrest, and pulls down the eyelid for imaging when instructed. (b) Location where the hyperspectral line-scanning is performed (translucent white rectangle). The hyperspectral line-scan dataset contains spatial ($y$) and wavelength ($\lambda$) information. The averaged spectrum corresponds to the average intensity along the spatial $y$ axis for each $\lambda$ value. The characteristic absorption spectrum of blood Hgb is clearly visible. (c) mHematology mobile application developed for data acquisition in a low-end Android smartphone (Samsung Galaxy J3). On the main application screen, it displays a circle and arc to serve as guidance for locating the eyeball and the inner eyelid at consistent distance and position within the image. To remove the background room light, the application automatically acquires two RGB photographs by controlling the built-in flashlight (i.e., white-light LED) to turn on and off. To compensate for the system response, two RGB images of a reflectance standard are taken (left). Similarly, the application automatically takes two RGB images with flash on and flash off for the individual’s exposed eyelid (right). (d) Spectral profiles of the white-light LED illumination sources in the image-guided hyperspectral line-scanning system and Samsung Galaxy J3. The data acquisition procedure incorporates reference measurements of white reflectance standards (99% reflectivity in the visible range) to compensate for the spectral responses of the light source and the camera in the system.
Fig. 3.
Fig. 3. Performance of spectroscopic blood Hgb measurements of the left and right inner eyelids. (a) High correlations between the computed and clinical laboratory blood Hgb levels in both of the training ($n = {138}$ plotted in blue) and validation ($n = {15}$ plotted in red) datasets. (b) Bland–Altman analysis of comparing the computed blood Hgb levels with the clinical laboratory results, showing narrow 95% limits of agreement (LOA) of [${-}{1.56},\;{1.58}\;{\rm g}\;{{\rm dL}^{- 1}}$] with bias of ${0.01}\;{\rm g}\;{{\rm dL}^{- 1}}$ in the validation dataset. (c) ${R^2}$ values between the computed blood Hgb levels and the clinical laboratory results of validation datasets from 10 different combinations of training and validation datasets. (d) LOA (error bar) and bias (square point) values of validation datasets from the different training–validation combinations. (e) and (f) Systematic comparisons of the spectra measured from the left (green) and right (magenta) inner eyelids among a subset of 36 participants. (e) The average spectral differences between the left and right eyelid spectra (Fig. S8) are statistically insignificant, and the Pearson correlation coefficients are close to 1 in all of the participants. (f) High correlations of the left and right spectroscopic blood Hgb measurements, compared with the clinical laboratory blood Hgb test results.
Fig. 4.
Fig. 4. Comparisons between the original spectra and the SSR-reconstructed spectra. (a) and (b) Inner eyelids’ spectral intensity (acquired by the image-guided hyperspectral line-scanning system and the mHematology application) plotted with the clinical laboratory blood Hgb values (vertical axis) from the training dataset ($n = {138}$). (c) Differences between the original and SSR-reconstructed spectra plotted with 95% confidence intervals at each wavelength. The transformation matrix that converts RGB data to spectral data is optimized by minimizing the differences in the training dataset. (d) Average spectral differences and Pearson correlation coefficients between the original and reconstructed spectra in the training dataset as a function of blood Hgb levels corresponding to each spectrum. (e) and (f) Inner eyelids’ spectral intensity (acquired by the image-guided hyperspectral line-scanning system and the mHematology application) visualized with the clinical laboratory blood Hgb values (vertical axis) from the validation dataset ($n = {15}$). (g) Differences between the original and SSR-reconstructed spectra plotted with 95% confidence intervals at each wavelength are still small, supporting the high fidelity of SSR. The differences in the wavelength range between 450 and 575 nm are generally higher, because distinct Hgb absorption is present in this range [Fig. S5(d)]. (h) Average spectral differences and Pearson correlation coefficients between the original and reconstructed spectra in the validation dataset as a function of blood Hgb levels.
Fig. 5.
Fig. 5. Performance of SSR blood Hgb measurements with the mHematology mobile application. (a) High correlations between the SSR-computed and clinical laboratory blood Hgb levels in both training ($n = {138}$ plotted in blue) and validation ($n = {15}$ plotted in red) datasets. (b) Bland–Altman analyses of comparing the computed blood Hgb measurements with the clinical laboratory results, showing narrow 95% limits of agreement (LOA) of [${-}{2.20},\;{2.29}\;{\rm g}\;{{\rm dL}^{- 1}}$] and bias of ${0.04}\;{\rm g}\;{{\rm dL}^{- 1}}$ in the validation dataset. In particular, the bias is not associated with actual blood Hgb levels in the validation dataset (Table S3 in Supplement 1). (c) ${R^2}$ values between the computed blood Hgb levels and the clinical laboratory results of validation datasets from 10 different combinations of training and validation datasets. (d) LOA (error bar) and bias (square point) values of validation datasets from the different training–validation combinations. The mHematology application reliably predicts the actual blood Hgb levels without any hardware attachments to the smartphone.

Tables (4)

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Table 1. Participant Characteristics

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Table 2. Spectral Differences between the Left and Right Inner Eyelids

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Table 3. Intra-Class Correlations (ICC) between the Left and Right Inner Eyelidsa

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Table 4. ICC between Spectroscopic and SSR Blood Hgb Measurementsa

Equations (11)

Equations on this page are rendered with MathJax. Learn more.

x 3 × 1 = S 3 × N y N × 1 + e 3 × 1 ,
X 3 × m = S 3 × N Y N × m .
[ R 1 R 2 R m G 1 G 2 G m B 1 B 2 B m ] = [ S 1 ( λ 1 ) S 1 ( λ 2 ) S 1 ( λ N ) S 2 ( λ 1 ) S 2 ( λ 2 ) S 2 ( λ N ) S 3 ( λ 1 ) S 3 ( λ 2 ) S 3 ( λ N ) ] × [ I 1 ( λ 1 ) I 2 ( λ 1 ) I m ( λ 1 ) I 1 ( λ 2 ) I 2 ( λ 2 ) I m ( λ 2 ) I 1 ( λ N ) I 2 ( λ N ) I m ( λ N ) ] ,
Y N × m = T N × 3 X 3 × m ,
Y N × m = T ^ N × p X ^ p × m ,
X ^ p × m = [ R 1 G 1 B 1 R 1 i G 1 j B 1 k R m G m B m R m i G m j B m k ] T ,
I m ( λ o r R G B ) = L ( λ o r R G B ) C ( λ o r R G B ) D ( λ o r R G B ) y ( λ o r R G B ) ,
y ( λ o r R G B ) = I m ( λ o r R G B ) I r e f e r e n c e ( λ o r R G B ) .
y ( λ o r R G B ) = I m ( λ o r R G B ) I b a c k g r o u n d ( λ o r R G B ) I r e f e r e n c e ( λ o r R G B ) I b a c k g r o u n d ( λ o r R G B ) .
B i a s = d ¯ = 1 n k = 1 n d k .
L O A = d ¯ ± 1.96 1 n 1 k = 1 n ( d k d ¯ ) 2 .

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