An artificial intelligence can detect diarrhoea with up to 98 per cent accuracy by analysing the sounds emanating from toilets. This skill could help us track outbreaks of diseases such as cholera. Maia Gatlin at the Georgia Institute of Technology and her colleagues collected 350 recordings of toilet-based sounds from YouTube and sound database Soundsnap – covering standard defecation, diarrhoea, urination and flatulence. The researchers then used 70 per cent of the recordings to train an AI to recognise audible differences between the four types of excretion. Once they confirmed that the AI could consistently do this with another 10 per cent of the data, they tested the AI's performance using the last 20 per cent of the recordings.

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Image has been cropped and resized. Scientists have developed machine-learning algorithms that can identify patterns in the bacteria of a patient's gut to determine whether the patient is likely to get infected if exposed to cholera. The researchers believe such artificial intelligence (AI) could be critical in areas of high cholera risk, since it can analyze trillions of bacteria, much more than can be done by humans. The study also demonstrates the power of machine learning to uncover medical insights that would otherwise remain obscure. READ: AI's Ethical Concerns Go Beyond Data Security and Quality The research is a collaboration between Duke University, Massachusetts General Hospital, and the International Centre for Diarrheal Disease Research, in Bangladesh.

These are patterns that even the most sophisticated scientist couldn't detect by eye,