Starting Ph.D. research in Medical Application- Tutors India

1. STARTING RESEARCH IN MEDICAL APPLICATION An Academic presentation by Dr. Nancy Agens, Head, Technical Operations, Tutors India Group  www.tutorsindia.com Email: info@tutorsindia.com

2. Today's Discussion OUTLINE In Brief Introduction AI Applications in Cancer Imaging Deep Learning, Medical Imaging, and MRI AI and ML: Shifting the Paradigm Conclusion Future Scopes

3. In Brief There has been great progress in research based on Artificial Intelligence in Medicine (AIM). The corresponding evolution of hardware technology, computer science, biomedicine, and communications has also be en tracked by AIM. Visualization of a new world of “high-performance medicine” by researchers and medical experts results from the convergence of human and artificial intelligence.

4. Introduction Medicine is has evolved as a data-centered discipline and, artificial intelligence (AI), in particular, machine learning (ML) has become an attractive field for analyzing the medical data. The current process of industrialization of AI has been reflected by this characterization. Therefore, the issues related to the use of Artificial Intelligence and Machine Learning should not be ignored anymore and definitely not in the medical domain. AI and ML are drawing much interest from the medical society as a solution to the knowledge extraction from data.

5. AI Applications in Cancer Imaging Artificial Intelligence helps to recognize image patterns that are complex in nature. It also provides the opportunity to interpret the images and transform them from a qualitative task to the quantifiable one. Artificial Intelligence can compute the data from the images which is a difficult task for humans and thus harmonizing decision making clinically. Artificial Intelligence can also combine multiple data streams and transform them into powerful integrated diagnostic systems spanning genomics, pathology, and electronic health records. Artificial Intelligence performs 3 main clinical tasks in cancer imaging: detecting, characterizing, and monitoring the tumors.

6. Deep Learning, Medical Imaging, and MRI To improve the efficiency of clinical practice, many deep learning methods are used which is increasing regularly. The efficiency of radiology practices can be improved using convolutional neural networks through protocol determination. Deep learning is also applied in the field of radiotherapy. Deep learning is also applied in advanced deformable image registration, which enables the quantitative analysis of different physical imaging modalities. Deep learning is used from image acquisition to retrieval and from segmentation to prediction of the disease. Contd..

7. This process is divided into two parts: (i) The signal processing chain, including restoration of images, and image registration. (ii) The application of deep learning in the segmentation of images, detection, and prediction of diseases, and systems based on images and reports, which addresses selected organs like the kidney, brain, the spine, and the prostate. Figure 1: Medical images

8. AI and ML: Shifting the Paradigm Artificial Intelligence has the potential to change the way the health care service is carried out. AI and ML provide solutions to complement the work of doctors for enabling the progress of new treatment paradigms. If there is a sign of large-vessel occlusion stroke in the scan, it is given first preference and sent to the radiologist’s queue, and the stroke team is also alerted. This helps in treating the patient at the correct time, thereby improving their health condition.

9. Conclusion For many decades the investigations of Artificial Intelligence and Machine Learning have been developed within the academic environment into broader social domains. Artificial intelligence is widely used in monitoring health resources and the result will likely improve efficiency and also reduces cost. As with any new technology, the possibilities for the development of AI in the medical field exist beyond current imagination.

10. Future Scopes Artificial Intelligence and Machine Learning assist radiologists to respond to pressures and interpret studies more rapidly. The pressure of radiologists to take the number of scans has increased in the past 5 years by as much as 20% to 50%. Blockchain is used in medical imaging applications. Blockchain helps to prevent the data breaches in the health care systems that have occurred recently.

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