Parkinson’s disease (PD) is one of the fastest‐growing neurodegenerative disorders, where timely diagnosis is essential for optimizing treatment. In this study, we created a radiomics–MDS‐UPDRS, a robust dataset by integrating DaTscan SPECT radiomics data with the clinical characteristics of MDS‐UPDRS collected from Parkinson’s progression markers initiative (PPMI) to monitor dopamine depletion in the striatum (caudate and putamen) and allow classification and progression analysis of PD. To construct the dataset, the striatum was segmented using a modified K‐means clustering algorithm, extracting 25 radiomics features combined with 59 clinical features. In addition, linear discriminant analysis was used to select 22 significant characteristics, and a four‐way feature selection method was used to identify 30 significant clinical features, resulting in a refined set of 52. Classification with machine learning models improved performance after LDA, achieving over 91% accuracy. We evaluated feature behavior across six PD severity stages and four clinical visits for progression analysis. The clinical features of MDS‐UPDRS were more sensitive to changes in the severity of the initial PD. At the same time, the integrated dataset, radiomics–MDS‐UPDRS, provided more balanced insights, showing a progression of 33.30%–83.30% and 36.36%–45.50% from the first visit to the fourth visit among the clinical and radiomics features and a progression of 73.33%–96.67% and 13.64%–54.55% between the minimal vs mild and minimal vs very severe stage. Our analysis also revealed practical links between progression features and real‐life scenarios, which highlights the practical value of our study for clinical decision‐making.

Unmanned aerial vehicles (UAVs) have been employed for a variety of inspection and monitoring tasks, including agricultural applications and search and rescue (SAR) in remote areas. However, traditional monitoring methods tend to focus on optimizing one aspect. This study aims to propose a complete framework by integrating advanced methods to provide a robust and accurate path coverage solution. The combination of edge detection and area decomposition with a pathfinding algorithm can improve the overall performance. An effective edge detection model is developed that simultaneously detects the boundary and segments the area of interest (AOI) from the aerial land images and provides precise area mapping of the area. An intuitive grid decomposition with grid‐to‐graph mapping improves the flexibility of the area decomposition and ensures maximal coverage and safe operation routes for the UAVs. Finally, a robust modified simulated annealing (MSA) algorithm is introduced to determine the shortest path coverage route. The performance of the proposed methodology is tested on aerial imagery. Area decomposition ensures that there are no gaps in the AOI during the coverage planning. The MSA algorithm obtains the minimum length cost, charge consumption cost, and minimum number of turns to cover the area. It is shown that the integration of these techniques enhances the performance of the coverage path planning (CPP). A comparison of the proposed approach with benchmark algorithms further demonstrates its effectiveness. This study contributes to creating a complete CPP application for UAVs, which may assist with precision agriculture as well as safe and secure rescue operations.