To be precise, artificial intelligence came into the medical arena to help us do tasks either faster or more accurate – but apart from that, the tech is now assisting us to accomplish tasks that were originally second to impossible. Before this new venture (where now researchers are using AI to evaluate cell therapy functionality,) experts tried to use machine intelligence to kill cancerous cells, using tinny robots.
As in, although not approved for human use, a team of scientists managed to inject tinny autonomous agents into the bloodstream of a living specimen to go shrink cancerous cells, by blocking their blood supply.
Looking at that, and other clinical findings that try to focus on the cell, to try to create the precision treatment for different patients, we come to realize how the cell, as an organ is becoming a key focus in the medicine world, in therapy creation.
Classifying Cells Using Artificial Intelligence
The deeper we go into certain medical studies the more the need to want to know about the cells — how they can be altered to help pass an intended signal to the immunity system to make it fight pathogens, and so on. But, while that happens to be a great approach for developing customized treatment, there has been this one need, to evaluate the effectiveness of the induced cells or the injected pluripotent stem cell in this case.
In that line, researchers have built a complete automated AI-driven multispectral imaging system that can classify the potency of stem cells – in treating (AMD) or age-related muscular degeneration. That is, using cells derived from retinal pigment epithelial tissues (iPSC-RPE). The work was reported at the 2018 ARVO conference.
What’s in the New Software
For years, medics have been trying to develop anti-aging solutions through drug research and all other viable methods, but to be fair there has not been any practical success that can be applied. Nonetheless, doctors claim and still insist that indeed it is possible to slow aging, and in the long run even reverse it because the whole thing revolves around reprogramming the DNA in cells.
Hopefully, this development is pointing us to that. Well, the new algorithm deploys a method called CNN (convolutional neural network). A deep learning technology to analyze, evaluate and cluster categorically, the iPSC-RPE cell therapy, with reference to standard, cost-based fashion and reproducible. All without human involvement, something that for long has been regarded as a sensitive physiological and molecular assay left to experts alone.
“Our model can classify cells with high accuracy by just analyzing absorbance images,” explained Nathan Hotaling and his fellows from the National Institute of Health, in the report. “Yes, the concept deploys multispectral absorbance imaging as the major technique, but it has proven to be extremely fast, non-invasive, robust, and top of all, it is fully autonomous, in gauging induced PSC maturity, identifying and rating functionality,” he added.
Artificial Intelligence Channels its Way into Medicine
Initially, it was all debate about whether truly machine intelligence will one day be in charge of medical diagnostics, but as things are turning now, we are soon going to have a pure robot as the major consultant in therapeutic topics. Off cause, it’s obvious that the public will take sometimes before trusting agents, but looking from another perspective, there will be that section of people who will prefer machines as their main “doctor” over humans.
Well, that aside, AI is already gaining grounds with the medical community and this is reflected in the way the FDA has started clearing –for human use, AI-powered medical gadgets. With the first device to be officially given clearance being a tool for the detection of diabetic retinopathy, christened the IDx-DR.
IDx-DR is praised for being super-fast and accurate, where it uses algorithmic power to analyze images from a retinal camera to offer diagnosis services within half an hour, something that does take quite long with the conventional methods.
Test Score: In the finding, the developed CNN together with ML were used to further compare the absorbance images to establish characteristics while grading iRPE in clinical assessment, and the system demonstrated a 97 percent accuracy in sensitivity — in the average of all correct and recommended criterions.
In short, we are entering a time when diagnostics will be done by machines autonomously. In addition, AI is also reducing the waiting time that doctors needed to design therapy suggestions.