V. G. Nikitaev1, A. N. Pronichev2, O.V. Nagornov3, V. Yu. Sergeev4, A. V. Kozyreva5

1–3 National Research Nuclear University "MEPhI" (Moscow, Russia)
4 Federal State Budgetary Institution of Continuing Professional Education «Central State Medical Academy (Moscow, Russia)
5 National Technical Physics and Automation Research Institute (Moscow, Russia)
1 vgnikitayev@mephi.ru, 2 anpronichev@mephi.ru, 3 ovnagornov@mephi.ru, 4 vasyur@yandex.ru, 5 a.v.kozyreva2015@gmail.com

Network structures are one of the important sources of diagnostic information in dermatoscopy. The problem is to identify the network structure on the dermatoscopic image. Difficulties are caused by the complexity of images (up to 16 million color shades with 24-bit color coding), the great variability of the object environment, the lack of highly qualified diagnosticians.

Purpose of the work – development of an interdisciplinary model of the pigment network to form a reference knowledge base in devices for diagnosing skin diseases based on the analysis of dermatoscopic images.

A scientific substantiation of the interdisciplinary model of the pigment network has been carried out. The proposed model allows for a quantitative description of the pigment network, translating mathematical, biological, and clinical criteria of typicality and atypicality into digital criteria suitable for use in automated diagnostic devices based on artificial intelligence.

The proposed model of the pigment network is applicable for the formation of a reference knowledge base in dermatological diagnostic devices, which contributes to the improvement of the accuracy and reproducibility of diagnostic decisions. In the development of this article, it is planned to integrate the developed interdisciplinary model of the pigment network into a unified decision support system in dermatology in digital dermatology devices.

Nikitaev V.G., Pronichev A.N., Nagornov O.V., Sergeev V.Yu., Kozyreva A.V. Interdisciplinary model of pigment network on dermatoscopic images in skin disease diagnostic devices. Biomedicine Radioengineering. 2025. V. 28. № 5. P. 14–17. DOI: https://doi.org/ 10.18127/j15604136-202505-03 (In Russian)

1. De Pinto G. et al. Global trends in cutaneous malignant melanoma incidence and mortality. Melanoma Research. 2024. V. 34. № 3. P. 265–275.
2. Jakhar D., Kaur I. Dermatoscopy: What Does It Add? Critical Thinking in Contemporary Dermatology: Cognitive Essays. Singapore: Springer Nature Singapore. 2024. P. 87–105.
3. Soglia S. et al. Understanding the anatomy of dermoscopy of melanocytic skin tumours: correlation in vivo with line‐field optical coherence tomography. Journal of the European Academy of Dermatology and Venereology. 2024. V. 38. № 6. P. 1191–1201.
4. Nikitaev V.G., Pronichev A.N., Nagornov O.V., Sergeev V.Yu., Kruglova L.S., Otchenashenko A.I., Deeva O.K. The method for evaluating the symmetry of the globule pattern in artificial intelligence systems for the diagnosis of skin neoplasms. Measurement Techniques. 2024. V. 67. № 9. P. 706–714.
5. Davydov M.I., Sel’chuk V.Y., Nikitaev V.G., Nagornov O.V., Pronichev A.N., Dmitrieva V.V., Polyakov E.V., Rasulov A.O., Kononets V.P., Melikhov S.A., Akimov I.S., Aidunbekov Z.M., Kadashev V.I., Lavrova A.A., Golovanova V.K., Pashnuk A.A., Strigin V.E. Physical research methods in expert systems of oncological disease diagnostics. Bulletin of the Lebedev Physics Institute. 2015. V. 42 (8). P. 237–239.
6. Nikitaev V.G., Nagornov O.V., Pronichev A.N., Polyakov E.V., Dmitrieva V.V. The blood smear image processing for the acute leukemia diagnostics. International Journal of Biology and Biomedical Engineering. 2016. V. 10. P. 109–114.
7. Ma D. et al. Implementation of computer vision technology based on artificial intelligence for medical image analysis. International Journal of Computer Science and Information Technology. 2023. V. 1. № 1. P. 69–76.