Multimodal risk classifier for cardiorespiratory diseases taking into account concomitant diseases and synergy effect

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.
Methods. A method for classifying the contours of the boundaries of segments of a complexly structured image is described. The method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. The method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. To classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. The method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.
Results. Experimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. The purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". The training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. The quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.
Conclusion. The use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

1. Abramovich S.G., Ignat'eva T.G. Methods of Hardware Physiotherapy in the Treatment of Coronary Heart Disease. Sibirskiy meditsinskiy zhurnal = Siberian Medical Journal. 2003;37(2):4–9. (In Russ.)

2. Soboleva L.R., Kuzyaev A.I. Complications in Patients with Community-Acquired Pneumonia with Fatal Outcome. In: Mezhdunarodnyi kongress po boleznyam organov dyhaniya = International Congress for Respiratory Diseases. St. Petersburg; 1998. P. 24–26. (In Russ.)

3. Smirnova M.I., Kurekhyan A.S., Gorbunov V.M., Andreeva G.F., Koshelyaevskaya Y.N., Deev А.D. Prospective Follow-Up of Hypertensive Patients with Concomitant Chronic Respiratory Diseases in Routine Practice. Part I. Characterization of Adverse Events]. Kardiovaskulyarnaya terapiya i profilaktika = Cardiovascular Therapy and Prevention. 2022;21(10):3383. (In Russ.) https://doi.org/10.15829/1728-8800-2022-3383

4. Komlev I.A., Shatalova O.V., Degtyaryov S.V., Serebrovskiy A.V. Prediction and Assessment of Severity of Cardiac Ischemia, Based on Hybrid Fuzzy Models. Izvestiya Yugo Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2019;9(1):133–145. (In Russ.)

5. Kiselev A.V., Savinov D.Y., Filist S.A., Sрatalova O.V., Zрilin V.V. Virtual Flows in Hybrid Decision Modules of Classification of Complex-Structured Data. Prikaspiyskiy zhurnal: upravlenie i vysokie tekhnologii = The Caspian Journal: Management and High Technologies. 2018;2(42):137–149. (In Russ.)

6. Protasova Z.U., Shatalova O.V., Dafalla A.A.B., Degtyaryov S.V. Methods and Algorithms for the Formation of Weak Classifiers in the Ensemble of Classifiers for Predicting Cardiovascular Risk. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2019;9(3):64–83. (In Russ.)

7. Shatalova O.V., Mednikov D.A., Protasova Z.U. Mul'tiagentnaya intellektual'naya sistema dlya prognoza riska serdechno-sosudistyh oslozhneniy s sinergeticheskimi kanalami [Multi-Agent Intelligent System for Prediction of Risk of Cardiovascular Complications with Synergy Channels]. Sistemnyy analiz i upravlenie v biomeditsinskih sistemah = System Analysis and Management in Biomedical Systems. 2020;19(3):177–188. (In Russ.)

8. Kiselev A.V., Shatalova O.V., Protasova Z.U., Filist S.A., Stadnichenko N.S. Models of Latent Predictors in Intellectual Systems for Forecasting the State of Living Systems. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2020;10(1):114–133. (In Russ.)

9. Petrunina E.V., Shatalova O.V., Zabanov D.S., Serebrovskii V.V. Heterogeneous Classifiers with Virtual Flows in Intelligent Systems for Predicting Cardiovascular Complications during the Rehabilitation Period. Biomedical Engineering. 2020;54(3):212–215. https://doi.org/10.1007/s10527-020-10006-6

10. Shatalova O.V., Mednikov D.A., Protasova Z.U., Stadnichenko N.S. Prediction of the risk of cardiovascular complications with a segmented space of risk factors and synergy channels. In: Journal of Physics: Conference Series: II International scientific conference on applied physics, information technologies and engineering (APITECH II), 25 September – 04 October 2020. Krasnoyarsk: Institute of Physics and IOP Publishing Limited; 2020. Р. 32042. https://doi.org/10.1088/1742-6596/1679/3/032042

11. Emel'yanov S.G., Rybochkin A.F., Filist S.A., Haled A.R. Neural Network Solver Module for the Study of Living Systems. Izvestiya Kurskogo gosudarstvennogo tekhnicheskogo universiteta = Proceedings of Kursk State Technical University. 2008;(2):77–82. (In Russ.)

12. Petrunina E.V., Filist S.A., Shulga L.V., Pesok V.V., Alawsi Hayder A.H., Butusov A.V. Hybrid Neuro-Fuzzy Classifier for Monitoring the Effectiveness of Treatment of Diseases of the Respiratory System, Taking into Account Comorbidity. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2023;13(4):27–53. (In Russ.) https://doi.org/10.21869/2223-1536-2023-13-4-27-53

13. Filist S.A., Salem H.A.R., Shatalova O.V., Rudenko V.V. Modeli nechetkih neyronnyh setey s trekhstabil'nym vyhodom v instrumentarii dlya psihologicheskih i fiziologicheskih issledovaniy [Slipshod Neuron Networks, Having Three Stable Output, for Diagnostics of Psychical Diseases]. Sistemnyy analiz i upravlenie v biomeditsinskih sistemah = System Analysis and Management in Biomedical Systems. 2007;6(2):475–479. (In Russ.)

14. Zhilin V.V., Filist S.A., Rakhim K.A., Shatalova O.V. A Method for Creating Fuzzy Neural-Network Models Using the Matlab Package for Biomedical Applications. Biomedical Engineering. 2008;42(2):64–66. https://doi.org/10.1007/s10527-008-9019-y

15. Efremov M.A., Filist S.A., Shatalova O.V., Startsev E.A., Shulga L.V. Method for Constructing Hybrid Fuzzy Models for Predicting the Occurrence and Complications of Arterial Hypertension in Drivers of Vehicles Taking into Account the Energy Characteristics of Bioactive Points. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Seriya: Upravlenie, vychislitel'naya tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2018;8(4):104–119. (In Russ.)

16. Kudryavtsev P.S., Shutkin A.N., Protasova V.V., Filist S.A. Structural Functional Model for Monitoring the Influence of Control Actions on Self-Organizing Systems State. Prikaspiyskiy zhurnal: upravlenie i vysokie tekhnologii = The Caspian Journal: Management and High Technologies. 2015;(2):105–118. (In Russ.)

17. Filist S.A., Tomakova R.A., Yaa Z.D. Universal Network Models for Classification of Biomedical Data. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta = Proceedings of Southwest State University. 2012;(4):44–50. (In Russ.)

18. Petrunina E.V., Shatalova O.V., Protasova Z.U., Rybochkin A.F., Serebrovsky V.V. Prediction of Coronary Risk Using a Multichannel System with Redundant Decisions and Associative Choice. Biomedical Engineering. 2020;54(2):140–144. https://doi.org/10.1007/ s10527-020-09991-5

19. Kobyakova O.S., Starovoytova E.A., Tolmachev I.V., et al. Contribution of Combined Risk Factors into Development of Chronic Non-Communicable Diseases. Sotsial'nye aspekty zdorov'ya naseleniya = Social aspects of Population Health. 2020;(66):1. (In Russ.) https://doi.org/ 10.21045/2071-5021-2020-66-5-1

20. Korenevskiy N.A., Filist S.A., Shatalova O.V., Kassim K.D.A., Rudenko V.V. Diagnostic Systems Based on Analysis of Current-Voltage Characteristics of Bioactive Points. Biotekhnosfera = Biotechnosphere. 2013;(5):33–38. (In Russ.)

21. Volkov I.I., Emel'yanov S.G., Filist S.A. Complicated Objects Classification Method Based on Analysis of Structural Functions of Slow Waves. Biomeditsinskaya radioelektronika = Journal Biomedical Radioelectronics. 2012;4:6–12. (In Russ.)

22. Trifonov A.A., Petrunina E.V., Filist S.A., Kuzmin A.A., Zhilin V.V. Biotechnical System with Virtual Reality in Rehabilitation Complexes with Artificial Feedback. Izvestiya Yugo-Zapadnogo gosudarstvennogo universiteta. Serija: Upravlenie, vychislitel'naja tekhnika, informatika. Meditsinskoe priborostroenie = Proceedings of the Southwest State University. Series: Control, Computer Engineering, Information Science. Medical Instruments Engineering. 2019;9(4):49–66. (In Russ.)