DIGITAL AND AI-DERMATOSCOPIC MONITORING OF THE EFFICACY OF BIOLOGICAL THERAPY IN PSORIASIS
Feruza Jumanazar qizi Boymurodova
3rd-year student, Faculty of Medicine
Karshi State University
ABSTRACT
This thesis analyzes the efficacy of applying AI-dermatoscopy and digital monitoring systems in the treatment of psoriasis with biological agents (monoclonal antibodies). Due to the subjective nature of traditional clinical assessment methods (such as the PASI index), the necessity of utilizing digital technologies to detect microvascular and vascular changes in the skin at an early stage is highlighted. The study results demonstrated that AI-dermatoscopic analysis and digital monitoring allow for an objective evaluation of therapy efficacy 2 weeks earlier than visual signs appear, enabling timely adjustment of the treatment regimen and accurate prediction of the remission period.
Keywords: Psoriasis, medications, AI-dermatoscopy, biological therapy, antibodies, digital monitoring.
INTRODUCTION
Relevance of the topic. In recent years, the use of biological therapy—specifically monoclonal antibodies (IL-17 and IL-23 inhibitors)—has represented a major breakthrough in dermatovenerology for the treatment of severe and treatment-resistant forms of psoriasis. These targeted drugs selectively act on the immunopathogenetic cascade of the disease, demonstrating high clinical and laboratory efficacy. While the introduction of biological agents (monoclonal antibodies) plays a crucial role in modern medicine, assessing treatment efficacy with the naked eye does not always yield accurate results. The routinely used PASI index remains subjective and largely dependent on the specialist’s experience. To detect microscopic changes in the skin layers and positive dynamics during the initial weeks of treatment, there is a growing need to utilize dermatoscopy based on modern digital technologies and artificial intelligence (AI) algorithms.
Objective of the study: To demonstrate the accuracy and efficacy of evaluating skin recovery dynamics via AI-dermatoscopy and digital monitoring in psoriasis patients receiving biological therapy (monoclonal antibodies), thereby improving overall treatment outcomes.
Tasks of the study:
To analyze dermatoscopic parameters of the skin in psoriasis patients undergoing monoclonal antibody therapy using an AI neural network.To identify the correlation between AI-dermatoscopic parameters and the traditional PASI index.To evaluate the practical significance of digital monitoring systems in improving treatment compliance and predicting remission periods.
MATERIALS AND METHODS
The study included 30 patients (18 males, 12 females; aged 18 to 65 years, mean age: 42.5 ± 8.3 years) diagnosed with moderate-to-severe plaque psoriasis receiving biological therapy (IL-17 and IL-23 inhibitors). Affected skin areas were evaluated using a digital dermatoscope at baseline (week 0) and at weeks 2, 4, and 12 of treatment. Images capturing erythema, vascular pattern, and plaque thickness were analyzed using a dedicated AI neural network, complemented by a home-based digital monitoring application.The acquired images (erythema degree, vascular structure, and layer thickness) were analyzed using a specialized AI neural network. Additionally, patients were enrolled in a mobile application monitoring system to capture and send general skin condition images from home.
RESULTS AND DISCUSSION
AI-assisted dermatoscopic analysis demonstrated early vascular reduction and subclinical resolution of inflammatory features as early as Week 2 in 86.7% (n = 26) of patients, significantly preceding visible clinical improvement. The AI-based quantitative metrics showed a strong positive correlation with traditional PASI scores (r = 0.84, p < 0.001), providing objective, high-sensitivity detection of early therapeutic response and optimizing patient compliance.The developed digital and AI-monitoring approach enables physicians to make timely adjustments to treatment tactics, prevent complications, and achieve medical and social cost-effectiveness by reducing inpatient hospital visits. Furthermore, the digital monitoring system improved patient treatment compliance by reducing the need for frequent clinic visits.
CONCLUSION
The implementation of AI-dermatoscopy and digital monitoring systems in psoriasis patients receiving biological therapy (monoclonal antibodies) provides an objective and precise evaluation of treatment efficacy. The results confirmed that analyzing dermatoscopic images via an artificial intelligence neural network possesses higher sensitivity compared to traditional visual examination and the PASI index. It is recommended to integrate AI-dermatoscopic scanning into clinical practice at dermatovenerology clinics and specialized centers as a mandatory monitoring criterion for patients undergoing biological therapy.
REFERENCES
3.Diagnosis of skin diseases through AI programs in dermatology. Journal of Science and Technology.
4. A. (2009). Skin and Venereal Diseases. Aripov, S. S. (2010). Dermatology Textbook.
Feruza Boymurodova Jumanazar qizi was born on May 22, 2005, in the Kitob district of the Qashqadaryo region. She is currently a student at the Faculty of Medicine at Qarshi State University, actively conducting research at the intersection of medicine and social projects.
She has participated in the “AI Girls Bootcamp” startup program in Tashkent, as well as the “RISE & GLOW” and “Kelajak” platforms. She has served as a volunteer in the “Salomatlik”, “Vitamed”, and “Oltin Qanot” organizations, and took part in the “Eng yaxshi targ‘ibotchi” (Best Promoter) competition. On an international level, she has been accepted into the Argentine Writers’ Union, and her scientific articles have been published in international collections.
In her submitted article, as a future physician and researcher, she places a strong emphasis on strengthening public health, advancing medical knowledge, and addressing pressing issues in practical medicine.