Parkinsons disease (PD) is a neurological syndrome or condition that occurs due to a deficit of dopamine-producing neurons in the substantia nigra. Diagnosing PD in its early stages is difficult, as its symptoms often resemble those of other neurological diseases. Therefore, recognizing reliable biomarkers is important for discriminating PD from related conditions, monitoring disease progression, and evaluating responses to therapeutic interventions. PD biomarkers are categorized into the following classes: clinical, neuroimaging, biochemical and proteomic, and genetic. Ongoing research aims to discover the most effective PD biomarkers that could help doctors identify PD risk and accelerate early diagnosis. Artificial intelligence (AI) methods, including deep learning and machine learning, have become increasingly significant in recent years due to their ability to evaluate and process large volumes of medical data with high accuracy. Furthermore, these methods have contributed significantly to the early diagnosis and effective treatment of various diseases, such as cancer and neurological conditions, such as Alzheimers disease, PD, and multiple sclerosis. Given that PD affects a large population, the present study aims to review the applications of AI approaches in the early diagnosis of PD and the latest advancements in the field of PD biomarkers. Promising results have been obtained using various AI algorithms, which are helpful not only in identifying the PD stages but also in supporting early diagnosis. However, the implementation of these techniques in clinical practice faces challenges, including data quality and variability, model interpretability, and the need for interdisciplinary collaboration.
Goswami et al. (Tue,) studied this question.