Artificial Intelligence in medical sector: It used to appear incredible that surgeons could look inside the human body without making any incisions. However, medical imaging in radiology has come a long way, and the most recent AI-driven approaches are going far further. They analyze body scans for distinctions that even a human eye can overlook by utilizing the vast computational power of machines and artificial intelligence. Modern imaging in medicine incorporates complex methods of evaluating each data point to separate health from sickness and signal from noise. The next few decades of radiology will be devoted to evaluating that data to make sure nothing is missed, if the first few decades were spent improving the resolution of the body images taken.
Imaging is changing from its original purpose of diagnosing medical diseases to becoming an essential component of treatment, particularly in the case of cancer. In order to monitor tumors and the spread of cancer cells and to determine whether treatments are effective, doctors are starting to rely more and more on imaging. The types of therapies patients receive will change as a result of imaging’s new function, and the data doctors receive about how well those treatments are working will greatly improve, allowing doctors to ultimately make better decisions about the types of treatments they need.
Functional imaging will become a part of care within the next five years, predicts Dr. Basak Dogan, an associate professor of radiology at the University of Texas Southwestern Medical Center. We believe that the real clinical questions cannot be answered by the present conventional imaging. However, patients who desire greater accuracy in their treatment so they may make more informed decisions will find that functional approaches are the solution.
Using artificial intelligence to detect issues earlier
Whether using X-rays, CT scans, MRIs, or ultrasounds, the first challenge in maximizing image quality is automating reading them as much as possible to free up radiologists’ valuable time. Because of the enormous processing power available today, it is possible to teach computers to distinguish between confusing and normal discoveries, artificial intelligence has demonstrated their value in this field. Radiologists feed their findings on thousands of normal and abnormal images to computer programs, which teach the computer to recognize when the images contain things that fall outside normal parameters. Software experts and radiologists have been working together to develop these formulas for years. The machine learns from more photographs and compares to get better at fine-tuning the differences.
For the U.S. Food and Drug Administration (FDA) to approve an algorithm involving imaging, it must be accurate 80% to 90% of the time. So far, the FDA has approved about 420 of these for various diseases (mostly cancer). The FDA still requires that a human be the ultimate arbiter of what the machine-learning algorithm finds, but such techniques are critical for flagging images that might contain suspicious findings for doctors to review—and ultimately provide faster answers for patients.
At Mass General Brigham, physicians use about 50 such algorithms to assist them in providing patient care. These algorithms range from identifying aneurysms and cancers to identifying embolisms and stroke symptoms in patients who present to the emergency room with many of the common symptoms associated with these conditions. The FDA has authorized about half of them, while the remaining ones are undergoing clinical trials.
Keeping better track of patients using artificial intelligence
While the first stage in using artificial intelligence (AI) based help in medicine is computer-assisted triaging, machine learning is also developing into a potent method of monitoring patients and tracking even the tiniest changes in their situations. This is especially important in the case of cancer, when deciding whether a patient’s tumor is expanding, contracting, or staying the same requires the time-consuming effort of monitoring the patient’s tumor. According to Dogan, “We struggle to grasp what is occurring to the tumor as patients receive chemotherapy.” Unfortunately, unless any sort of shrinkage begins to take place halfway through treatment, which could take months, “our usual imaging tools can’t identify any change.”
In some circumstances, imaging can be helpful by detecting changes in tumors that are unrelated to their size or architecture. “Most of the alterations in a tumor are not nearly at the level of cell death in the very early phases of chemotherapy,” adds Dogan. The modifications involve altering how the body’s immune cells and cancer cells interact. Additionally, cancer frequently does not contract in a predictable manner from the outside in. Instead, certain cancer cells within a tumor may disappear while others keep growing, causing the mass to become more pockmarked and resemble a moth-eaten garment overall. In rare instances, the tumor’s size may even grow since some of that cell death is linked to inflammation. Even if that does not necessarily mean that the cancer cells will continue to develop. Currently, it is impossible to determine how much of a tumor is still alive and how much is dead using standard imaging. Mammography and ultrasound, the two most often used breast cancer imaging methods, are intended to detect anatomical characteristics.
Dogan is evaluating two methods at UT Southwestern for using imaging to monitor functional changes in breast cancer patients. She is imaging breast cancer patients with funding from the National Institutes of Health after one chemotherapy cycle to detect minute changes in pressure around the tumor by injecting microbubbles of gas. As tumors grow, these bubbles tend to gather around them and fluctuate in pressure; compared to normal tissues, growing malignancies have more blood arteries to sustain their proliferation.
Laser doppler imaging, which converts light into sound signals, is being tested in a different investigation by Dogan. By oscillating, cells in breast tissue are exposed to laser light, which generates sound waves that are recorded and analyzed. Since cancer cells typically require more oxygen than healthy cells to continue growing, this approach is well suited to determining the oxygen levels in tumors. The difference between a tumor’s developing and non-growing areas can be determined by changes in sound waves. According to Dogan, “We can distinguish which tumors are most likely to metastasis to the lymph nodes and which are not just by imaging the tumor.” It is currently impossible for doctors to predict which malignancies will move to the lymph and which won’t. It might provide insight into how the tumor is going to behave.
Additionally, without the need for invasive biopsies or visual scans, artificial intelligence may be able to detect early indications of cancer cells that have spread to other bodily regions. Doctors may have a better chance of finding these fresh deposits of cancer cells if they concentrate on organs like the bones, liver, and lungs where cancer cells often spread.
Recognizing unnoticed anomalies
According to Dreyer, these algorithms may even identify aberrations for any ailment that no human could find with enough data and photos. His team is also aiming to create an algorithm that monitors specific biomarkers in the human body, both anatomically and functionally, in order to detect changes that would indicate a person is at risk of suffering a stroke, fracture, heart attack, or other unfavorable occurrence. While it’s still a few years away, according to Dreyer, “those are the kinds of things that are going to be transformational in healthcare for artificial intelligence.” That is the holy grail of imaging.
It will require a ton of data from hundreds of thousands of patients to get there. The United States’ compartmentalized healthcare systems make it difficult to share such data, though. One option is federated learning, in which researchers create algorithms that are then applied to databases of anonymised patient data from other institutions. Institutions won’t have to risk their safe systems, and privacy is preserved.
The use of artificial intelligence (AI) based imaging may even begin to benefit patients at home if more of those models are validated, whether through federated learning or another method. People may someday be able to access imaging data using portable ultrasounds delivered via a smartphone app, for example, when self-testing and telehealth became more commonplace as a result of COVID-19.
The significant revolution in health care that will result from AI will be that it will provide many solutions to patients themselves, or before they become patients, so that they can stay healthy, according to Dreyer. Giving patients the knowledge and tools to make the best decisions for their health would probably be the most effective strategy to optimize imaging.
Conclusion
They analyze body scans for distinctions that even a human eye can overlook by utilizing the vast computational power of machines and artificial intelligence. The modifications involve altering how the body’s immune cells and cancer cells interact. Mammography and ultrasound, the two most often used breast cancer imaging methods, are intended to detect anatomical characteristics. Artificial intelligence may be able to detect early indications of cancer cells that have spread to other bodily regions.
