This study presents an automated, objective method for eyelid movement assessment in de-novo Parkinson's disease(PD) using a one-dimensional camera setup during monologue. These measurements were related to Dopamine Transporter Single Photon Emission Tomography and clinical scores. State-of-the-art computer-vision technologies and deep-learning neural networks were utilized to measure fourteen eyelid movement markers describing blinking and eyelid kinematics. Video-recordings were collected from a total of 120 de-novo patients with PD and 55 healthy controls. Abnormal blinking was present in 38% of PD, indicated by a reduced blink rate (p < 0.001), an increased inter-blink interval (p < 0.001), and an increased rigidity of the palpebral aperture (p < 0.001). The classification experiment reached an area under the curve of 0.81 on a blinded test set. The blink rate correlated with the loss of nigral dopaminergic neurons (r = 0.35, p < 0.001). These findings suggest eyelid movement markers as strong reflections of striatal dopaminergic activity levels, underscoring the method's potential as a reliable early PD biomarker.

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• časopisecké články MeSH

BACKGROUND AND OBJECTIVE: An aging society requires easy-to-use approaches for diagnosis and monitoring of neurodegenerative disorders, such as Parkinson's disease (PD), so that clinicians can effectively adjust a treatment policy and improve patients' quality of life. Current methods of PD diagnosis and monitoring usually require the patients to come to a hospital, where they undergo several neurological and neuropsychological examinations. These examinations are usually time-consuming, expensive, and performed just a few times per year. Hence, this study explores the possibility of fusing computerized analysis of hypomimia and hypokinetic dysarthria (two motor symptoms manifested in the majority of PD patients) with the goal of proposing a new methodology of PD diagnosis that could be easily integrated into mHealth systems. METHODS: We enrolled 73 PD patients and 46 age- and gender-matched healthy controls, who performed several speech/voice tasks while recorded by a microphone and a camera. Acoustic signals were parametrized in the fields of phonation, articulation and prosody. Video recordings of a face were analyzed in terms of facial landmarks movement. Both modalities were consequently modeled by the XGBoost algorithm. RESULTS: The acoustic analysis enabled diagnosis of PD with 77% balanced accuracy, while in the case of the facial analysis, we observed 81% balanced accuracy. The fusion of both modalities increased the balanced accuracy to 83% (88% sensitivity and 78% specificity). The most informative speech exercise in the multimodality system turned out to be a tongue twister. Additionally, we identified muscle movements that are characteristic of hypomimia. CONCLUSIONS: The introduced methodology, which is based on the myriad of speech exercises likewise audio and video modality, allows for the detection of PD with an accuracy of up to 83%. The speech exercise - tongue twisters occurred to be the most valuable from the clinical point of view. Additionally, the clinical interpretation of the created models is illustrated. The presented computer-supported methodology could serve as an extra tool for neurologists in PD detection and the proposed potential solution of mHealth will facilitate the patient's and doctor's life.

• Klíčová slova
• Acoustic analysis, Facial analysis, Hypokinetic dysarthria, Hypomimia, Machine learning, Parkinson's disease,
• Publikační typ
• časopisecké články MeSH