Machine learning improves the accuracy of point-of-care disease detection

What if people could detect cancer and other diseases with the same speed and ease of pregnancy tests or glucose meters? Researchers at the Carl R. Woese Institute for Genomic Biology believe this is near a step towards achieving this goal by integrating machine learning-based analytics into point-of-care biosensing technology.

A new method called Loca-Pram was reported in the journal Biosensors and Bioelectronics Improves biomarker detection accessibility by eliminating the need for technical experts to perform image analysis.

Traditional medical diagnostic techniques require a physician to send a patient's blood or tissue sample to a clinical laboratory. There, expert scientists will carry out testing procedures and data analysis.

Current technology requires patients to visit hospitals and obtain a long-lasting diagnosis. Many people have barriers where more appointments may not be financially or spatially feasible. We think we can make a difference by developing more point-of-care technologies that people can use. ”

Han Lee, first author of the study and graduate student of the Nanosensor Research Group

Point-of-care diagnosis is performed and results are achieved at the patient care site, at home, at the clinic or anywhere in between. This allows for low-cost, easy-to-use, and quick testing that will help you inform your next step. Some examples already employed in daily life include urine pregnancy tests, Covid-19 antigen test kits, and glucose meters that allow diabetics to respond to lower levels of blood glucose and spikes throughout the day.

In the field of point-of-care, researchers are investigating new ways to integrate these types of techniques into patient care settings, including appointments with professionals such as oncologists and oral surgeons. This helps reduce patient time and financial burdens while improving physicians' real-time decision-making.

“Doctors say they want something similar when you get in with a bacterial infection. They will test you right away and send you home from an appropriate antibiotic appointment to treat the specific bacteria you have,” said Brian Cunningham, a professor of electrical and computer engineering, CGD leader. “So why not try something similar to choose the right anti-cancer drug or to determine if the medication you've been taking for a few weeks is starting to work?”

Previously, the group reported photonic resonator absorption microscopy (or a novel biosensing method known as a pram) to detect molecular biomarker molecules in the body whose presence and levels indicate healthy or disease state. PRAM allows for the detection of single biomarker molecules, including nucleic acids, antigens and antibodies. Instead, common biosensing techniques detect cumulative signals of hundreds to thousands of molecules.

Cunningham said, “Essentially, what we do is make a red LED light shine at the bottom of the sensor. Then, at the top of the sensor, it's detected whenever a molecule lands and there are small particles made of gold.

Images generated using Pram depict a red background with small black spots scattered around them. However, these images themselves look relatively simple, but to get accurate counts you need a trained eye that can decipher the spots that really correspond to the AUNP-tagged biomarker molecule.

“There are many types of artifacts, such as dust particles and nanoparticle aggregates. Without a lot of experience, it's difficult to distinguish between them,” Lee said. “The traditional counting algorithm we use requires a lot of tweaking parameters to remove these artifacts.”

We proposed moving this process out of the laboratory and integrating machine learning into the image analysis process as it is suitable for a point-of-care environment.

“Han really was himself, and after taking classes in college, he became interested in machine learning to learn about it,” Cunningham said. “He said he thought he could come to me one day and create a machine learning algorithm.

Compared to other biosensing techniques, Pram is suitable for incorporating deep learning algorithms as it generates microscopic images rather than detecting optical signals. However, these algorithms are as good as the data they trained it, so Lee decided to image the same sample using both PRAM and a scan electron microscope.

The AuNP is 1000 times smaller than human hair, and is only shown as small black spots in the pram image, allowing for more clearly visualization by electron microscopes. In a time-like process, Lee Cross used electron microscope images to reference all the spots in the Pram image to obtain very accurate data from the machine learning training set.

“It was very difficult to find a suitable place to compare because it's like finding needles in the desert. One way I came up with was to create a reference point like a sea lighthouse, where you can find the exact same place as the registration,” Lee said.

The resulting deep learning-based method known as localization with PRAM integrated context recognition allows for high-precision detection of molecular biomarkers in real time without the need for technical expert eye and experience. On testing, the team found that Loca-Pram surpasses traditional technology with accuracy, detecting lower levels of biomarkers and minimizing false positives and negative rates.

“My PhD was started because I wanted to change the Point of Care Field,” Lee said. “I just want to do everything on my own to develop more advanced technologies that could have an impact in the future.”

The publication, “Photonic Resonator Absorption Microscopy for Localization of Physically Grounded Deep Learning-enabled Gold Nanoparticles and Digital Resolution Resolution Molecular Diagnosis,” can be found at https://doi.org/10.1016/j.bios.bios.bios.bios.2025.117455 and was supported by the National Science Aid Association.