ArticleFrontiers in neurology
AI-based retrospective analysis: differential improvement profiles of medication and deep brain stimulation in Parkinson's disease.
Article in Frontiers in neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Bradykinesia in Parkinson's disease (PD) involves reduced movement speed, amplitude, and rhythmicity. While the MDS-UPDRS Part III is the standard clinical tool for motor assessment, it has limited sensitivity to specific kinematic features. Levodopa and subthalamic nucleus deep brain stimulation (STN-DBS) are common treatments for PD, yet their differential effects across motor domains are not fully characterized. This study applies AI-based video analysis to evaluate the effects of levodopa and STN-DBS on limb bradykinesia. Methods: This retrospective study assessed fifty-three patients with Parkinson's disease undergoing STN-DBS. Motor performance was video-recorded during Levodopa-off and Levodopa-on states (levodopa challenge test performed prior to surgery), as well as after DBS activation (OFF Results: Conventional UPDRS-III item scores suggested that levodopa was more effective than DBS in improving upper-limb tasks (items 3.4 Finger Tapping and 3.5 Fist-clenching test), while lower-limb tasks (items 3.7 Toe Tapping and 3.8 Leg Agility) showed no significant changes. In contrast, AI-based kinematic analysis revealed more differentiated treatment effects. Levodopa was associated with improvements in movement speed, amplitude, and stability in the upper limbs, as well as a significant impact on lower-limb amplitude, both in toe tapping (item 3.7) and leg agility (item 3.8). DBS, by comparison, enhanced upper-limb motor output but had limited effects on the lower limbs, with improvements in speed and amplitude observed only in the toe tapping (item 3.7) task. Additionally, levodopa demonstrated superior improvements in lower-limb amplitude, both in toe tapping (item 3.7) and leg agility (item 3.8), compared to DBS. Conclusion: This study demonstrates that AI-based kinematic analysis enables a nuanced and individualized characterization of motor responses to medication and STN-DBS in Parkinson's disease, complementing conventional clinical scoring. Although both therapies improve bradykinesia, they appear to preferentially modulate distinct motor domains across individuals, underscoring their complementary roles in treatment. These findings highlight the potential of AI-based motor assessment to support personalized symptom profiling and more individualized therapeutic decision-making in Parkinson's disease.
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