Selfies to self-diagnosis: Algorithm 'amps up' smartphones to diagnose disease

ScienceDaily | 2/12/2019 | Staff
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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. Addressing these limitations requires external smartphone hardware to obtain quantitative results -- imposing a design tradeoff between accessibility and accuracy.

Researchers from Florida Atlantic University's College of Engineering and Computer Science have developed a novel cell phone imaging algorithm that enables analysis of assays typically evaluated via spectroscopy, a highly sophisticated and powerful device used in scientific research.

Analysis - Images - Researchers - Saturation - Method

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. Their findings, published in the journal Analyst of the Royal Society of Chemistry, 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 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," said 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."

Study - Asghar - Co-authors - Benjamin - Coleman

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 megapixel (MP) camera; the iPhone 6 with a 12 MP camera, and the Samsung Galaxy...
(Excerpt) Read more at: ScienceDaily
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