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Artificial intelligence-based algorithm for intensive care of traumatic brain injury

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A recent Finnish study published in Scientific Reports presents the first artificial intelligence (AI)-based algorithm designed for use in intensive care units for treating patients with severe traumatic brain injury. The project is a collaborative project between three Finnish university hospitals: Helsinki University Hospital, Kuopio University Hospital and Turku University Hospital. Traumatic brain injury (TBI) is a significant global cause of mortality and morbidity with an increasing incidence, especially in low-and-middle income countries. The most severe TBIs are treated in intensive care units (ICU), but in spite of the proper and high-quality care, about one in three patients dies. Patients that suffer from severe TBI are unconscious, which makes it challenging to accurately monitor the condition of the patient during intensive care.


Machine Learning Method Allows Hospitals to Share Patient Data Privately

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Penn Medicine researchers used federated learning to train an algorithm to analyze magnetic resonance imaging scans of brain tumor patients to distinguish healthy brain tissue from cancerous regions. Researchers at the University of Pennsylvania's Perelman School of Medicine, in conjunction with the University of Texas MD Anderson Cancer Center, Washington University, and the Hillman Cancer Center at the University of Pittsburgh, have developed a machine learning method that can facilitate the sharing of patient data without compromising privacy. The model uses the federated learning approach that trains an algorithm across multiple decentralized devices or servers containing local data samples without exchanging them. The researchers found the approach to be successful in analyzing magnetic resonance imaging (MRI) scans and distinguishing between healthy brain tissue and cancerous regions. The model could allow doctors in hospitals worldwide to input their own patient brain scans, which would support the development of a concensus model that would be clinically useful.


Tokyo Medical University scandal just reaffirmed what many female doctors already knew: The bar was higher for them

The Japan Times

The admissions scandal whereby Tokyo Medical University admitted to manipulating females' entrance exams did not come as a surprise for many women doctors, but rather was verification of what they had suspected for a long time: Some medical universities set the bar higher for women. That suspicion was backed up by the fact that the ratio of women who have passed the national medical exam consistently stayed at around 30 percent for nearly 20 years. "We heard rumors a number of times that medical universities were placing caps on the number of female students," said Ruriko Tsushima, an obstetrician and the head of Tsushima Ruriko Women's Life Clinic Ginza in Tokyo. "Such practices should not be forgiven." Another doctor who currently works at a private hospital in Tokyo also said it was "common knowledge" among female students who were planning to apply for medical school.


'Unsafe' A&E services halted in Stafford

BBC News

Children's accident and emergency services at a scandal-hit hospital have been suspended after senior staff said it was "not clinically safe". A shortage of staff with the right specialist training at Stafford's County Hospital had forced the closure to patients under 18, the trust said. The hospital was the subject of a public inquiry after hundreds of patients died amid "appalling" levels of care between 2005 to 2008. Robert Francis QC, who led the public inquiry, made 290 recommendations calling for "fundamental change". Some patients were left "sobbing and humiliated" due to mistreatment by staff, the inquiry heard.


AI project greenlit after reducing A&E attendances by a third

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An AI project which successfully cut A&E attendances by a third has been greenlit for a wider rollout. Over 1,000 patients were involved in a trial of an AI system developed by Health Navigator at York Teaching Hospitals Foundation Trust over the last four years. AI was used to identify patients at risk of unplanned hospital admissions. By highlighting these patients, nurses were deployed to help coach them over six months on how to improve their health and reduce the risk of visiting A&E. The trial resulted in a 30 percent reduction in unplanned hospital admissions and a 25 percent reduction in planned admissions.