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Neural Networks in biopsy can ensure accuracy and significantly reduce the processing time
A biopsy is a complex cognitive skill that is mastered over years with intense practice. And in certain cases, like a bone tumor, a biopsy carries the risk of displacing the bone. It is reported that in conventional biopsies, such as for breast cancer, the agreement can be as low as 48%. Given the massive amount of information that should be reviewed, it becomes a time-consuming and hazy task. This is where deep neural networks can come to help in diagnostics involving biopsy. Application of Neural network in biopsy involves AI trained neural network which processes the humongous amount of data to generate the results.
What is a neural network?
An artificial neural network is a circuit of biological neurons programmed using artificial intelligence to process information. Their design is inspired by connections in the biological neural network. In other words, a neural network can be considered as an AI-enabled biological network that can simulate biological data, and perform image analysis, speech recognition, and adaptive control. They are trained by feeding a dataset to derive conclusions from a complex and unrelated set of information. Neural network in biopsies basically uses image processing techniques to generate processed images of tumors and malformations.
How does it work for biopsies?
Biopsies involve invasive methods such as extracting a part of the affected organ. Quite often they carry risks of infection and bleeding. In addition, the pathologists have to put it through a lengthy process and wait for days to arrive at a conclusion. What if all this process can be bypassed with a simple non-invasive method. The neural network in biopsy exactly does this. For example, in a recent method developed by UCLA Samueli School of Engineering and David Geffen School of Medicine at UCLA, called Virtual Histology, the pathologist can take images of suspicious tumors present on the skin and develop a microscopic image of the skin. Dr. Scumpia, a researcher working on the project is of opinion that when combined with other machine learning techniques, it can extract more information. This is just one case of how d. Of-late Neural network in biopsies has become go-to technologies in different areas like a breast cancer diagnosis, renal transplantation rejection, etc.
Neural network in biopsy can never be the holy grail of pathology.
Like any other computational technique, the application of neural networks to biopsy has limitations. The algorithms can perform only to the extent they are trained. They may fail to identify an additional condition that could have been done by a trained pathologist. Therefore, to ensure the best clinical outcomes, the use of neural networks in a biopsy should be limited only to assisting the pathologist rather than taking charge of the whole process.