Authors:
Francesco Mercaldo
1
;
2
;
Luca Brunese
2
;
Mario Cesarelli
3
;
Fabio Martinelli
1
and
Antonella Santone
2
Affiliations:
1
Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy
;
2
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy
;
3
Department of Engineering, University of Sannio, Benevento, Italy
Keyword(s):
Parkinson, Spiral, Machine Learning, Deep Learning, Explainability.
Abstract:
There is no definitive test for Parkinson’s disease, and the rate of misdiagnosis, particularly when made by individuals without specialized training, is significantly elevated. The spiral drawing test is a clinical assessment tool used to evaluate fine motor skills, hand-eye coordination, and tremor in individuals, particularly those with neurological disorders such as Parkinson’s disease. In this test, a person is typically asked to trace or draw a spiral pattern on a piece of paper or a digital tablet. The test measures the smoothness and steadiness of their hand movements. Any irregularities or tremors in the drawn spiral can provide valuable information to healthcare professionals in diagnosing or monitoring conditions like Parkinson’s disease, essential tremors, or other movement disorders. In this paper, we provide a method aimed at automatically analyse spiral drawing tests to understand whether a subject is affected by Parkinson’s disease. We employ two different Convolu-tio
nal Neural Networks: DenseNet and ResNet50, by obtaining an accuracy equal to 0.96 in the evaluation of a dataset composed of 3,991 spiral drawing tests, thus showing the effectiveness of the proposed method. Moreover, with the aim to provide a kind of explainability behind the model prediction, the proposed method is able to visualise, directly on the spiral drawing test image, the areas of the test image that from the model point of view are related to Parkinson’s disease.
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