ISSN 1812-7231 Klin.inform.telemed. Volume 13, Issue 14, 2018, Pages 69–73
А. B. Buzinovsky, D. M. Bayazitov, А. В. Lyashenko, D. V. Novikov, Т. L. Godlevska
Odessa National Medical University, Ukraine
Characteristics of laparoscopic images as a basis for postoperative period risks estimation
Introduction. Pelvis pain in women is in charge for 25% of advertisements for medical help and for more than 40% of all diagnostic laparoscopic investigations.
Formulation of the problem. Methodology. Two types of inflammation have been identified on the basis of algorithms on videolaparoscopic color, texture and contour estimation — local and pronounced ones. Local process was diagnosed in case of superficial solitary foci diameter up to 2, 0 cm while pronounced one was characterized by multiple joined and bilateral foci with their size exceeded 2, 0 cm. Odds ratio values were calculated with the purpose of estimation of risks of staying at hospital more than four days after women with inflammation, endometriosis and tumor-like processes in tubo-ovarial zone have been operated on.
The object of the study. The effectiveness of prognosis of staying patients at hospital during postoperative period which was based on the proposed videolaparoscopic criteria of the pronouncement of inflammation was investigated.
Study results. Odds ratio in women who had positive index (laparoscopy verified severe inflammation) was 18, 804 ± 0, 496, while in women with endometriosis the odds ratio was 14, 824 ± 0, 290, and in case of tumors of tuboovarial zone — 9, 969 ± 0, 442.
Conclusions. The criteria of pelvic inflammation pronouncement determined on the basis of videolaparoscopic data satisfactorily forecasted the length of women staying at hospital in postoperative period.
Laparoscopy, System of support of surgeon decision, Risks of surgical complications precipitation
1. Bayazitov D. М., Kresyun N. V., Buzynovsky А. B., Lyashenko А. V., Nenova О. М. Automatic computer diagnostics of appen dicitis during laparoscopic intervention. Кlinichna Hirurgija [Clinical Surgery]. 2017, no. 8, iss. 904, pp. 21–23. (In Ukr.).
2. Doan D. H., Kroshilin A. V., Kroshilina S. V. The review on the problem of decision support in medical informational systems under conditions of uncertainty. Fundamentalnye Issledovaniya [Fundamental Investigations]. 2015, no. 12, pp. 26–30. (In Russ.).
3. Koleshnikova T. V., Shteinberg А. М. [Interactive informational system of the support of multicriterial decision]. Trudy Mezhdunarodnoj nauchno-tehnicheskoj konferencii "Perspektivnye informacionnye tehnologii" [Proc.of the Int. Conf. "Perspective informational technologies"]. Moscow, 2015, vol. 1, pp. 171–173. (In Rus.).
4. Kravchenko V. V. The automatic informational technology on the decision support in the course of human physical health governing. Inzheneriya Programnogo Obespecheniya [Engine e ring of software providing]. 2015, vol. 2, iss. 22, pp. 29–39. (In Ukr.).
5. Litvin А. А., Litvin V. A. Systems of decision support in surgery. Novosti Hirurgii [News in Surgery]. 2014, no. 1, pp. 96–100. (In Russ.).
6. Mintser O. P., Shevchenko Ya. O., Feshchenko A. I., Yaroshenko O. O. [Decisions in mobile medicine]. Trudy Konf. SPPR 2017 "Sistemy pidtrymki pryjnyattya rishen. Teoria I praktika" [Proc.of the Conf. SPPR, 2017 "Systems of decision support. Theiry and practice"]. 2017, pp. 81–82. (In Ukr.).
7. Buzynovskij A. B., Kovalenko O. S., Bayazitov D. M., Lyashenko A. V., Nenova O. M. The system of decision support in laparoscopic surgery and it's effectiveness estimation in the course of appen dectomy. Dosyagnennya Biologii i Mediciny [[Achievements of Biology and Medicine]. 2016, no. 1, pp. 31–35. (In Ukr.).
8. Moonesinghe S. R., Harris S., Mythen M. G. et al. Survival afterpostoperative morbidity: a longitudinal observational cohortstudy. Br. J. Anaesth, 2014, vol. 113, pp. 977–984.
9. Oshima L. E., Emanuel E. J. Shared decision making to improve care and reduce costs. N. Engl. J. Med., 2013, vol. 368, iss. 1, pp. 6–8.
10. Kumar S., Singhal P. and Krovi V. N. Computer-Vision-Based Decision Support in Surgical Robotics. IEEE Design & Test., 2015, vol. 32, no. 5, pp. 89–97.
11. Crebbin W., Beasley S. W., Watters D. A. Clinical decision making: how surgeons do it. ANZ J. Surg., 2013, vol. 83, iss. 6, pp. 422–428.
Full-text version http://kit-journal.com.ua/en/viewer_en.html?doc/2018_14/008.pdf