New Algorithm to Help Smartphones Diagnose Diseases Developed
Scientists have developed a new imaging algorithm that enables smartphones to diagnose diseases.
Scientists have developed a new imaging algorithm that enables smartphones to diagnose diseases by analysing assays typically evaluated via spectroscopy, a highly sophisticated and powerful device used in medical research.
Smartphones have emerged as powerful evaluation tools capable of diagnosing medical conditions in point-of-care settings, said researchers from the Florida Atlantic University (FAU) in the US.
They also are a viable solution for health care in the developing world because the devices allow untrained users to collect and transmit data to medical professionals.
Although smartphone camera technology today offers a wide range of medical applications such as microscopy and cytometric analysis, in practice, cell phone image tests have limitations that severely restrict their utility.
Through the analysis of more than 10,000 images, the researchers have been able to demonstrate that the saturation method they developed consistently outperformed existing algorithms under a wide range of operating field conditions.
The findings, published in the journal ‘Analyst’, is 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 optimised for image appearance rather than for quantitative image-based measurements, and they can not be bypassed or reversed easily. Furthermore, most lab-based biological and biochemical assays still lack a robust and repeatable cell phone analogue," said Waseem Asghar, an assistant professor at FAU.
"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," he said in a statement.
Asghar and colleagues performed image capture using three smartphones.
The researchers 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) colour space.
Cell phone images are natively stored as arrays of RGB pixel intensities, commonly referred to as colour channels.
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 optimised version of existing RGB ratio tests.
The researchers also applied the test to an ELISA (enzyme-linked immunosorbent assay), a plate-based assay technique designed for detecting and quantifying substances such as peptides, proteins, antibodies and hormones.
They discovered that for HIV, saturation analysis enabled an equipment-free evaluation and a limit-of-detection was significantly lower than what is currently available with RGB methods.
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