Digital Pathology Technology Set to Transform Medical Diagnosis

Pathologists have historically encountered significant challenges in acquiring sharp, in-focus images without manual intervention. Given that digital pathology images are often thousands of times larger than regular digital photos, automating microscopy for diagnosis from tissue, blood, and other specimen types was considered impossible. However, after a decade of rigorous research by a team of scientists, a transformative breakthrough in medical diagnosis is set to enhance the speed and accuracy of pathology results.

Queensland University of Technology (QUT, Brisbane, Australia) and Sullivan Nicolaides Pathology (SNP, Queensland, Australia) have pioneered an automated microscope scanning and analysis system that has undergone rigorous testing and is now implemented and accredited, ready for global deployment. The system has been shown to significantly improve testing in terms of cost-efficiency, quality, and speed. The cutting-edge digital pathology technology is capable of processing thousands of tests daily and has received accreditation from the National Association of Testing Authorities (NATA).

The system has been shown to enhance the productivity of pathologists and scientists by a factor of 10 or more. It also offers the benefit of seeking second opinions through telepathology and significantly improves record-keeping and access to historical records as there is no need to archive glass slides for years. The system is poised to revolutionize various aspects of healthcare and is already being used by SNP laboratories to enhance diagnostic speed and accuracy.

“Our scientists now use a digitized image often with associated AI instead of being tied to a microscope for many hours,” said SNP Chief Executive Officer Dr. Michael Harrison. “This is the most significant change in the performance of morphological tests for decades.”

“Our active scanner knows what it is scanning and where it should scan, using image analysis and artificial intelligence,” added UQ Professor of AI Brian Lovell. “This greatly increases image quality and reduces file size.”

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