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The machine-learning algorithm to detect shock by thermal imaging has 75% accuracy
Predicting shock (less blood and oxygen supply to major organs, which can lead to death) even 12 hours before it can be clinically recognised by doctors by using the current gold standard (intra-arterial blood pressure) is now possible, thanks to the work by an AIIMS-led multi-institutional team of researchers. Shock can arise from loss of blood volume, inefficient pumping by the heart or infection (sepsis).
Efficient algorithm
The machine-learning algorithm to detect shock at the time a single photo is taken using thermal imaging has an accuracy of 75%. The ability of the algorithm to forecast the probability of a shock happening three, six and 12 hours before clinical recognition can be done using the gold standard method is 77%, 69% and 69% respectively. The algorithm was used in conjunction with pulse rate to both detect and predict shock. The results were published in the journal Scientific Reports. In paediatric intensive care units, 70-90% babies develop signs of sepsis. Almost 30% paediatric ICU patients suffer from sepsis shock and 30% of them end up dying due to multiorgan failure. “This number will be much higher at district hospitals. Sepsis shock is one major killer in paediatric ICUs,” says Dr. Tavpritesh Sethi from the Department of Paediatrics at AIIMS, New Delhi, who led the team. In principle, the model can be used for predicting shock in adult patients too. But the model has to be tested on adults as the current study was limited to 539 thermal images of paediatric patients. It is possible to prevent organ failure and death by adopting simple measures such as fluid management through transfusion and raising the blood pressure using certain drugs. Body starts responding to shock very quickly but takes some time for clinical recognition. This is where the machine-learning algorithm comes handy in saving lives with its ability to detect and predict shock. “Due to noise, thermal images are fuzzy and so it is difficult for the computer to identify body parts. So the machine-learning algorithm was trained to identify body parts, capture body surface temperature and calculate the temperature difference between abdomen and feet to detect and predict shock,” says Aditya Nagori from Institute of Genomics and Integrative Biology and first author of the paper. “The team is excited to launch a smartphone application which will incorporate the model capability to predict shock,” he adds. The researchers were able to use the machine-learning algorithm to detect difference in body temperature at AIIMS once the ICU with big data warehousing, the first of its kind in India, started functioning since February 2016. “Here data of every patient in the paediatric ICU at AIIMS is captured every second,” Dr. Sethi says.
