University of South Australia

A new deployable AI tool for the neonatal intensive care unit can identify a deadly condition in premature infants. WHAT IT'S ABOUT A research team from Asan Medical Center, the largest hospital in South Korea, created a deep multi-task learning model that learns and pinpoints areas filled with air in the abdominal cavity or locations of lesions called intestinal perforation. From an AMC database of approximately 2.6 million paediatric X-ray images spanning 23 years, 294 images of intestinal perforation and 252 control images were utilised to train the model. The team applied data augmentation so the AI can simultaneously identify different patterns of intestinal perforation and lesion location in each patient. FINDINGS Based on findings published in Computers in Biology and Medicine, the model showed 94.9% accuracy when verified using internal data. Researchers also validated the model using external data: 164 intestinal perforation images and 214 control images were selected from a collection of 64,000 images from 11 local hospitals. Using this data, it demonstrated 84.1% accuracy, on par with specialists. The model was also found to help increase the medical staff's diagnostic accuracy from 82.5% to 86.6% and inter-interpreter agreement from 71% to 86%. WHY IT MATTERS Intestinal perforation is a serious threat to newborns, especially premature infants. Diagnosis requires X-ray imaging, but interpretation is difficult because lesions are often unclear, and immediate readings in the NICU increase the risk of delayed or missed diagnoses. Existing AI models also have limited applications to newborns since they are trained on adult data, which differs significantly in body size, posture, and clinical presentation. AMC's AI model for identifying neonatal intestinal perforation has shown potential to enable early detection of such a fatal condition in newborns, helping prevent NICU deaths. "Neonatal intestinal perforation presents a high level of urgency, making rapid diagnosis paramount. However, imaging findings are ambiguous and differ from those in adults, making the diagnostic rate significantly dependent on the interpreter's experience," explained Dr Yoon Hee-mang, a professor at the AMC Department of Radiology. Besides demonstrating expert-level accuracy, the model also helped enhance professional consensus, Dr Yoon added. THE LARGER TREND The past few years saw various projects launched across the Asia-Pacific to enhance care for newborns and premature infants. In Australia, the University of South Australia has demonstrated through studies how AI-driven imaging systems can remotely monitor vital signs of premature babies in the NICU and diagnose and automate the treatment of jaundice in newborns within a second. A SAP-backed screening programme in India utilises a machine learning-based tool to identify retinopathy of prematurity , a condition associated with preventable blindness among preterm infants.

A new AI tool developed at the University of South Australia can predict when a patient can safely withdraw from taking long-term antidepressants. WHAT IT'S ABOUT Using AI, UniSA researchers analysed passively collected and underused dispensing data from the Pharmaceutical Benefits Scheme (PBS) to identify the most successful cases of deprescription among 100,000 patients over a 10-year period. The team trained and tested two machine learning algorithms – one assessed final prescription records and the other monitored patients' dose reductions and outcomes from their first prescription. "By applying AI to the PBS database, we have identified patterns linked to successful withdrawal, forecasting which patients are most likely to succeed when taking them off antidepressants," explained Dr Lasantha Ranwala, UniSA doctorate student and AI researcher. Based on findings presented at MedInfo 2025, an international conference on digital health and informatics, the models demonstrated 81% and 90% accuracy, respectively. WHY IT MATTERS Antidepressant use has continued to soar worldwide, including in Australia, where the medication was dispensed to 14% of its population in 2023-2024 alone. Among Australians aged 10-24, who already accounted for 35% of long-term users, prescriptions saw the sharpest increase in the same period. Prolonged antidepressant use has been linked to such side effects as weight gain, sexual dysfunction and cardiac issues. Studies have also noted that about half of patients experience withdrawal effects after ceasing medication. An AI that can predict safe antidepressant withdrawal can help general practitioners deprescribe obsolete medication "with greater confidence," according to UniSA. "Healthcare providers are often reluctant to cease antidepressant prescriptions due to their concerns about withdrawal effects, making it difficult for doctors to know who can safely discontinue treatment," claimed Dr Ranwala. A successful deprescription is defined as the absence of any antidepressant medication for at least one year following previous long-term use (more than 12 months). A deprescribing attempt is considered a failure if the medication strength rises again within six months of reduction. The research team is now working to make the AI tool more accurate and easier to use, with plans to test with clinics and explore how AI could be used to help patients improve their medicine intake. THE LARGER TREND Studies worldwide have increasingly demonstrated AI's potential to support clinicians in developing optimal, personalised medication protocols. Another new study in New South Wales, for example, is using AI and genomic data to predict patient responses to antidepressants . The project, which recently received nearly $2 million in federal grant funding, aims to reduce trial-and-error prescribing. A Monash University-led study has developed AI that can identify the optimal anti-seizure medication for newly diagnosed epilepsy patients.

SATURDAY, March 29, 2025 -- A new formula made with natural citrus oil could help cancer patients find relief from dry mouth, a common and painful side effect of radiation treatments. The formula was created by researchers at the University of South Australia and Stanford University. It mixes limonene -- a citrus oil found in lemons, limes and oranges -- with healthy fats known as lipids. Mixed together, they make the oil much easier for the body to absorb and reduce common side effects like dry mouth and stomach pain. In lab tests, the new mix was 180 times more soluble than pure limonene. In early trials, it increased absorption in the body by more than 4000%, according to a news release. Dry mouth, or xerostomia, affects up to 70% of patients who get radiation for head and neck cancer. It can make speaking and swallowing very difficult and can lower quality of life. “Cancer patients undergoing radiotherapy and other medical treatments regularly experience dry mouth, which not only prevents them from comfortably swallowing, but can also have other negative and potentially life-threatening outcomes,” said co-researcher Leah Wright , a professor of chemical engineering at The University of Adelaide in Australia. Limonene has long been known to help with saliva production, but high doses were needed to work well. Those doses often caused side effects like indigestion and “citrus burps.” Lead author Clive Prestidge , head of the Nanostructure and Drug Delivery research group at the University of South Australia, says the new formula solves that problem. “The therapeutic benefits of limonene are well known. It’s used as an anti-inflammatory, antioxidant and mood-enhancing agent, and can also improve digestion and gut function. But despite its widespread use, its volatility and poor solubility have limited its development as an oral therapy,” Prestidge said in a news release. Wright added that even though limonene can be ingested directly, it's not well tolerated, especially with folks who have dry mouth. "Plus, its poor absorption prevents it from effectively reaching the salivary glands -- the target site," she added. “This inventive and highly impactful limonene-lipid formulation could provide a simple, effective oral solution for dry mouth, offering cancer patients long-lasting relief and comfort, improved oral health, and a higher quality of life during a difficult time,” Wright said. The findings were recently published in the International Journal of Pharmaceutics . Whatever your topic of interest, subscribe to our newsletters to get the best of Drugs.com in your inbox.