Artificial Intelligence (AI) in healthcare is going to improve the birth process of humans with better diagnosis method when baby is in mother’s womb. Yes, using the machine learning approach, now AI can help predict the pregnancy related risks.
As per the published in the American Journal of Pathology, a machine learning model can analyze placenta slides and inform more women of their health risks in future pregnancies, leading to lower healthcare costs and better outcomes.
Placenta Complications During Pregnancy
Actually, when a baby is born, doctors sometimes examine the placenta for features that might suggest health risks in any future pregnancies. Providers analyze placentas to look for a type of blood vessel lesion called decidual vasculopathy (DV).
These indicate that the mother is at risk for preeclampsia, a complication that can be fatal to both the mother and baby in any future pregnancies. Once detected, preeclampsia can be treated, so there is considerable benefit from identifying at-risk mothers before symptoms appear.
However, although there are hundreds of blood vessels in a single slide, only one diseased vessel is needed to indicate risk. This makes examining the placenta a time-consuming process that must be performed by a specialist, so most placentas go unexamined after birth.
How Machine Learning Predict Pregnancy Risks?
Researchers said, pathologists train for years to be able to find disease in these images, but there are so many pregnancies going through the hospital system that they don’t have time to inspect every placenta with full attention and accuracy.
While on the other hand researchers trained a machine learning algorithm to recognize certain features in images of a thin slice of a placenta sample. The team showed the tool various images and indicated whether the placenta was diseased or healthy.
Because it’s difficult for a computer to look at a large picture and classify it, the team employed a novel approach through which the computer follows a series of steps to make the task more manageable.
First, the computer detects all blood vessels in an image. Each blood vessel can then be considered individually, creating similar data packets for analysis.
Then, the computer can access each blood vessel and determine if it should be deemed diseased or healthy. At this phase, the algorithm also considers features of the pregnancy, such as gestational age, birth weight, and any conditions the mother might have. If there are any diseased blood vessels, then the picture is marked as diseased.
The tool achieved individual blood vessel classification rates of 94% sensitivity and 96% specificity, and an area under the curve of 0.99. While algorithm helps pathologists know which images they should focus on by scanning an image, locating blood vessels, and finding patterns of the blood vessels that identify.
The team noted that the algorithm is meant to act as a companion tool for physicians, helping them quickly and accurately assess placenta slides for enhanced patient care.
AI Assisted Pregnancy Risk Detection
Though, this algorithm isn’t going to replace a pathologist anytime soon. The goal here is that this type of algorithm might be able to help speed up the process by flagging regions of the image where the pathologist should take a closer look.
Such studies demonstrate the importance of partnerships within the healthcare sector between engineering and medicine as each brings expertise to the table that, when combined, creates novel findings that can help so many individuals.
Such useful findings have significant implications for the use of artificial intelligence in healthcare. As healthcare increasingly embraces the role of AI, it is important that doctors partner early on with computer scientists and engineers so that we can design and develop the right tools for the job to positively impact patient outcomes.
And with the high-quality healthcare training data for machine learning can further help to improve the risks level associated with pregnancies. AI companies are using the right training datasets to train such model to learn precisely and predict accurately.
Source: Health Analytics