Evaluation of medical images is of key importance in the work of medical staff today. Especially this problem pays important role in otolaryngology. The aim of our work was to develop an automatic complex method for assessing the state of the human paranasal sinuses. Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021. The structure of the mucous membrane of the maxillary sinus were calculated and compared. In the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal.
More often, instrumental methods are used for diagnostic purposes, for example, a computed
tomography (CT) [
Considering all of the above, the purpose of our work was to design an automatic complex method for assessing the state of the human paranasal sinuses.
Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021 (see Table 1). The selection criterion for the examined persons was the presence of an unilateral cyst (see Fig. 1) of the maxillary sinus, which was subsequently removed endoscopically. During the removal of the cyst and the enlargement of the natural anastomosis, samples of the mucous membrane of the maxillary sinus were taken (see Fig. 2).
The research was carried out on a spiral computed tomography (SCT) Toshiba Aquilion 4 (Japan), which is distinguished by the reliability of results and ease of use. The Asterion Super 4 System is a multi-slice spiral CT scanner for the whole body. This system generates 4 slices in one 0.75 second revolution using a multi-row detector with selectable thickness using.
Staining with hematoxylin and eosin is most widespread method for investigation in histology,
pathologic anatomy, biology. Nuclei are stained in blue Staining with hematoxylin, cellular cytoplasm
is stained in pink-red with eosin as result [
This method allows you to identify the cellular composition of the test sample accurately and informatively, to clarify the features of the location of cells and the size of the nuclei. In the course of the work, attention was paid to such parameters as the thickness of the basement membrane, the quantitative composition of the cells above and below the basement membrane, as the main markers of the inflammatory processes in the tissue (Fig. 3).
Automatic evaluation of histological slides is a challenge for research both in relation to classical
staining methods and in relation to the created ones. Immunohistochemical staining (IHC) allows
selective identification of antigens (proteins) in cells and tissues using the principle of binding of
specific antibodies to antigens and applying an additional dye [
The obtained micropreparations after all stages of preparation were studied using an Olympus BX41 ”microscope with objectives x4, x100, x200, x400. The results were further processed by the Olympus DP-soft version 3.2 software, which allows to interpret the obtained data after the morphometric study accurately and informatively. Also, a histological examination of the state of the ciliated epithelium of the mucous membrane of the paranasal sinuses was carried out.
In order to determine the accuracy of the method a calculation in a manual mode was carried out after carrying out the calculations in an automatic way for the correction and comparing the results.
An algorithm of the automated measurement was described in our previous works [18-20].
During the work, the data of the studied signs of the structure of the mucous membrane were obtained both in manual and automatic modes (see Fig. 4 and 5).
The results of the measurements are presented in Table 2.
Table 2 The research results of indicators of the structure of the mucous membrane of the maxillary sinus by the manual and automatic mode
Number
At the subsequent stages, automatic and manual measurements of the thickness and the density of the bone of the upper and lower walls of the maxillary sinus were carried out, as potentially dangerous for the development of the complications, according to the data of the spiral computed tomography. All stages of automatic image evaluation have been presented in our previous works. An example of determining the thickness of the skull bones in automatic and manual modes is shown in Fig. 6 A.
At the subsequent stages, automatic and manual measurements of the thickness and the density of the bone of the upper and lower walls of the maxillary sinus were carried out, as potentially dangerous for the development of the complications, according to the data of the spiral computed tomography. All stages of automatic image evaluation have been presented in our previous works. An example of determining the thickness of the skull bones in automatic and manual modes is shown in Fig. 6 B.
The research included the upper wall of the maxillary sinus due to its potential danger in terms of the development of intraorbital complications and the lower wall of the maxillary sinus in connection with the development through it the odontogenic maxillary sinusitis. Thus, it can be assumed that the work carried out is one of the first ones related to this medical topic [22].
The method of the automatic evaluation of the images deserves special attention. It is of particular importance since the workload on the medical staff [23] increases every day, which in turn can significantly affect the quality, accuracy and reliability of the obtained results and the correct interpretation of the data. It is known, the method of the automatic image assessment is currently used in various branches of medicine [24, 25]. Quite often it is also used in dentistry [26] or in otolaryngology, most often for the planning sugery, measuring the size of sinuses.
In the presented research, an algorithm, for solving such a difficult task as the measuring density according to the SCT data, is shown, which is undoubtedly one of the key indicators of the structure of the bone tissue of the paranasal sinusesʾ walls.
It is also interesting that there is a slight difference in the calculation of parameters in manual and automatic modes, which allows us to make an assumption about the reliability of our proposed method. The most variable parameter with the most significant difference in results of automatic and calculation was bone density. This difference may be connected with difficulties in the calculation process.
This method of automatic calculation of medical parameters is promising nowadays and may be used in different fields of medicine [27-29]
Thus, in the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal. The obtained data shows minimal average related error in the most of cases in the automatic detection of the structure of mucosa of Maxillary sinus as well as in the structure of density and thickness of its walls. The most variable parameter with the biggest difference in results the manual and automatic calculation was bone density.
R. Radutniy, A. Nechyporenko, V. Alekseeva, G. Titova, D. Bibik and V. Gargin, "Automated Measurement of Bone Thickness on SCT Sections and Other Images", 2020 IEEE Third International Conference on Data Stream Mining & Processing (DSMP), 2020. DOI: 10.1109/dsmp47368.2020.9204289.
V. Gargin et al., "Relationship between bone density of paranasal sinuses and adrenal steroids pattern in women during menopausal transition", Anthropological Review, vol. 83, no. 4, pp. 407418, 2020. DOI: 10.2478/anre-2020-0031.
A. Nechyporenko, V. Reshetnik, V. Alekseeva, N. Yurevych, R. Nazaryan and V. Gargin, "Implementation and analysis of uncertainty of measurement results for lower walls of maxillary and frontal sinuses", 2020 IEEE 40th International Conference on Electronics and Nanotechnology (ELNANO), 2020. DOI: 10.1109/elnano50318.2020.9088916.
S. Suttapreyasri, P. Suapear and N. Leepong, "The Accuracy of Cone-Beam Computed Tomography for Evaluating Bone Density and Cortical Bone Thickness at the Implant Site: Micro-Computed Tomography and Histologic Analysis", Journal of Craniofacial Surgery, vol. 29, no. 8, pp. 2026-2031, 2018. DOI: 10.1097/scs.0000000000004672.
D. Levy, P. Pecha, S. Nguyen and R. Schlosser, "Trends in complications of pediatric rhinosinusitis in the United States from 2006 to 2016", International Journal of Pediatric Otorhinolaryngology, vol. 128, p. 109695, 2020. DOI: 10.1016/j.ijporl.2019.109695.
D. Chumachenko, V. Balitskii, T. Chumachenko, V. Makarova, V. and M. Railian, "Intelligent expert system of knowledge examination of medical staff regarding infections associated with the provision of medical care", CEUR Workshop Proceedings, pp. 321, 2019.
A. Stenzinger et al., "Artificial intelligence and pathology: From principles to practice and future applications in histomorphology and molecular profiling", Seminars in Cancer Biology, 2021. DOI: 10.1016/j.semcancer.2021.02.011
A. Binder et al., "Morphological and molecular breast cancer profiling through explainable machine learning", Nature Machine Intelligence, vol. 3, no. 4, pp. 355-366, 2021. DOI: 10.1038/s42256-021-00303-4.
S. Krivenko, V. Lukin, O. Krylova, L. Kryvenko and K. Egiazarian, "A Fast Method of Visually Lossless Compression of Dental Images", Applied Sciences, vol. 11, no. 1, p. 135, 2020. DOI: 10.3390/app11010135.
S. Panigrahi and T. Swarnkar, “Machine learning techniques used for the histopathological image analysis of oral cancer-A Review,” The Open Bioinformatics Journal, vol. 13, no. 1, pp. 106–118, 2020.
G. Kopanitsa, A. Dudchenko, and M. Ganzinger, “Machine learning algorithms in cardiology domain: A systematic review (preprint),” JMIR Medical Informatics, 2019.
S. M. S. Dashti and S. F. Dashti, “An expert system to diagnose spinal disorders,” The Open Bioinformatics Journal, vol. 13, no. 1, pp. 57–73, 2020.