Scientists from UNSW Sydney with collaborators at Boston University have developed a tool that shows early promise in detecting Parkinson’s disease years before the first symptoms start appearing.

Using deep learning techniques, the team achieved 92.8 percent sensitivity and 86.2 percent specificity for Parkinson’s Disease detection. Not only is their proposed framework performing well, but it ...

New research has used a specially-designed machine learning algorithm to identify unique combinations of metabolites that may be able to detect Parkinson’s disease early ...

Identifying Parkinson’s Disease early is crucial for slowing the disease progression and a new tool developed by Khalifa University can now detect the disease using sensors on the average smartphone.

Harnessing the strength of AI, researchers illuminate distinct Parkinson's disease types, successfully forecasting subtypes through patient stem cell imagery.

Second, this machine learning approach enabled us to identify chemical markers that are the most important in accurately predicting who will develop Parkinson's disease in the future.

Researchers at the Australian National University (ANU) will use machine learning to help solve the mystery of how to determine the progression of Parkinson's disease.

The mystery of how Parkinson’s disease progresses could be cracked thanks to researchers at the Australian National University (ANU) and machine learning.