Advancing Parkinson’s Disease Detection: A Review of AI and Deep Learning Innovations

Neurodegenerative in nature, Parkinson's disease primarily impacts motor and non-motor processes with resultant significant detriments to the quality of life. In fact, for this reason, an early diagnosis becomes imperative—the appropriate application of intervention treatment often hampers an already conventional type of diagnosis. Thereby, clinical diagnosis remains mostly subjective. Advances in some state-of-the-art computational algorithms open new pathways offering noninvasive scalable solutions toward early detection. These approaches employ manifold modalities, ranging from voice analysis to wearable sensors, and hybrid machine learning frameworks, with diagnosis accuracy going as high as 97% in some cases. The major contributions are: Enhanced Smallest Normalized Difference Associative Memory, advanced nonlinear speech analysis techniques, telemonitoring systems that enable the remote monitoring of symptoms, and hybrid feature selection models such as HEFS-MLDR, which integrates dimensionality reduction with ensemble learning methodologies. While these are promising developments, a number of open challenges remain: dependence on high-quality data, generalizability across diverse populations is limited, and hybrid models are computationally intractable. Recent studies also highlighted that more work is needed to be done on developing better integration of multimodal data, more interpretable AI models, and clinical validation across diverse populations for broad generalizability. On the other hand, notwithstanding the mentioned difficulties, advances in computational methods have underlined the potential of these approaches for further improvement in diagnostic systems toward more accessible, more accurate, and efficient early diagnosis of Parkinson's disease.