The use of a computer program in the diagnosis of chronic rhinosinusitis

Purpose of research is to develop a computer program to improve the diagnosis and monitoring of treatment of pa- tients with chronic rhinosinusitis.  
Methods. The program was developed on the basis of multifactorial and correlation analyses, taking into account a combination of indicators of invasive laboratory diagnostic methods. Laboratory diagnostics is performed for biomarkers of endothelial dysfunction in peripheral blood samples of patients (homocysteine, cystatin C, highly sensitive C-reactive protein  (hsCRB),  D-dimer).  The  certificate  of  state  registration  of  the  computer  program  No.  2021611887  dated 02/08/2021 was received.  
Results. The program is based on protocols of clinical and laboratory examination of patients with chronic rhinosinusitis. The program operates according to the following stages: data input; mathematical calculation using a mathematical model; output of a digital result of the value "y"; generation of a report with the output of the result on the screen. The program has such qualities as visibility and ease of use. Clinical cases are given as examples of the program's work. The operation of the program is demonstrated in the form of an interface image.  
Conclusion. The developed software can be recommended for use in clinical practice. In the pre- and postoperative periods, with the help of this program, it is possible to conduct treatment monitoring using objective standardized highly sensitive clinical and laboratory research methods. The implementation of the software package in practice will help to implement an integrated approach in the diagnosis of CRS. The presented program is an innovative tool for the diagnosis and evaluation of the effectiveness of treatment in patients with CRS. The developed program can be recommended  for use  in  the  routine practice of an otorhinolaryngologist  in outpatient and hospital settings of healthcare institutions. 

1. Krivopalov A.A., Moroz N.V., Artyushkin S.A., Shamkina P.A., Zakharova G.P. Assessment of the prevalence of chronic rhinosinusitis. Rossiiskaya otorinolaringologiya = Rus-sian Otorhinolaryngology. 2022;(21):91–98. (In Russ.) https://doi.org/10.18692/1810-4800-2022-5-91-98

2. Gurov A.V., Karpishchenko S.A., Karpova E.P., et al. Chronic rhinosinusitis. practical recommendations. Farmakologiya i farmakoterapiya. Spetsvypusk = Pharmacology and Pharmacotherapy. Special Issue. 2024. P. 54–68. (In Russ.) https://doi.org/10.46393/27132129_2024_1_56

3. Lazareva A.M., Smirnova O.V. Immunological features of various phenotypes of chronic rhinosinusitis. Meditsinskaya immunologiya = Medical Immunology. 2025;27(2):275-286. (In Russ.) https://doi.org/10.15789/1563-0625-IFO-3026

4. Kochetkov P.A., Svistushkin V.M., Schennikova E.S. The use of intranasal glucocorticosteroids in the complex treatment of patients with chronic diseases of the nose and paranasal sinuses. Meditsinskii sovet = Medical Council. 2020;(6):66–70. (In Russ.) https://doi.org/10.21518/2079-701X-2020-6-66-70

5. Fokkens W.J., Lund V.J., Hopkins C., et al. European position paper on rhinosinusitis and nasal polyps. Rhinology. 2020;(58):1–464.

6. Vasina L.V., Petrishchev N.N., Vlasov T.D. Endothelial dysfunction and its causes the main markers. Regionarnoe krovoobrashchenie i mikrotsirkulyatsiya = Regional Blood Cir-culation and Microcirculation. 2017;(1):4–15. (In Russ.)

7. Abduganieva Sh. Kh., Nikonorova M. L. Digital solutions in medicine. Krymskii zhur-nal eksperimental'noi i klinicheskoi meditsiny = Crimean Journal of Experimental and Clini-cal Medicine. 2022;12(2):73–85. (In Russ.) https://doi.org/10.29039/2224-6444-2022-12-2-73-85

8. Gusev A.V., Zarubina T.V. Support for medical decision-making in medical information systems of a medical organization. Vrach i informatsionnye tekhnologii = Doctor and Information Technologies. 2017;(2):60–72. (In Russ.)

9. Luchinin A.S. Artificial intelligence in hematology. Klinicheskaya onkogema-tologiya = Clinical Oncology and Hematology. 2022;(15):16–27. (In Russ.) https://doi.org/10.21320/2500-2139-2022-15-1-16-27

10. Kobrinsky B.A. Medical decision support systems in advanced training: history and current trends. Metodologiya i tekhnologiya nepreryvnogo professional'nogo obrazovaniya = Methodology and Technology of Continuing Professional Education. 2020;(4):21–37. (In Russ.) https://doi.org / 10.24075/MTCPE.2020.022

11. Fayzullin T.R., Aleksanyan T.A. Clinical experience in using computer software to predict the risks of complications during aesthetic breast surgery. Plasticheskaya khirurgiya i esteticheskaya meditsina = Plastic Surgery and Aesthetic Medicine. 2023;(4):23–26. (In Russ.) https://doi.org/10.17116/plast.hirurgia202304123

12. Kuzminov O.M., Fetisova V.I., Fetisov I.A. An information model for analyzing the quality of medical care for a specific patient for the didactic provision of educational programs. Nauchnyi rezul'tat. Meditsina i farmatsiya = Scientific Result. Medicine and Pharmacy. 2017;3(1):31–41. (In Russ.) https://doi.org/10.18413/2313-8955-2017-3-1-31-41

13. Benjamens S., Dhunnoo P., Mesko B. The state of artifi cial intelligence-based FDAapproved medical devices and algorithms: an online database. N. P. J. Digit. Med. 2020;(3):118. https://doi.org/10.1038/s41746-020-00324-0

14. Uvalieva I. Architectural and algorithmic model of an intelligent clinical decision support system. In: Ural'skii simpozium po biomeditsinskoi inzhenerii, radioelektronike i informatsionnym tekhnologiyam (USBEREIT) = Ural Symposium on Biomedical Engineering, Radio Electronics and Information Technologies (USBEREIT), 2020. P. 180–183. (In Russ.) https://doi.org/10.1109/USBEREIT48449.2020.9117801

15. Demushkina K.M., Demushkin M.O., Kuzmin A.V. Review of design methods for medical decision support systems. Modeli, sistemy, seti v ekonomike, tekhnike, prirode i obshchestve = Models, Systems, Networks in Economics, Technology, Nature and Society. 2022;(2):75–89. (In Russ.) https://doi.org/10.21685/2227-8486-2022-2-6

16. Lebedev G.S., Fartushny E.N., Shaderkin I.A., et al. Creation of an information system to support medical decision-making based on evidence-based medicine methods. Zhurnal telemeditsiny i elektronnogo zdravookhraneniya = Journal of Telemedicine and Electronic Health. 2019;5(1):8–16. (In Russ.) https://doi.org/10.29188/2542-2413-2019-5-1-8-16

17. Rebrova O.Y. Effectiveness of medical decision support systems: methods and results of assessment. Klinicheskaya i eksperimental'naya tireoidologiya = Clinical and Experimental Thyroidology. 2019;15;(4):148–155. (In Russ.) https://doi.org/10.14341/ket12377

18. Potekaev N.N., Dolya O.V., Frigo N.V., et al. Artificial intelligence in medicine. General provisions. Philosophical aspects. Klinicheskaya dermatologiya i venerologiya = Clinical Dermatology and Venereology. 2022;(21):749–756. (In Russ.) https://doi.org/10.17116/klinderma202221061749

19. Knyazev E.G., Samchenko A.A., Ryumkin K.V. Formation of a system for supporting medical decision-making based on digitization of clinical recommendations using formal logic tools. Menedzhment kachestva v meditsine = Quality Management in Medicine. (In Russ.) 2019;(4):52–57.

20. Mamaev A.N., Kudlay D.A. Statistical methods in medicine. Moscow: Prakticheskaya meditsina; 2021. 136 p. (In Russ.)