A Framework for Integrating Digital Imaging in Psoriatic Disease Management

Thesis Abstract

Psoriatic disease management faces significant challenges due to the variability in clinical presentation, subjective assessment, and limited quantification methods, which often hinder accurate monitoring and personalized treatment strategies. Despite advances in digital imaging technologies, integration into routine clinical practice remains inconsistent, primarily due to the lack of comprehensive frameworks that facilitate systematic application, interpretation, and standardization of imaging data. This study aims to develop a robust framework for integrating digital imaging into the management of psoriatic disease, thereby enhancing diagnostic accuracy, disease monitoring, and treatment decision-making processes. The specific objectives include identifying critical digital imaging modalities relevant to psoriatic disease, analyzing existing clinical workflows incorporating imaging, proposing an integrative framework aligned with dermatological diagnostic criteria, and evaluating its potential impact on clinical outcomes. The research adopts a mixed-methods approach, combining qualitative exploration of clinician perspectives with quantitative analysis of imaging data. The qualitative component involves semi-structured interviews with 30 dermatologists and rheumatologists from tertiary healthcare centers, aimed at understanding current practices, barriers, and facilitators concerning digital imaging adoption. The quantitative component entails a cross-sectional study of 200 psoriatic patients, with digital images captured using high-resolution dermatoscopy and computer-assisted image analysis software. Data collection instruments include interview guides, standardized imaging protocols, and digital measurement tools. Content analysis and thematic coding are applied to qualitative data, guided by the Theory of Planned Behavior and the Technology Acceptance Model to elucidate factors influencing technology integration. Quantitative data undergo statistical analysis through descriptive statistics, Pearson correlation coefficients, and multiple regression analysis, aiming to determine the relationship between imaging parameters and clinical severity scores such as the Psoriasis Area and Severity Index (PASI). Advanced image processing techniques, including machine learning algorithms, are employed to extract quantifiable disease features, facilitating objective assessment. The anticipated results are the identification of key imaging parameters most predictive of disease activity, clinicians’ perceived barriers to digital imaging integration, and the development of a theoretical framework that synthesizes clinical, technological, and behavioral factors. It is expected that the proposed framework will demonstrate improved consistency in disease assessment, greater diagnostic confidence, and enhanced capacity for longitudinal monitoring, supported by statistically significant correlations between image-derived metrics and clinical severity indices. The integration of digital imaging is projected to reduce inter-observer variability and support personalized treatment approaches. This research substantially contributes to the evolving field of dermatological digital health by providing an evidence-based, systematic framework for incorporating digital imaging into routine psoriatic disease management. It advances understanding of behavioral and technological factors influencing adoption and offers a model for broader clinical application. Furthermore, the study bridges the gap between technological capabilities and clinical utility, promoting standardized imaging practices and data-driven decision-making. In conclusion, the study advocates for the systematic adoption of digital imaging technologies in psoriasis care to improve diagnostic precision, treatment efficacy, and patient outcomes. It recommends integrating the framework into clinical protocols, training programs to enhance clinician competency, and ongoing evaluation of technology performance. Future research should explore longitudinal validation of the framework in diverse populations and investigate its integration with emerging digital health tools such as teledermatology and artificial intelligence-based diagnostic systems, thereby fostering continuous enhancement in psoriasis management paradigms.