Collaborating Authors

Electron Microscopes Can Finally See in Wonderful Color


Imagine a Where's Waldo book with nothing but black and white pictures. Now you know what it's like trying to find a virus on a greyscale microscopic image. Microbiologists have dealt with this problem for decades, because when things get small, things go dark. Photons, bits of light essential to discerning color, are too clunky to resolve anything much smaller than say, a synapse connecting two neurons. If you want to look at things like viruses, bacteria, or molecules passing through cell walls, you must use an electron microscope.

Artificial Intelligence and the Pathologist: Future Frenemies?


The manuscript titled "AlphaGo, deep learning, and the future of the human microscopist" in this month's issue of the Archives of Pathology & Laboratory Medicine1 describes the triumph of Google's (Mountain View, California) artificial intelligence (AI) program, AlphaGo, which beat the 18-time world champion of Go, an ancient Chinese board game far more complex than chess. The authors have hypothesized that the development of intuition and creativity combined with the raw computing of AI heralds an age where well-designed and well-executed AI algorithms can solve complex medical problems, including the interpretation of diagnostic images, thereby replacing the microscopist. Of note, in a prior work, the microscope was predicted to have a 75% chance of remaining in use for another 144 years.2 To support their hypothesis, the authors presented recent studies that compared the performance of nontraditional interpreters to those of experienced pathologists, in making accurate diagnoses (note: 1 author disclosed a significant financial interest in an AI company). One study examined the potential of using pigeons (yes, pigeons) for medical image studies,3 wherein the pigeons engaged in a matching game of completely benign and unambiguously malignant breast histology images.

Machine Learning Algorithms Are Now Detecting Malaria


The device could be a major stride in diagnosing malaria, which affects over 200 million people annually. Malaria is a parasitic infection most commonly spread by mosquitos. It can be detected by assessing a patient's blood sample via microscope. Usually, a trained professional must be present to diagnose malaria, specifically a microscopist who can identify the malaria parasites in blood samples. But in the poorest areas of the world, where malaria is so prevalent, these professionals are in short supply.

AI-Powered Microscope Counts Malaria Parasites in Blood Samples

IEEE Spectrum Robotics

Today, a Chinese manufacturer and a venture backed by the Bill & Melinda Gates Foundation will announce plans to commercialize a microscope that uses deep learning algorithms to automatically identify and count malaria parasites in a blood smear within 20 minutes. AI-powered microscopes could speed up diagnosis and standardize detection of malaria at a time when the mosquito-borne disease kills almost half a million people per year. An experimental version of the AI-powered microscope has already shown that it can detect malaria parasites well enough to meet the highest World Health Organization microscopy standard, known as competence level 1. That rating means that it performs on par with well-trained microscopists, although the researchers note that some expert microscopists can still outperform the automated system.

Outsmarting Disease -- With Artificial Intelligence


More and more, 67-year-old Washington resident Lon Coleman feels like he's wandering through a fog. He walks into the living room and forgets why, or makes a phone call only to blank on whose number he dialed. An author of three books who once wrote up to five poems a day, now the lines that spring to his mind often slip away as soon as he puts pencil to paper. Sometimes the fog clears, and when his memory comes back, "it's amazing," he says. "Sometimes it doesn't, I have to admit."