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Mayuri Rane
Thadomal Shahani Engineering College

Cancer is evolving to be the second worst threat to humans after the cardiovascular diseases. Millions of people around the world are being diagnosed with cancer and the mortality rate graph doesn’t seem to fall down. Not knowing the major cause of this deadly disease has been the biggest factor in this growing trend and not having a complete cure or any prevention has just added to the seriousness of its existence.

Despite all these drawbacks, there’s always a hope to arrive to successful research and development of diagnostic analysis to help prevent the spread. With the great advent of the biotech study, we have managed to get to the roots of the different pathways that trigger the malignancy in the human body, yet getting to a cure is not concrete. This is where deep learning and artificial intelligence are playing great roles.

Usually cancer tumors are detected in the later or final stages and there is not much scope of treatment as the tumor has already spread enough to cause harm. The fact that early detection of malignancy could help in recovery has been a driving force for the AI enthusiasts and hence they have diverted their attention to help the health and medical sector function before time to save lives of the many that fall prey to cancerous tumors.

Many technological advancements are helping to collect, analyze and use the data of various previous health cases to help in the diagnosis and treatment of the cancer patients. For instance, when a young girl completed her routine follow-up in the USA, the reports showed an occurrence of medulloblastoma that is, a cancerous tumor in the brain which the young girl had already been recovered from through various radiotherapy treatments. The consulting doctor thought of suggesting her with the same treatments as she had priorly been treated with but due to one of his past experiences he decided to take a back seat and decide a different journey for her diagnosis.

He proceeded with the full genome-methylation analysis which analyzed the different hydrocarbons attached to the DNA. The concept behind this step strongly relates itself to epigenetics, a process in which the gene activity is altered without any mutation in the underlying genes. Once the results were obtained, he forwarded these results to a German Cancer Research Centre which had newly developed an AI system which could differentiate between the cancer types. Also, the analysis of the methyl groups helps to differentiate in the different cancer types. After processing all the information obtained, the doctor came to a conclusion that his patient was suffering from glioblastoma which was a completely different type of cancer when compared to medulloblastoma. If he had continued with the treatment and therapies for medulloblastoma, it would have severe effects on the patient and her cure would have stayed on the difficult side. This is an accurate illustration of the implementation of AI in the field of cancer genetics.

Some researchers are also working on the implementation of AI during screening tests so that it becomes easier for detection of tumors as well as patients with high risk of cancer. Identification of metastatic areas during regular testing can be carried out with a high percent of accuracy due to improved algorithms and more advancements in the software that are being developed to cater to these medical needs. Scientists have successfully obtained the software tool which helps to detect the cancer type quite accurately.

Unlike most of the cancer detection systems, prostate cancer is detected by biopsies using a standard set of positions on the prostate but in such a case the cancer can be missed. So, a new approach is using a multiparametric magnetic resonance imaging (MRI) in which different MRI scans are combined. This growing strength of AI will surely bridge the gap between diagnosis and treatment.

Huge software companies like Google are executing projects which will help medical sciences to grow and come to light. The development of microscopes with advanced software is also expected which will replace the traditional microscope in terms of cost and accuracy. This would help in obtaining quick results of tests which will help speed up the treatment. One of the recent published paper by a genomic team in Europe states that they have been able to successfully devise a deep learning algorithm that can help to identify molecular alterations from the images of different cancerous tumors. Also, they have mentioned about the accuracy of obtaining cancer patient biopsies for a few genetic alterations which could help doctors in deciding the treatment options after confirming their specificities.

The output of a combination of AI and Biotechnology will have a massive positive impact in the future of medical sciences for prediction, diagnosis and recovery.

Reference (Aug-20-A2)

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