Introduction to Deep Learning in Healthcare
Deep learning, a style of artificial intelligence, has been making waves within the medical field, particularly in ophthalmology, dermatology, radiology, and pathology. Researchers at Google have made significant progress in using deep learning to detect breast cancer metastases in lymph nodes, achieving accuracy comparable to that of a trained pathologist. However, one major obstacle to the widespread adoption of deep learning in pathology is the necessity for digital representations of microscopic tissue.
Overcoming the Barrier with Augmented Reality Microscope
To address this issue, a prototype Augmented Reality Microscope (ARM) platform has been developed. The ARM is a modified light microscope that permits real-time image evaluation and presentation of machine learning algorithm results directly into the sector of view. This modern platform could be retrofitted into existing light microscopes present in hospitals and clinics worldwide using low-cost, readily-available components, without requiring whole slide digital versions of the tissue.
How the Augmented Reality Microscope Works
The ARM uses modern computational components and deep learning models, corresponding to those built upon TensorFlow, to run a big selection of pre-trained models. As the user views the sample through the eyepiece, a machine learning algorithm projects its output back into the optical path in real-time. This digital projection is visually superimposed on the unique image of the specimen, assisting the viewer in localizing or quantifying features of interest. The computation and visual feedback update quickly, running at roughly 10 frames per second, allowing for seamless model output updates because the user scans the tissue.
Applications and Potential of the Augmented Reality Microscope
The ARM can provide a wide range of visual feedback, including text, arrows, contours, heatmaps, or animations, and is able to running many varieties of machine learning algorithms aimed toward solving different problems corresponding to object detection, quantification, or classification. As an illustration of its potential utility, the ARM has been configured to run two different cancer detection algorithms: one for breast cancer metastases in lymph node specimens and one other for prostate cancer in prostatectomy specimens. These models can run at magnifications between 4-40x, and the results of a given model is displayed by outlining detected tumor regions with a green contour.
Global Health Impact and Future Applications
The ARM has the potential to make a major impact on global health, particularly within the diagnosis of infectious diseases, corresponding to tuberculosis and malaria, in developing countries. Additionally, it may be used together with digital pathology workflows in hospitals where scanners face challenges or where rapid turnaround is required. The ARM may also be adapted for a broad range of applications across healthcare, life sciences research, and material science.
Conclusion
The Augmented Reality Microscope is a groundbreaking innovation that may speed up the adoption of deep learning in pathology and other fields. By providing real-time image evaluation and machine learning algorithm results, the ARM has the potential to enhance the accuracy and availability of high-quality healthcare worldwide. As researchers proceed to explore the chances of the ARM, it’s prone to have a major impact on global health and beyond.
