In clinical practice, it is sometimes difficult to establish whether a patient's tremor is due to Parkinson's disease or essential tremor. The distinction is crucial as the health implications differ significantly between the two conditions. Therefore, the present study aims to develop a diagnostic method based on machine learning techniques to help differentiate whether a patient's tremor is due to one condition or the other. To achieve this, 110 patients with tremor, correctly diagnosed with either Parkinson's disease or essential tremor, will participate. They will undergo two diagnostic tests (tapping test and Archimedean spiral) to capture data that can be processed using machine learning techniques.
Study Type
OBSERVATIONAL
Enrollment
110
For the administration of the tapping test, a computer application installed on a Tablet will be used. The test will be administered up to 3 times, with a 15-minute interval between each administration. Each tapping test trial will last 15 seconds. At the beginning of the session, the patient will perform 2 practice attempts.
For the administration of the Archimedean spiral, a computer application installed on a Tablet will be used. The Tablet screen will display a drawing of the spiral, serving as a reference for the participant. The test will be administered up to 3 times, with a 15-minute interval between each administration. At the beginning of the session, the patient will perform 2 practice attempts.
Hospital Sant Camil-Consorci Sanitari Alt'Pènedes i Garraf
Barcelona, Catalonia, Spain
RECRUITINGSensitivity
Proportion of participants with confirmed Parkinson's disease for whom the machine learning-based diagnostic algorithm yields a 'positive' result.
Time frame: through study completion, an average of 1 year
Specificity
Proportion of participants with confirmed essential tremor for whom the machine learning-based diagnostic algorithm yields a 'negative' result.
Time frame: through study completion, an average of 1 year
Reliability
The reliability of the diagnostic algorithm will be evaluated based on the repeatability of the classification result (positive or negative) among the different tests conducted on the same patient
Time frame: through study completion, an average of 1 year
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