The Application of PROMETHEE and K-means Clustering Techniques for Enhancing Robotic Assistance in Bariatric Surgery

Authors

  • Declan Ikechukwu Emegano Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.
  • Abdullahi Garba Usman Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.
  • Auwalu Saleh Mubarak Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.
  • Berna Uzun Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.
  • Ilker Ozsahin Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.
  • Dilber Uzun Ozsahin Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, 27272 Sharjah, United Arab Emirates.

DOI:

https://doi.org/10.48165/gjs.2024.1205

Keywords:

weight loss, techniques, surgical, robotic bariatric surgery, outcomes PROMETHEE, clustering

Abstract

Robotic bariatric surgery (RBS) uses robotics in weight loss operations. The robotic system has been tremendous in the field of bariatric surgery (BS) owing to its overwhelming advantages. As a result, surgeons perform RBS by controlling robotic arms equipped with surgical equipment from a console. In this study, our focus is to evaluate the BS dataset and techniques such as Roux-en-Y gastric bypass (RYGB), laparoscopic vertical sleeve gastrectomy (LGSV), adjustable gastric band (AGB), mini gastric bypass (MGB), single anastomosis duodenal bypass (SADI-S), open RYGB, biliopancreatic diversion with duodenal switch (BPD-DS), and mini gastric bypass one anastomosis gastric bypass (MGB - OAGBP). We used the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and K-mean clustering analysis to look at how well RBS could help with surgery outcomes. Our result showed that the K-mean of 11 neurons input layer has 100 Self-Organizing Maps (SOM). Also, the PROMETHEE result showed that MGB-OAGBP ranked highest with 0.0056 as the net flow value. The integration of PROMETHEE net flow values, positive and negative outranking values, and BS predicted values demonstrated that MGB-OAGDP ranked highest. In conclusion, our study shows that PROMETHEE and K-means clustering can improve the efficiency of RBS, which can lead to better outcomes for patients.

Author Biographies

  • Declan Ikechukwu Emegano, Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.

    Department of Biomedical Engineering, Near East University, TRNC, Mersin 10, 99138, Nicosia, Turkey.

  • Abdullahi Garba Usman, Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.

    Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138

  • Dilber Uzun Ozsahin, Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, 27272 Sharjah, United Arab Emirates.

    Research Institute for Medical and Health Sciences, University of Sharjah, 27272 Sharjah, United Arab Emirates. 

    Operation Research Centre in Healthcare, Near East University, 99138 Nicosia/TRNC, Mersin 10, Turkey.

References

[1] Lo, T., & Tavakkoli, A. (2019). Bariatric surgery and its role in obesity pandemic. Current Opinion in Physiology, 12, 51–56. https://doi.org/10.1016/j.cophys.2019.09.002.

[2] El Ansari, W., & Elhag, W. (2021). Weight Regain and Insufficient Weight Loss After Bariatric Surgery: Definitions, Prevalence, Mechanisms, Predictors, Prevention and Management Strategies, and Knowledge Gaps—a Scoping Review. Obesity Surgery, 31(4), 1755–1766. https://doi.org/10.1007/s11695-020-05160.

[3] Chooi, Y. C., Ding, C., & Magkos, F. (2019). The epidemiology of obesity. Metabolism, 92, 6–10. https://doi.org/10.1016/j.metabol.2018.09.005.

[4] Biener, A., Cawley, J., & Meyerhoefer, C. (2017). The High and Rising Costs of Obesity to the US Health Care System. Journal of General Internal Medicine, 32(Suppl 1), 6–8. https://doi.org/10.1007/s11606-016-3968-8. [5] Khaitan, L., & Shea, B. J. (2020). Laparoscopic vertical sleeve gastrectomy, long and short-term impact on weight loss and associated co-morbidities. Annals of Translational Medicine, 8(Suppl 1), S5. https://doi.org/10.21037/atm.2020.01.89.

[6] Parikh, M., Eisenberg, D., Johnson, J., & El-Chaar, M. (2018). American Society for Metabolic and Bariatric Surgery review of the literature on one-anastomosis gastric bypass. Surgery for Obesity and Related Diseases, 14(8), 1088– 1092. https://doi.org/10.1016/j.soard.2018.04.017.

[7] Luesma, M. J., Fernando, J., Cantarero, I., Lucea, P., & Santander, S. (2022). Surgical Treatment of Obesity. Special Mention to Roux-en-Y Gastric Bypass and Vertical Gastrectomy. Frontiers in Endocrinology, 13, 867838. https://doi.org/10.3389/fendo.2022.867838.

[8] Magouliotis, D. E., Tasiopoulou, V. S., Sioka, E., Chatedaki, C., & Zacharoulis, D. (2017). Impact of Bariatric Surgery on Metabolic and Gut Microbiota Profile: A Systematic Review and Meta-analysis. Obesity Surgery, 27(5), 1345– 1357. https://doi.org/10.1007/s11695-017-2595-8.

[9] Seeras, K., Sankararaman, S., & Lopez, P. P. (2023). Sleeve Gastrectomy. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK519035/

[10] Gundogdu, E., & Moran, M. (2020). Adjustable gastric banding. Annals of Laparoscopic and Endoscopic Surgery, 6. https://doi.org/10.21037/ales-2019-bms-06.

[11] Sudan, R., Bennett, K. M., Jacobs, D. O., & Sudan, D. L. (2012). Multifactorial Analysis of the Learning Curve for Robot-Assisted Laparoscopic Biliopancreatic Diversion With Duodenal Switch. Annals of Surgery, 255(5), 940. https://doi.org/10.1097/SLA.0b013e31824c1d06.

[12] El Chaar, M., Stoltzfus, J., Elias, B., & Claos, L. (2017). A5283 - Laparoscopic Roux-en-Y Gastric Bypass vs. Mini Gastric Bypass: A Systematic Review of the Literature. Surgery for Obesity and Related Diseases, 13(10, Supplement), S192. https://doi.org/10.1016/j.soard.2017.09.425.

[13] Bauerle, W. B., Mody, P., Estep, A., Stoltzfus, J., & El Chaar, M. (2023). Current Trends in the Utilization of a Robotic Approach in the Field of Bariatric Surgery. Obesity Surgery, 33(2), 482–491. https://doi.org/10.1007/s11695-022- 06378-1.

51 Emegano et al, Global Journal of Sciences, 1(2), 2024, 38–51

[14] Cepolina, F., & Razzoli, R. P. (2022). An introductory review of robotically assisted surgical systems. The International Journal of Medical Robotics + Computer Assisted Surgery, 18(4), e2409. https://doi.org/10.1002/rcs.2409.

[15] Klodmann, J., Schlenk, C., Hellings-Kuß, A., Bahls, T., Unterhinninghofen, R., Albu-Schäffer, A., & Hirzinger, G. (2021). An Introduction to Robotically Assisted Surgical Systems: Current Developments and Focus Areas of Research. Current Robotics Reports, 2(3), 321–332. https://doi.org/10.1007/s43154-021-00064-3.

[16] Kulaylat, A. N., Richards, H., Yada, K., Coyle, D., Shelby, R., Onwuka, A. J., Aldrink, J. H., Diefenbach, K. A., & Michalsky, M. P. (2021). Comparative analysis of robotic-assisted versus laparoscopic cholecystectomy in pediatric patients. Journal of Pediatric Surgery, 56(10), 1876–1880. https://doi.org/10.1016/j.jpedsurg.2020.11.013.

[17] Barua, R., & Datta, S. (2023). Emerging Surgical Robotic Applications for Modern Minimally Invasive Surgery (MIS): In M. K. Habib (Ed.), Advances in Computational Intelligence and Robotics (pp. 314–332). IGI Global. https://doi.org/10.4018/978-1-6684-7791-5.ch014

[18] Han, J., Davids, J., Ashrafian, H., Darzi, A., Elson, D. S., & Sodergren, M. (2022). A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches. The International Journal of Medical Robotics and Computer Assisted Surgery, 18(2), e2358. https://doi.org/10.1002/rcs.2358.

[19] Peng, Y.-C., Jivani, D., Radke, R. J., & Wen, J. (2020). Comparing Position- and Image-Based Visual Servoing for Robotic Assembly of Large Structures. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 1608–1613. https://doi.org/10.1109/CASE48305.2020.9217028.

[20] Peters, B. S., Armijo, P. R., Krause, C., Choudhury, S. A., & Oleynikov, D. (2018). Review of emerging surgical robotic technology. Surgical Endoscopy, 32(4), 1636–1655. https://doi.org/10.1007/s00464-018-6079-2. [21] Powell-Wiley, T. M., Poirier, P., Burke, L. E., Després, J.-P., Gordon-Larsen, P., Lavie, C. J., Lear, S. A., Ndumele, C. E., Neeland, I. J., Sanders, P., St-Onge, M.-P., & American Heart Association Council on Lifestyle and Cardiometabolic Health; Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; Council on Epidemiology and Prevention; and Stroke Council. (2021). Obesity and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation, 143(21), e984–e1010. https://doi.org/10.1161/CIR.0000000000000973.

[22] Rosenblatt, P. L., McKinney, J., & Adams, S. R. (2013). Ergonomics in the operating room: Protecting the surgeon. Journal of Minimally Invasive Gynecology, 20(6), 744. https://doi.org/10.1016/j.jmig.2013.07.006. [23] Zhou, X.-Y., Guo, Y., Shen, M., & Yang, G.-Z. (2020). Application of artificial intelligence in surgery. Frontiers of Medicine, 14(4), 417–430. https://doi.org/10.1007/s11684-020-0770-0.

[24] Benalcazar, D. A., & Cascella, M. (2023). Obesity Surgery Preoperative Assessment and Preparation. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK546667/

[25] Kermansaravi, M., Shahmiri, S. S., DavarpanahJazi, A. H., Valizadeh, R., Berardi, G., Vitiello, A., Musella, M., & Carbajo, M. (2021). One Anastomosis/Mini-Gastric Bypass (OAGB/MGB) as Revisional Surgery Following Primary Restrictive Bariatric Procedures: A Systematic Review and Meta-Analysis. Obesity Surgery, 31(1), 370–383. https://doi.org/10.1007/s11695-020-05079-x.

[26] Seeras, K., Acho, R. J., & Lopez, P. P. (2023). Roux-en-Y Gastric Bypass Chronic Complications. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK519489/

[27] Brans, J. P., Vincke, Ph., & Mareschal, B. (1986). How to select and how to rank projects: The Promethee method. European Journal of Operational Research, 24(2), 228–238. https://doi.org/10.1016/0377-2217(86)90044-5. [28] Balcioglu, O., Ikechukwu Emegano, D., Uzun, B., Şahin, T., Ozsahin, I., & Uzun Ozsahin, D. (2023). Comparative evaluation of blood conservation techniques in cardiovascular surgery using multicriteria decision-making methods. 2023 Advances in Science and Engineering Technology International Conferences (ASET), 1–8. https://doi.org/10.1109/ASET56582.2023.10180430

[29] Uzun, B., Usanase, N., Ozsahin, D. U., & Ozsahin, I. (2024). The Application of the Multi-Criteria Decision-Making Model in the Risk Assessment of Cervical Cancer Complications. 2024 Advances in Science and Engineering Technology International Conferences (ASET), 1–5. https://doi.org/10.1109/ASET60340.2024.10708754

[30] Duwa, B., Onakpojeruo, E. P., Uzun, B., Ozsahin, I., & Ozsahin, D. U. (2022). Comparative Evaluation of 3D Filaments, Used in Additive Manufacturing of Biomedical Tools; Using Fuzzy Promethee. [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-2020207/v1

[31] Ozsahin, D. U., Ikechukwu Emegano, D., Uzun, B., & Ozsahin, I. (2024). A Comparative Study on Male Breast Cancer Treatment Alternatives Using a Multi-Criteria Decision-Making (MCDM) Approach. 2024 Advances in Science and Engineering Technology International Conferences (ASET), 1–5. https://doi.org/10.1109/ASET60340.2024.10708657

[32] Ozsahin, D. U., Gelisen, M. I., Taiwo, M., Agachan, Y., Rahi, D., & Uzun, B. (2021). Decision Analysis of the COVID 19 Vaccines. The EuroBiotech Journal, 5(s1), 20–25. https://doi.org/10.2478/ebtj-2021-0017. [33] Yan, S., Tao, X., & Xu, D. (2021). High-precision robotic assembly system using three-dimensional vision. International Journal of Advanced Robotic Systems, 18(3), 17298814211027029. https://doi.org/10.1177/17298814211027029.

[34] Bellini, V., Valente, M., Turetti, M., Del Rio, P., Saturno, F., Maffezzoni, M., & Bignami, E. (2022). Current Applications of Artificial Intelligence in Bariatric Surgery. Obesity Surgery, 32(8), 2717–2733. https://doi.org/10.1007/s11695-022-06100-1.

Hany, M., Zidan, A., Elmongui, E., & Torensma, B. (2022). Revisional Roux-en-Y Gastric Bypass Versus Revisional One-Anastomosis Gastric Bypass After Failed Sleeve Gastrectomy: A Randomized Controlled Trial. Obesity Surgery, 32(11), 3491–3503. https://doi.org/10.1007/s11695-022-06266-8.

Downloads

Published

2025-01-24

Issue

Vol. 1 No. 2 (2024): Global Journal of Sciences

Section

Research Articles

How to Cite

The Application of PROMETHEE and K-means Clustering Techniques for Enhancing Robotic Assistance in Bariatric Surgery . (2025). Global Journal of Sciences, 1(2), 38-51. https://doi.org/10.48165/gjs.2024.1205