Smartphone app for non-invasive detection of anemia using only patient-sourced photos - PubMed Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Dec 4;9(1):4924.
doi: 10.1038/s41467-018-07262-2.

Smartphone app for non-invasive detection of anemia using only patient-sourced photos

Robert G Mannino  1   2   3 , David R Myers  1   2   3 , Erika A Tyburski  1   2   3 , Christina Caruso  2 , Jeanne Boudreaux  2 , Traci Leong  4 , G D Clifford  1   5 , Wilbur A Lam  6   7   8
Affiliations

Smartphone app for non-invasive detection of anemia using only patient-sourced photos

Robert G Mannino et al. Nat Commun. .

Abstract

We introduce a paradigm of completely non-invasive, on-demand diagnostics that may replace common blood-based laboratory tests using only a smartphone app and photos. We initially targeted anemia, a blood condition characterized by low blood hemoglobin levels that afflicts >2 billion people. Our app estimates hemoglobin levels by analyzing color and metadata of fingernail bed smartphone photos and detects anemia (hemoglobin levels <12.5 g dL-1) with an accuracy of ±2.4 g dL-1 and a sensitivity of 97% (95% CI, 89-100%) when compared with CBC hemoglobin levels (n = 100 subjects), indicating its viability to serve as a non-invasive anemia screening tool. Moreover, with personalized calibration, this system achieves an accuracy of ±0.92 g dL-1 of CBC hemoglobin levels (n = 16), empowering chronic anemia patients to serially monitor their hemoglobin levels instantaneously and remotely. Our on-demand system enables anyone with a smartphone to download an app and immediately detect anemia anywhere and anytime.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Implementation of a smartphone app for measuring hemoglobin (Hgb). a A patient simply downloads the app onto their smartphone, opens the app, obtains a smartphone photo of his/her fingernail beds, and without the need for any blood sampling or additional smartphone attachments or external calibration tools, quantitatively measures blood Hgb levels. The patient first takes an image of their fingernails, and is then prompted by the app to tap on the screen to select the regions of interest corresponding to the nailbeds, and a result is then displayed on the smartphone screen. Images are screenshots and photos of actual operation of this app. b As smartphone images with fingernail irregularities such as camera flash reflections or leukonychia may affect Hgb level measurements, a quality control algorithm integrated within the Hgb level measurement app detects and omits those irregularities to preserve measurement integrity and accuracy. To that end, the user selects regions of interest from within the fingernail image, and any color values that fall outside of expected color ranges are excluded from Hgb measurement. In this example, when the quality control system was implemented to exclude the fingernail bed irregularities, Hgb level was measured to be 14.7 g dL−1, comparable to the patient’s CBC Hgb level of 15.3 g dL−1. Without the quality control algorithm, Hgb level was measured at 12.8 g dL−1, indicating that the algorithm resulted in a 76% reduction in error. Note that as the smartphone image-based algorithm is device-agnostic, the analysis of the smartphone images, and therefore the Hgb level measurements, could also be transmitted to another device (e.g., laptop, cloud-based server) for remote rather than on-board analysis
Fig. 2
Fig. 2
The smartphone-based image analysis algorithm accurately measures Hgb levels. a The smartphone image analysis algorithm measures blood Hgb levels to within ±0.97 g dL−1 of the CBC Hgb level (r = 0.82, mean |error|). The solid line represents the ideal result where smartphone Hgb level is equal to the CBC Hgb level whereas the dashed line represents the actual data fit. Inset images illustrate example patient-sourced photos that were used to calculate Hgb level measurements. b The receiver-operating characteristic (ROC) analysis graphically illustrates the algorithm’s diagnostic performance against a random chance diagnosis (red line), with an area under the curve of 0.5, and a perfect diagnostic (green lines), with an area under the curve of 1. In the case of this noninvasive smartphone app Hgb measurement system (black line), the area under the curve of 0.88 suggests viable diagnostic performance of this algorithm. When using the WHO Hgb level cutoff of < 12.5 g dL−1, the sensitivity of the test is 97% (95% CI, 89–100%), n = 100 patients. c Bland–Altman analysis reveals minimal experimental bias with 0.2 g dL−1 average error, indicating that Hgb measurement is has a very small bias. The dashed line represents the relationship between the residual and the average of Hgb level measurements obtained from the CBC and the algorithm (r = 0.26). The solid red lines represent 95% limits of agreement (±2.4 g dL−1). n = 100
Fig. 3
Fig. 3
Diagnosis profile of our hemoglobin measurement patient population. Subjects with hemolytic anemia, healthy controls, cancer, other anemia (e.g., aplastic anemia), as well as various other blood disorders (e.g., such as thrombocytopenia, deep vein thrombosis, and hemophilia) participated in the study. These data represent the diagnosis profiles of the subjects shown in Fig. 2. n = 100
Fig. 4
Fig. 4
Personalized calibration further improves the accuracy of Hgb levels measurement. a Healthy and chronically transfused anemic patients were monitored over four weeks (i.e., over the course of a therapeutic blood transfusion cycle). CBC Hgb levels (white text) were used in conjunction with the images to generate a personalized algorithm for each individual. b The patient-specific algorithms were used to measure Hgb levels over a subsequent blood transfusion cycle. This patient-specific calibration improved the average error of Hgb level measurements to within 0.41 g dL−1 of the CBC Hgb level. Bland–Altman analysis shows negligible experimental bias in the data. A random effects model is used to statistically confirm consistency of average Hgb level measurement error between individual subjects. The average error (solid black line) indicates the Hgb measurement of the smartphone app is negligibly biased. The dashed line represents the correlation (r = −0.24) between the residual error and the average of Hgb level measurements obtained from the CBC and the algorithm. The solid red lines represent 95% limits of agreement (0.92 g dL−1). n = 4 patients, 4 measurements per patient
Fig. 5
Fig. 5
Background lighting and subject skin tone has minimal effect on app accuracy. Plotting measurement error against. a skin tone and b background lighting reveals low and negligible correlation (r = 0.13 and r = 0.00, respectively) in either case. Dashed lines indicate linear fit between the measurement error and the tested parameter (skin tone and background lighting, respectively). Inset images highlight a representative range of measured background skin tones and lighting conditions. n = 100 patients
Fig. 6
Fig. 6
App outperforms hematologists in physical exam-based hemaglobin measurement. Hematologists were able to estimate Hgb levels to within ±4.6 g dL−1 (a) (95% limits of agreement) with an ROC of 0.63 (c). The app outperforms the hematologists in both respects with Hgb level accuracy measurement to within ±1.0 g dL−1 (b) (95% limits of agreement) and an ROC of 0.94 (d). n = 50 subjects. Plots (a, c) represent the pooled results of 5 board-certified hematologists estimating blood hemoglobin levels based on images of patients fingernails
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Dorsey E, Topol E. State of telehealth. New Engl. J. Med. 2016;375:154–161. doi: 10.1056/NEJMra1601705. - DOI - PubMed
    1. Wolf J, et al. Diagnostic inaccuracy of smartphone applications for melanoma detection. JAMA Dermatol. 2013;149:422–426. doi: 10.1001/jamadermatol.2013.2382. - DOI - PMC - PubMed
    1. Rappaport Kathryn M., McCracken Courtney C., Beniflah Jacob, Little Wendalyn K., Fletcher Daniel A., Lam Wilbur A., Shane Andi L. Assessment of a Smartphone Otoscope Device for the Diagnosis and Management of Otitis Media. Clinical Pediatrics. 2016;55(9):800–810. doi: 10.1177/0009922815593909. - DOI - PubMed
    1. Maamari RN, Ausayakhun S, Margolis TP, Fletcher DA, Keenan JD. Novel telemedicine device for diagnosis of corneal abrasions and ulcers in resource-poor settings. JAMA Ophthalmol. 2014;132:894–895. doi: 10.1001/jamaophthalmol.2014.335. - DOI - PMC - PubMed
    1. Breslauer D, Maamari R, Switz N, Lam W, Fletcher D. Mobile phone based clinical microscopy for global health applications. PLoS One. 2009;4:e6320. doi: 10.1371/journal.pone.0006320. - DOI - PMC - PubMed

Publication types

MeSH terms