SUPPORT FOR MEDICAL DECISION-MAKING WHEN PLANNING THE LASER LITHOTRIPSY PROCEDURE
Keywords:
Fuzzy estimation, laser lithotripsy, stone mass, destruction time, laser energy, laser frequency
Abstract
When preparing for the laser lithotripsy procedure, choosing the parameters of the laser installation,
the doctor considers many factors, such as the mass and density of concretions found in the kidney,
the location of kidney stones, and the proximity of blood vessels. Another important parameter is the time
of exposure to the stone with a laser beam before the stone is destroyed. At the same time, calculating the
time of destruction of a stone is a rather time–consuming procedure, the time of destruction depends on
the mass of the stone and the parameters of the laser energy and its frequency. Therefore, it is relevant to
create a system to support medical decision-making during the laser lithotripsy procedure, which allows
you to calculate the time of stone destruction and select the values of laser parameters. The article proposes
an algorithm to support the choice of the laser operating mode by a urologist during the laser lithotripsy
procedure in the treatment of human urolithiasis, which is part of the medical decision support system
in surgery and urology using computer vision technologies. The proposed algorithm for fuzzy estimation
of laser parameters when choosing its operating mode, depending on the mass of the stone and the
selected time of destruction of the stone and other factors (distribution of stone density, location of the
stone in the kidney, proximity of walls and vessels) generates recommendations for setting the parameters
of the laser. The medical decision support system made it possible to reduce the time for a doctor to decide,
to avoid mistakes when choosing the parameters of the laser installation for crushing kidney stones.
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the Russian Military Medical Academy], 2019, 38 (4), pp 122-125. Available at: https://journals.ecovector.
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13. Fitri L.A., Haryanto F., Arimura H., YunHao C., Ninomiya K., Nakano R. Automated classification of urinary
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mochekamennoy bolezni [Artificial intelligence in the diagnosis and treatment of urolithiasis], Rossiyskiy
zhurnal telemeditsiny i elektronnogo zdravookhraneniya [Russian Journal of Telemedicine and
E-Health], 2022, 8 (1), pp. 42-57. Available at: https://doi.org/10.29188/2712-9217-2022-8-1-42-57.
16. Jendeberg J., Thunberg P., Lide n M. Differentiation of distal ureteral stones and pelvic phleboliths using
a convolutional neural network, Urolithiasis, 2020. Available at: https://doi.org/10.1007/s00240-020-
01180-z.
17. Nithya A., Appathurai A., Venkatadri N., Ramji D.R., Anna Palagan C. Kidney disease detection and segmentation
using artificial neural network and multi-kernel k-means clustering for ultrasound images, Meas J
Int Meas Confed., 2020, 149. Available at: https://doi.org/10.1016/j.measurement.2019.106952.
18. Kudryash V.L., Marshev S.V., Gabliya M.Yu i dr. Prakticheskie aspekty primeneniya distantsionnoy
litotripsii u bol'nykh mochekamennoy bolezn'yu [Practical aspects of the use of remote lithotripsy in
patients with urolithiasis], Urologiya [Urology], 2013, No. 2, pp. 12-17.
19. Chernega V.S., Eremenko S.N., Eremenko A.N. i dr. Prognozirovanie vremeni transuretral'noy
gol'mievoy litotripsii v lechenii urolitiaza [Predicting the time of transurethral holmium lithotripsy in
the treatment of urolithiasis], Vrach i informatsionnye tekhnologii [Doctor and Information Technologies],
2020, No. 2, pp. 72-80. DOI: 10.37690/1811-0193-2020-2-72-80.
20. Rudenko M.A., Rudenko A.V. Nechetkaya model' klassifikatsii meditsinskikh izobrazheniy na osnove
neyronnykh setey [Fuzzy model of classification of medical images based on neural networks],
Mezhdunarodnaya konferentsiya po myagkim vychisleniyam i izmereniyam [International Conference
on Soft Computing and Measurements], 2021, Vol. 1, pp. 336-339.
informatsionnykh sistemakh meditsinskoy organizatsii [Support for medical decision-making in medical
information systems of a medical organization], Vrach i informatsionnye tekhnolo-gii [Doctor and
information technologies], 2017, No. 2, pp. 60-72.
2. Omirova N.I., Tishkov A.V. Sistema podderzhki prinyatiya resheniy vracha-urologa v
biotekhnicheskoy sisteme diagnostiki mochekamennoy bolezni [Decision support system of a urologist
in a biotechnical system for diagnosing urolithiasis], Biotekhnosfera [Biotechnosphere], 2020,
No. 5(65), pp. 43-48. DOI: 10.25960/bts.2020.5.43.
3. Shchamkhalova K.K., Merinov D.S., Artemov A.V. i dr. Iskusstvennyy intellekt i neyronnye seti v
urologii [Artificial intelligence and neural networks in urology], Eksperimental'naya i klinicheskaya
urologiya [Experimental and clinical urology], 2023, Vol. 16, No. 2, pp. 32-37. DOI: 10.29188/2222-
8543-2023-16-2-32-37.
4. Galchenkov A.S., Novitskiy V.O., Kondrat'ev E.A. Servisy podderzhki prinyatiya resheniy po
diagnostike i lecheniyu zabolevaniy i ikh prakticheskoe primenenie na primere KhBP 5D [Decision
support services for the diagnosis and treatment of diseases and their practical application on the example
of CKD 5D], Vrach i informatsionnye tekhnologii [Doctor and information technology], 2020,
No. 1, pp. 45-51. DOI: 10.37690/1811-0193-2020-1-45-51.
5. Levenkov K.O., Korovin E.N., Novikova E.I. Neyrosetevoe modelirovanie protsessa vybora skhemy
lecheniya patsientov s khronicheskim pielonefritom i mochekamennoy bolezn'yu [Neural network modeling
of the process of choosing a treatment regimen for patients with chronic pyelonephritis and urolithiasis],
Modelirovanie, optimizatsiya i informatsionnye tekhnologii [Modeling, optimization and information technology],
2018, Vol. 6, No. 4 (23), pp. 61-71. – DOI: 10.26102/2310-6018/2018.23.4.005.
6. Kotsar' A.G. Matematicheskoe modelirovanie i algoritmizatsiya prognozirovaniya, diagnostiki,
profilaktiki i lecheniya mochekamennoy bolezni: diss. … d-ra med. nauk [Mathematical modeling and
algorithmization of forecasting, diagnosis, prevention and treatment of urolithiasis: dr. of med. sc.
diss.], 2014, 302 p.
7. Zubarev D.A., Korenevskaya S.N., Kotsar' A.G. i dr. Ispol'zovanie nechetkikh matematicheskikh modeley
pri vedenii bol'nykh s mochekamennoy bolezn'yu [The use of fuzzy mathematical models in the management
of patients with urolithiasis], Sistemnyy analiz i upravlenie v biomeditsinskikh sistemakh [System
analysis and management in biomedical systems], 2017, Vol. 16, No. 2, pp. 345-350.
8. Chernega V.S., Tlukhovskaya-Stepanenko N.P., Eremenko A. i dr. Otsenka skorosti fragmentatsii
mochevykh kamney pri kontaktnoy litotripsii gol'mievym lazerom [Assessment of the rate of fragmentation
of urinary stones during contact lithotripsy with a holmium laser], Urologiya [Urology], 2018,
No. 5, pp. 69-73. DOI: 10.18565/urology.2018.5.69-72.
9. Korobkov D.M., Mosina L.M., Stepanov N.Yu. Primenenie distantsionnoy udarno-volnovoy litotripsii
pri mochekamennoy bolezni [The use of remote shock wave lithotripsy in urolithiasis],
Mezhdunarodnyy nauchno-issledovatel'skiy zhurnal [International Scientific Research Journal], 2021,
No. 2-3 (104), pp. 31-33. DOI: 10.23670/IRJ.2021.103.2.067.
10. Stepanov N.Yu. Duvayarov Z.A., Boyarkin E.V. i dr. Primenenie ekstrakorporal'noy udarno-volnovoy
litotripsii i kontaktnoy ureterolitotripsii pri lechenii konkrementov v distal'nom otdele mochetochnika
[The use of extracorporeal shock wave lithotripsy and contact ureterolithotripsy in the treatment of
concretions in the distal ureter], Mezhdunarodnyy nauchno-issledovatel'skiy zhurnal [International
Scientific Research Journal], 2020, No. 6 (98), pp 116-118.
11. Chernega V.S., Tlukhovskaya-Stepanenko N.P., Eremenko S.N. i dr. Setevaya model' dlya otsenki
dlitel'nosti meditsinskogo tekhnologicheskogo protsessa lazernoy kontaktnoy litotripsii [A network
model for estimating the duration of the medical technological process of laser contact lithotripsy],
Vrach i informatsionnye tekhnologii [Doctor and Information Technologies], 2018, No. 4, pp. 75-82.
12. Borisov D.D., Kul'nev S.V., Sevryukov V.V. Primenenie iskusstvennogo intellekta pri organizatsii
lechebno-diagnosticheskikh meropriyatiy [The use of artificial intelligence in the organization of therapeutic
and diagnostic measures], Izvestiya rossiyskoy voenno-meditsinskoy akademii [Proceedings of
the Russian Military Medical Academy], 2019, 38 (4), pp 122-125. Available at: https://journals.ecovector.
com/RMMArep/article/view/26037/20184/ru_RU.
13. Fitri L.A., Haryanto F., Arimura H., YunHao C., Ninomiya K., Nakano R. Automated classification of urinary
stones based on microcomputed tomography images using convolutional neural network, Physica
Medica, 2020, 78, pp. 201-208. Available at: https://doi.org/10.1016/j.ejmp.2020.09.007.
14. Parakh A., Lee H., Lee J.H., Eisner B.H., Sahani D.V., Do S. Urinary Stone Detection on CT Images Using
Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization, Radiol Artif
Intell., 2019 Jul 24, 1(4):e180066. Available at: https://doi.org/10.1148/ryai.2019180066.
15. Pranovich A.A., Ismailov A.K., Karel'skaya N.A. i dr. Iskusstvennyy intellekt v diagnostike i lechenii
mochekamennoy bolezni [Artificial intelligence in the diagnosis and treatment of urolithiasis], Rossiyskiy
zhurnal telemeditsiny i elektronnogo zdravookhraneniya [Russian Journal of Telemedicine and
E-Health], 2022, 8 (1), pp. 42-57. Available at: https://doi.org/10.29188/2712-9217-2022-8-1-42-57.
16. Jendeberg J., Thunberg P., Lide n M. Differentiation of distal ureteral stones and pelvic phleboliths using
a convolutional neural network, Urolithiasis, 2020. Available at: https://doi.org/10.1007/s00240-020-
01180-z.
17. Nithya A., Appathurai A., Venkatadri N., Ramji D.R., Anna Palagan C. Kidney disease detection and segmentation
using artificial neural network and multi-kernel k-means clustering for ultrasound images, Meas J
Int Meas Confed., 2020, 149. Available at: https://doi.org/10.1016/j.measurement.2019.106952.
18. Kudryash V.L., Marshev S.V., Gabliya M.Yu i dr. Prakticheskie aspekty primeneniya distantsionnoy
litotripsii u bol'nykh mochekamennoy bolezn'yu [Practical aspects of the use of remote lithotripsy in
patients with urolithiasis], Urologiya [Urology], 2013, No. 2, pp. 12-17.
19. Chernega V.S., Eremenko S.N., Eremenko A.N. i dr. Prognozirovanie vremeni transuretral'noy
gol'mievoy litotripsii v lechenii urolitiaza [Predicting the time of transurethral holmium lithotripsy in
the treatment of urolithiasis], Vrach i informatsionnye tekhnologii [Doctor and Information Technologies],
2020, No. 2, pp. 72-80. DOI: 10.37690/1811-0193-2020-2-72-80.
20. Rudenko M.A., Rudenko A.V. Nechetkaya model' klassifikatsii meditsinskikh izobrazheniy na osnove
neyronnykh setey [Fuzzy model of classification of medical images based on neural networks],
Mezhdunarodnaya konferentsiya po myagkim vychisleniyam i izmereniyam [International Conference
on Soft Computing and Measurements], 2021, Vol. 1, pp. 336-339.
Published
2024-05-28
Issue
Section
SECTION III. INFORMATION PROCESSING ALGORITHMS
