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

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STARTING RESEARCH IN MEDICAL APPLICATION
An Academic presentation by Dr. Nancy Agens,
Head, Technical Operations, Tutors India
Group www.tutorsindia.com Email
info_at_tutorsindia.com
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Today's Discussion
In Brief Introduction AI Applications in Cancer
Imaging Deep Learning, Medical Imaging, and MRI
AI and ML Shifting the Paradigm
Conclusion Future Scopes
OUTLINE
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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.
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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.
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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.
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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..
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This process is divided into two parts

1. The signal processing chain, including
   restoration of images, and image registration.
2. 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
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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
radiologists queue, and the stroke team is also
alerted. This helps in treating the patient at
the correct time, thereby improving their health
condition.
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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.

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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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