Feasibility of smartphone colorimetry of the face as an anaemia screening tool for infants and young children in Ghana

Abstract

Background Anaemia affects approximately a quarter of the global population. When anaemia occurs during childhood, it can increase susceptibility to infectious diseases and impair cognitive development. This research uses smartphone-based colorimetry to develop a non-invasive technique for screening for anaemia in a previously understudied population of infants and young children in Ghana. Methods We propose a colorimetric algorithm for screening for anaemia which uses a novel combination of three regions of interest: the lower eyelid (palpebral conjunctiva), the sclera, and the mucosal membrane adjacent to the lower lip. These regions are chosen to have minimal skin pigmentation occluding the blood chromaticity. As part of the algorithm development, different methods were compared for (1) accounting for varying ambient lighting, and (2) choosing a chromaticity metric for each region of interest. In comparison to some prior work, no specialist hardware (such as a colour reference card) is required for image acquisition. Results Sixty-two patients under 4 years of age were recruited as a convenience clinical sample in Korle Bu Teaching Hospital, Ghana. Forty-three of these had quality images for all regions of interest. Using a naïve Bayes classifier, this method was capable of screening for anaemia (<11.0g/dL haemoglobin concentration) vs healthy blood haemoglobin concentration (≥11.0g/dL) with a sensitivity of 92.9% (95% CI 66.1% to 99.8%), a specificity of 89.7% (72.7% to 97.8%) when acting on unseen data, using only an affordable smartphone and no additional hardware. Conclusion These results add to the body of evidence suggesting that smartphone colorimetry is likely to be a useful tool for making anaemia screening more widely available. However, there remains no consensus on the optimal method for image preprocessing or feature extraction, especially across diverse patient populations.

Assessment of bilirubin levels in patients with cirrhosis via forehead, sclera and lower eyelid smartphone images

“…Rather than generating a model for bilirubin based on just one imaging site, a multivariate approach is possible [ 15 ]. Fig 8 presents results generated by combining data from the sclera and lower eyelid.…”

Prediction of anemia in real-time using a smartphone camera processing conjunctival images

“…Our algorithm utilizes pixel extraction instead of analysis of image RGB values. In previous methods that used RGB value analysis, which a majority of other Hb prediction apps use [ 7 – 10 , 13 – 15 , 26 – 28 ], each pixel has a red, green, and blue value that contributes to the color of the image and is limited to one of 256 values [ 28 ]. Using techniques described by Sumner [ 29 ], a RAW image processing algorithm that maximized the color resolution of images was developed, wherein each color level was encoded with 32 bits giving 4.3 trillion values for each channel and 8x1028 colors allowing for highly accurate color analysis.…”

“…However when applied non-invasively to fingernail surfaces in Bihar, India to predict Hb values in a population of school-aged children the app proved to be highly inaccurate without algorithm training [ 12 ]. The benefit of basing Hb prediction on images of the conjunctiva [ 26 ] compared to other vascularized surfaces such as fingernails is that vascular beds are not affected by factors including poor perfusion from vasoconstriction and shock, fungal infections, discoloration from trauma, fingernail polish and staining, or artificial fingernails [ 16 ].…”

Use of point‐of‐care haemoglobin tests to diagnose childhood anaemia in low‐ and middle‐income countries: A systematic review

“…The novel colour-based assay offers a significant cost advantage at 0.26 USD per test, however further evidence on its use is required. Other affordable technologies, such as smartphone-based colorimetry are at early stages of development for identifying severe anaemia in LMICs [87].…”

“…Other affordable technologies, such as smartphone-based colorimetry are at early stages of development for identifying severe anaemia in LMICs. ( 87 )…”