Smartphone algorithm shows promise for point-of-care disease diagnosis

The smartphone imaging algorithm enables analysis of assays typically evaluated via spectroscopy.

Feb 12th, 2019
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Researchers from Florida Atlantic University's (FAU; Boca Raton, FL) College of Engineering and Computer Science have developed a novel smartphone imaging algorithm that enables analysis of assays typically evaluated via spectroscopy. Analyzing more than 10,000 images, the researchers demonstrated that the saturation method they developed consistently outperformed existing algorithms under a wide range of operating field conditions. Their findings are a step forward in developing point-of-care diagnostics by reducing the need for required equipment, improving the limit of detection, and increasing the precision of quantitative results.

"Smartphone cameras are optimized for image appearance rather than for quantitative image-based measurements, and they can't be bypassed or reversed easily. Furthermore, most lab-based biological and biochemical assays still lack a robust and repeatable cell phone analogue," says Waseem Asghar, Ph.D., lead author and an assistant professor in FAU's Department of Computer and Electrical Engineering and Computer Science. "We have been able to develop a cell phone-based image preprocessing method that produces a mean pixel intensity with smaller variances, lower limits-of-detection, and a higher dynamic range than existing methods."

For the study, Asghar and co-authors Benjamin Coleman and Chad Coarsey, graduate students in the Asghar Laboratory in FAU's College of Engineering and Computer Science, performed image capture using three smartphones: the Android Moto G with a 5 Mpixel camera, the iPhone 6 with a 12 Mpixel camera, and the Samsung Galaxy Edge 7 with a 12 Mpixel camera.

They tested for image capture at various conditions, measured algorithm performance, tested sensitivity to camera distance, tilt and motion, and examined histogram properties and concentration response. They also examined limit of detection as well as properties of saturation, ambient lighting levels, and relationship with red-green-blue (RGB) color space. Smartphone images are natively stored as arrays of RGB pixel intensities, commonly referred to as color channels.

Images of a diagnostic assay are captured using a smartphone camera. Regions of interest are extracted and are converted to HSV (hue, saturation, value) space. After the conversion process, the standard pixel intensity analysis is applied to the saturation channel and the values are used to determine absorbance and concentration of the sample automatically. (Image credit: Florida Atlantic University)

Using several thousand images, the researchers compared saturation analysis with existing RGB methods and found that it both analytically and empirically improved performance in the presence of additive and multiplicative ambient light noise. They also showed that saturation analysis can be interpreted as an optimized version of existing RGB ratio tests. They verified that the ideal image capture conditions include constant white light, a clean white background, minimal distance to the sample, and zero angular displacement of the camera.

The researchers also applied the test to an enzyme-linked immunosorbent assay (ELISA), a plate-based assay technique designed for detecting and quantifying substances such as peptides, proteins, antibodies, and hormones. They discovered that for human immunodeficiency virus (HIV), saturation analysis enabled an equipment-free evaluation and limit of detection was significantly lower than what is currently available with RGB methods.

The FAU-developed methodology represents an improvement in repeatability, practicality, and image capture noise rejection. In addition, saturation analysis is not affected by many of the major limiting factors for image-based tests, such as ambient lighting variations, shading, and variable light levels. The researchers anticipate that the favorable properties of saturation analysis will encounter and enable smartphone image-based point-of-care tests with less equipment overhead and lower limits of detection.

Full details of the work appear in the journal Analyst.

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