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For centuries the microscope has been one of the most important tools in science, enabling the examination of objects invisible to the naked eye. Commonly when people think of microscopes they think of light microscopes, also known as optical microscopes, which work by passing light through a sample and some magnifying lenses into your eyes through the ocular lenses. The first microscope was developed by a Dutch father-son team, Hans and Zacharias Janssen, in the late 16th century. The duo discovered that by stacking more lenses over one another, they were able to magnify an object even further. As a result, they constructed the first microscope by adding some lenses to the end of a tube.
Since the development of the Janssen’s microscope and the other subsequent pioneering versions of the optical microscope, the magnification power and image quality substantially improved over decades, until the fundamental physics of light came in the way limiting resolution. Later on in the 20th century, this limitation led to the development of more advanced microscopy methods such as electron microscopy or scanning probe microscopy, which allowed for even greater magnification with a higher resolution.
Although these advanced microscopy technologies made great strides in science, a true revolutionizing factor in microscopy was the development of digital microscopy in 1986. Japanese scientists realized they could record the magnified image formed to our eyes into a computer, paving the way for a whole new world of opportunities.
The development of digital microscopy has seen significant benefits, including advancements in scanning and image digitization technologies, particularly in medical sciences. These new technologies have led to the creation of whole slide imaging (WSI), also referred to as virtual or wide-field microscopy - a crucial method for performing histological analyses computationally. But what is WSI and why is it beneficial?
WSI produces high-resolution digital images from different glass slide samples such as tissue sections or smears, with a high-speed slide scanner. The scanning can be done with multiple magnifications and it can capture a whole slide instead of just a small area. The digital format allows the images to be manipulated directly, for example for enhanced contrast or colors, among other markings and annotations. Also, digital microscopy images are much easier to store, archive, and share.
Digital sharing holds numerous advantages such as the possibility for real-time consultation or second opinions, and collaborative projects or research validation without the need to send physical slides anywhere. Additionally, as the recent COVID-19 pandemic taught us, another significant advantage of digital sharing is e-learning. Like all hands-on style subjects, histology and pathology learning faced major challenges during the pandemic due to forced remote learning and limited access to hands-on microscopy sessions. Luckily, cloud-based microscopy and e-learning platform solutions such as the Aiforia Hub for Education eased these challenges.
Aiforia Hub for Education software for e-learning.
The Aiforia Hub for Education platform is meant for teaching and learning histology, anatomy, pathology, and other fields, and is particularly useful for remote learning. It allows educators to upload any image from any scanner and share these directly with students. Case notes, annotations, and background information can be added along with the images. Gjermund Gunnes, Professor of Veterinary Pathology at NMBU, describes their experience using Aiforia and how the platform became an invaluable tool for their students, in this interview.
In addition to the above-mentioned fields, another domain to benefit from digital microscopy, and especially WSI, is pathology - even to the extent that a novel subfield, digital pathology, emerged. Learn more in our post detailing digital pathology and its benefits. Lastly, the development of digital microscopy laid the foundation for the next revolution in microscopy, which is already underway: automated sample analysis using artificial intelligence.