What does mathematics have to do with medicine?

Mathematics and science go hand-in-hand, and thus the field of medicine and surgical implants alike attracts mathematically minded individuals. I’ll give a personal example starting with learning algorithm and artificial intelligence. My current dementia research experiences daily can help ascertain how deeply intertwined math is with the field of medical science.

Today mathematics is the driving force behind most modern medical procedures, as learning algorithm and artificial intelligence are incorporated into new human implants.

The field of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two areas of human implants, it is becoming commonplace and may be essential for the advancement of future research into the neurological conditions.

Mathematics is helping scientists discover new depths within the brain that were previously undiscovered. The ability to use deep learning algorithms combined with artificial intelligence can help scientists find areas of the brain that were previously undiscovered. These breakthroughs in medical science allow researchers to develop a tiny implant’s as small as a grain of rice and as thin as a sheet of paper.

In this paper, I argue that introducing mathematics into medical science overtakes traditional research in medicinal chemical compounds, in the discovery of new innovative ways of helping people diagnosed with life-threatening diseases, that affect an individuals quality of life.

For instants, Neuroscience has traditionally perceived as a subdivision of biology. These days, it is an interdisciplinary science which liaises closely with other disciplines, such as deep learning algorithms and artificial intelligence.

Some researchers believe that neuroscience means the same as neurobiology. Neurobiology looks at the biology of the nervous system, while neuroscience refers to anything to do with the nervous system.

Neuroscientists are involved in a much broader scope of fields today than before. Due mainly to the advances in disciplines of mathematical, and medical science modeling. These models are allowing to research profound aspects of the brain nervous system.

Modeling using these new disciplines allowed us to design and develop the implant that is predicted to recover 35% of short-term memories for a patient’s suffering from dementia — using a tiny implant which is inserted into a small insertion in the groin and then maneuvered into the posterior cerebral artery, which feeds the brain’s hippocampus.

The implant is maneuvered very carefully into position; then using a remotely controlled computer, the neuroscientist, activates the implant to unfold the five tiny senses.

The implants software uses a learning algorithm that is designed to compare the memory neural firing rates patterns of people, not affected by dementia. The device then mimics the firing rates codes to a dementia patient’s short-term memory. It rebuilds and enhances the dementia patient’s memory losses by stimulating their own hippocampal spatiotemporal neural codes.

Using this model, it predicted a significant improvement of approximately 34% in memory recall.

just one example of the way mathematics and medical science are converging.

At the same time, computer algorithms, software modeling, and hardware advances have brought machine learning to previously unimagined levels of achievement throughout the field of neurological science.

In recent decades, brain researchers have learned a considerable amount about the physical conductivity within the brain and about how the nervous system routes information and processes it. However, there is still a vast amount yet to be discovered.

To give you a better understanding of the way mathematic is evolving in medical applications, you should click on the following posts.



For further examples as too, how mathematics is evolving in the field of medical science, go to our blog site.


Author Doc.G


Microscopes, with the learning algorithm, identify dendritic spines with 90% accuracy.

It has been possible for us to learn a tiny amount about the brain by using microscopes, and it even been taught by neuroscientists how to recognized part of the brain circuitry.

However, now neuroscientist and software engineers, have developed a new learning algorithm software with the objective of vastly improving the daily life of a microscope user.

A Combination, of the new specific algorithm, aptly named a neural network with a small amount of training, microscopes can now autonomously and efficiently identify small neuronal compartments called dendritic spines with over 90% accuracy.

Researchers are studying the complicated process called synaptic plasticity, which is thought to be the cellular basis of learning and memory. When single dendritic spines stimulate, hundreds of signaling molecules are mobilized to carry new information throughout the neuron.

Scientists must patiently sift through a neuron’s dendritic arbor, scanning hundreds of spines for suitable candidates to use for imaging.

Experiments are frequently repeated to amass sufficient data, and those that fail mid-way then we must be restarted. This often unspoken aspect can quickly turn prolonged imaging into a tedious and time-consuming chore, ultimately slowing scientific progress.

A study published in PLOS ONE, Dr. Michael Smirnov, Ph.D.


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