Early detection of a disease empowers medical professionals to initiate interventions at a stage when treatment can be most effective, enhancing the well-being of patients. Unfortunately, Parkinson's disease (PD) remains notorious for its difficult detection in preliminary stages, resulting in delayed treatment and poor patient outcomes. Gait analysis along with machine learning plays a critical role in the early and accurate diagnosis of PD, revolutionizing how we detect and manage this disorder. Machine learning algorithms, when fed with vast amounts of gait data, can effectively learn to detect patterns indicative of PD with accuracy. These algorithms can analyze subtle gait features that clinicians may find difficult to recognize, resulting in more trustworthy and objective judgements. Therefore, in this chapter, the authors delve into the critical significance of machine learning in early identification of de novo Parkinson's disease by utilizing gait analysis as well as parameter selection for smooth algorithm performance.
TopIntroduction
Within the vast landscape of medical research, certain challenges beckon researchers to embark on transformative journeys. Parkinson's disease, a complex neurodegenerative disorder, stands as a poignant emblem of such an expedition, demanding profound insights and innovative solutions. As the second-most prevalent neurodegenerative ailment, Parkinson's disease (PD) unveils a narrative of progressively deteriorating motor abilities over time, rendering it a prominent contributor to functional disabilities on a global scale (Poewe et.al, 2017; GBD Neurological Disorders Collaborator Group, 2017). It is a disorder of basal ganglia in which the dopaminergic tract is compromised, demonstrated by dopaminergic neuron loss in the substantia nigra Gaillard et.al (2017). The defining symptoms include bradykinesia, resting tremor, and/or rigidity, typically manifesting in later life (DeMaagd and Philip, 2015). Gait issues, such as freezing of gait, difficulty in gait initiation and diminished balance and postural control become more noticeable in the middle and advanced stages.
Clinical examination is the premier method for diagnosing and tracking PD symptoms, but it contains a number of completely subjective components, the reason being a huge dearth of objective and quantifiable indicators. This void in the diagnostic realm contributes to substantial direct and indirect healthcare expenditures. In typical clinical practise, diagnostic error rates of Parkinson’s Disease range from 15% to 24% (Rajput and Rajput, 2014; Schrag et.al, 2002; Hughes et.al, 1992). Non-Parkinsonian's disease tremor disorders like essential tremor and various forms of secondary parkinsonism are frequent causes of errors in therapeutic practise (Tolosa et.al, 2021).
The footprint of Parkinson's disease extends beyond its neurological origins, leaving its mark on the intricate art of locomotion. Gait/Locomotion issues associated with Parkinson's disease account for a significant share of functional impairments. These are progressive in nature with varying trends of gait disturbances and include decreased arm swing amplitude, decreased smoothness of locomotion, increased inter-limb asymmetry, slowed pace, diminished step length, shambled steps, raised double-limb assistance, raised cadence, defragmentation of turns, issues with initiating gait, freezing during gait and decreased postural control and balance (Pistacchi et.al, 2017; Mirelman et.al, 2019; Son et.al, 2017). Such deviations in Parkinson’s Disease are widely evaluated using semi-quantitative rating scales and questionnaires like Movement Disorders Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Tinetti Gait and Balance Assessment, and Unified Parkinson’s Disease Rating Scale (UPDRS) (Goetz et. Al, 2007; Scura and Munakami, 2023; Fahn and Elton, 1987). With these instruments, clinicians and researchers alike strive to capture the nuances that embody the gait of Parkinson's disease, seeking to unlock a more comprehensive understanding of this disease.
In pursuit of enhanced patient management and well-being, a precise quantitative analysis of gait patterns in Parkinson’s Disease emerges crucial to its successful management and well-being of the patients. A precise quantitative analysis of gait patterns becomes more than a diagnostic tool—it becomes a window into the world of movement, an avenue to discern the subtleties that often remain hidden within the complexity of the disease. Therefore, in an era where data meets compassion, and technology meets compassion, the development of accurate diagnostic tools occupies a paramount role in the agendas of developers and researchers across disciplines, as they strive to illuminate new avenues for understanding and addressing the complexities of Parkinson's disease.