Inside the Box

Photo by Alexandre Debiève on Unsplash

Gitanjali Rao’s Epione

Gitanjali Rao, an award-winning inventor, was 13 years old when a family friend became addicted to her prescription opioid drugs. She learned that over 20 million Americans over the age of 12 suffer from some form of addiction to prescription drugs. Also, according to the National Institute of Drug Abuse, over 130 people in the United States die every day after overdosing on opioids. To help her friend and others like her, Gitanjali resolved to invent a better solution for detecting opioid addiction. A previous blog post titled “Fighting Opioid Addiction” described how she created Epione, her device. This post examines the design of her device.

Epione diagnoses opioid addiction using a novel application of colorimetry. Colorimetry is a widely used technique in medical diagnosis to determine the amount of a biochemical compound in the body. Someone addicted to opioids produces more of a certain protein in their body. The amount of this protein is proportional to the level of addiction. By determining the amount of this protein present in a patient, the doctor will know the level of opioid addiction in this patient.

Epione consists of four major parts: a miniature computer, a chamber for the sample, a camera, and a Bluetooth link to a smartphone. Rao uses a Raspberry Pi, a palm-size computer, to control the functions of the device. She loaded the Raspberry Pi with image processing algorithms for sample identification. The chamber which houses the sample during testing is opaque so that outside light does not affect the test results. She’s coated the inside of the chamber with a highly reflective white coating also to enhance the visibility of the sample. An LED lights the inside of the chamber for optimum visibility of the sample with consistent illumination. To capture the images of the sample, Rao affixed a digital camera focused on the sample to the wall of the chamber. Finally, she has created a Bluetooth link to a smartphone app that triggers the testing and displays the results.

One of the major advantages of Epione is that doctors can use it right in their own offices. First, they prepare a fluid sample from the patient in the normal way for a colorimetry scan. Then they place the sample into the chamber. Using the smartphone app, they trigger the camera to take high-resolution images of the sample. The app saves these images and displays them for the doctors to examine. Then the doctors can choose to send the images to a lab for technicians to examine with their testing equipment. Alternatively, the doctors can choose to analyze the sample image with Epione.

If a doctor analyzes the sample with Epione, then the Raspberry Pi uses its image processing algorithms, the embedded artificial intelligence, for pattern recognition. The Raspberry Pi compares the image of the sample to an image of a normal protein concentration.

Epione uses an extensive database of cataloged images of fluid samples for this comparison. The images in this database represent a spectrum of protein concentration from low to high. When the protein associated with opioid addiction is present in high concentration, the fluid sample has a distinctive color signature. Epione finds the database image with the highest similarity and notes the corresponding level of opioid addiction. Epione then sends this information back to the smartphone.

The app creates an easy-to-read display, placing the addiction level of the patient along a range of values from none to high. At a glance, doctors and their patients can see their level of addiction on this scale, along with suggested actions.

Gitanjali worked on her device with the mentoring of Michael McMurray, a professor at the University of Colorado medical school in Denver. As McMurry says, Gitanjali has duplicated the function of expensive complicated equipment in a portable, inexpensive device. There is still much work to be done before Epione is available to doctors and patients. One of the big tasks remaining is calibrating her device with the very expensive, state-of-the-art colorimetry lab equipment. There is potential for Gitanjali’s device to find application to any condition requiring colorimetry testing. Her mentor McMurray described Gitanjali’s work and her invention as “mind blowing.”

Read more about Gitanjali Rao and her inventions in my new book Teen Innovators: Nine Young People Engineering a Better World with Creative Inventions.

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