Health & Medicine, Australia (Commonwealth Union) – Advancements in artificial intelligence (AI) have brought about remarkable transformations in various industries, and one field that has significantly benefited from AI is surgery. AI applications in surgery hold immense potential to improve patient outcomes, enhance surgical precision, and revolutionize the way surgical procedures are conducted.
AI algorithms are being used to analyze medical imaging data, such as CT scans and MRI scans, to assist surgeons in preoperative planning. By accurately identifying anatomical structures and highlighting potential abnormalities, AI algorithms provide invaluable guidance for surgical approaches. This enables surgeons to visualize the surgical site in greater detail, resulting in more precise and efficient planning.
Robotic-assisted surgery has gained popularity in recent years, and AI is at the heart of these surgical robots. AI algorithms enable real-time image analysis, motion tracking, and instrument control, allowing surgeons to perform precise and minimally invasive procedures. Surgical robots, controlled by skilled surgeons, offer unparalleled precision, flexibility, and dexterity, thus reducing complications and improving patient recovery times.
Recent research led by a PhD researcher from The University of Western Australia (UWA) has discovered that AI utilizing computer or machine learning can assist in lowering common cardiovascular complications following non-cardiac surgery, consisting of heart attacks together with injuries to the heart muscle.
Dr Janis Nolde, from the UWA, Medical School along with the Royal Perth Hospital, along with a global team of researchers assessed data from over 24,000 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation (VISION) study, with the results appearing in Anaesthesia.
Researchers hoped to ascertain if machine learning and data had the ability to forecast medical complications specifically cardiovascular complications from surgery (which excluded heart surgery) prior to their occurrence, so as to better mark as well as treat vulnerable patients.
“Every year, more than 200 million patients globally undergo major non-cardiac surgeries, and around 10 million of them experience a significant cardiovascular event within 30 days, which can lead to higher mortality rates, poor health and decreased long-term survival,” explained Dr Nolde further indicating that the most frequent cardiovascular complications post-surgery are heart attacks along with injury to the heart muscle, however they are regularly hard to detect as symptoms can be veiled and routine tests can fail to detect them.
The application of a sensitive laboratory test which gages a protein (troponin) released into the bloodstream when there is harm or injury to the heart muscle, the researchers discovered that 1 in 6 patients have increased levels in the 1st 3 postoperative days.
Dr Nolde further indicated that the condition, referred to as myocardial injury following non-cardiac surgery, is linked with a greater risk of death and other serious complications in the next few weeks, however forecasting of this is hard with variables like age, fitness, any underlying medical disorders and issues that come up during or early after surgery, all of which have to be taken into account.
“Machine learning, particularly neural networks, offers a promising approach as these techniques can analyse large amounts of data and identify complex patterns and relationships that are otherwise difficult to spot. They are very adaptable, so can be implemented and tailored to many different settings.”
Professor of Medicine at UWA and Head of Cardiology at Royal Perth Hospital Graham Hillis stated that the findings of the research indicate that integrating machine learning methods with routinely gathered data before, during and after surgery can be a promising technique to enhance the identification of patients at most risk and those whose risk may be elevated over time.
Professor Hillis indicated that this may permit healthcare professionals to identify issues earlier and intervene quicker to mitigate possible complications.
“Further work is planned to fine-tune these methods and embed them into routine care.”
