## 1 Introduction  
Cerebral Palsy (CP) is a neurological disorder characterized by sensorimotor movement issues, affecting posture and movement control [1]. It encompasses various motor impairments that result in permanent, yet modifiable, dysfunctions. With an incidence of 2-3 per 1000 live births, CP is one of the leading causes of physical disabilities in children globally [2]. Early therapeutic interventions during childhood development are crucial for minimizing impairments and preventing secondary complications [3]. Lifelong therapy is necessary to reduce the impact of these impairments and to improve quality of life. This therapy should be regularly evaluated and adjusted to align with individual therapeutic goals [4]. Therapy planning should be based on diagnoses and resources, considering feasibility and cost-effectiveness [5]. Regular motor assessments, such as the Gross Motor Function Measurement (GMFM), are vital for tracking progress and refining therapy strategies. The focus of targeted therapy is on promoting independence and social participation. In order to be able to adequately assess the effectiveness of the therapy, it is essential to discuss the individual therapy goals beforehand [4]. The GMFM-66 (GMFM-66) is recognized as the reference standard for assessing motor abilities in children with CP due to its high reliability and validity [6]. It is a shortened version of the GMFM-88 with 88 items, derived through Rasch analysis to reduce the number of items while preserving the core measurement properties and clinical interpretability. The GMFM-66 includes 66 items and is specifically developed and standardized for children with CP. The administration of this assessment typically takes 45 to 60 min [7]. During the assessment, children's motor abilities are evaluated across five dimensions (lying and rolling; sitting; crawling and kneeling; standing; walking, running, and jumping), and a total score is calculated using specialized scoring software [6]. This score reflects the children's motor abilities and can be used both to evaluate progress in motor function and to measure the success of therapeutic intervention [8]. In recent years, digital innovations such as tablet-based motion capture systems, wearable inertial sensors, and computer vision-based scoring techniques have been explored to further reduce clinician workload and enhance objectivity in motor assessments [9]. These technologies aim to complement traditional assessments like the GMFM-66 while preserving clinical relevance and ensuring efficient data collection in various settings. The use of AI in healthcare, particularly for the treatment of children with developmental and neuro-motor conditions, is becoming increasingly common, with AI technologies supporting diagnostics and assessments [9]. The integration of AI-based systems into motor assessments reduces the workload for healthcare professionals and helps minimize the duration and intensity of testing procedures [10]. This is particularly beneficial for children with CP, who exhibit significantly greater fatigue during submaximal activities compared to typically developing peers. This increased fatigue has been attributed to a reduced ability to develop neurophysiological compensation mechanisms [11]. This approach not only enhances the precision of diagnostics but also contributes to more efficient resource allocation in the management of conditions like CP [12]. In this context, in order to increase the efficiency of the application of the GMFM-66, the reduced GMFM-66 (rGMFM-66) was developed. The rGMFM-66 utilizes AI technology to reduce the number of test items required. A previous study based on retrospective data demonstrated that the rGMFM-66 can predict the GMFM-66 very precisely in a study population of 365 children with CP (mean age: 8 years 9 months, SD: 3 years 10 months), with an intraclass correlation coefficient (ICC) of 0.997 (95% CI: 0.996-0.997). An average of only 34 test items was required to achieve these results.