Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
권호 표지
DOI QR코드

DOI QR Code

Research on Patient Monitoring Technologies to Build a Smart Operating Room

스마트 수술실 구축을 위한 환자 모니터링 핵심 기반 기술 연구

  • 구본근 (대구경북과학기술원 기초학부) ;
  • 윤현수 (대구경북과학기술원 지능형로봇연구부) ;
  • 이현기 (대구경북과학기술원 지능형로봇연구부) ;
  • 장민강 (대구경북과학기술원 융합전공) ;
  • 하호건 (대구경북과학기술원 지능형로봇연구부) ;
  • 구교권 (대구경북과학기술원 지능형로봇연구부) ;
  • 김현지 (경북대학교 의과대학 마취통증의학교실)
  • Received : 2025.03.06
  • Accepted : 2025.04.13
  • Published : 2025.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Over the past decade, the number of surgeries performed in South Korea has steadily increased. Along with this rise, disputes stemming from medical accidents have also grown, bringing smart operating rooms into the spotlight as a potential solution. This paper proposes a prevention system targeting the occurrence of pressure ulcers, one of the accidents that may arise in operating rooms. Since contact pressure is the primary cause of pressure ulcers, our study presents two frameworks for detecting contact pressure. First, we utilize pressure-sensing pads to obtain the patient's condition and pinpoint the areas under pressure using ultra-high-resolution technology. Second, we propose a method to monitor the patient's condition through photoplethysmography (PPG) and detect areas experiencing abnormally high pressure using an artificial intelligence algorithm that leverages fine-tuning and data augmentation techniques. The proposed frameworks achieved a 40 dB improvement relative to the input and an accuracy of 96%, respectively, demonstrating the potential to enhance patient monitoring by integrating artificial intelligence into smart operating rooms.

지난 10년간 한국에서 진행된 수술의 건수는 꾸준히 증가하는 추세이다. 수술의 건수가 증가함에 따라 의료사고로 인한 분쟁 또한 늘어나고 있으며, 이에 대한 해결책으로 스마트 수술실이 주목받고 있다. 본 논문에서는 수술실에서 일어날 수 있는 사고 중 욕창 발생에 대한 예방 시스템을 제안한다. 접촉압력이 욕창의 가장 주요한 원인인 만큼, 우리 연구에서는 접촉압력을 감지하기 위한 프레임워크를 2가지 제시한다. 먼저 압력 감지 패드를 사용하여 환자의 상태를 얻고 초고해상도 기술을 통해 압력이 가해지는 위치를 찾아낸다. 또한 광혈량도를 통해 환자의 상태를 모니터링하고, 이를 미세 조정과 데이터 증강을 사용하는 인공지능 알고리즘을 이용해 비정상적으로 큰 압력을 받는 곳을 찾아내는 기술을 제시한다. 제안된 두 프레임워크는 각각 입력에 비해 40dB 개선, 96%의 정확도를 달성하였으며, 이는 스마트 수술실에 인공지능을 결합하여 환자 모니터링을 개선할 수 있음을 나타낸다.

Keywords

Acknowledgement

이 연구는 2022년도 경북대학교병원 생명의학연구원 연구비의 지원으로 이루어 졌음. 또한, 본 연구는 경북대학교병원 임상시험 심사위원회의 사전 승인을 받았음(KNUH IRB 2024-10-008-001).

References

  1. Abdelmoghith, A., Shaaban, R., Alsheghri, Z. and Ismail, L. (2020). IoT-based Healthcare Monitoring System: Bedsores Prevention, 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability, Jul. 27-28, London, UK, pp. 64–69.
  2. Chae, S. W. and Lee, D. (2024). Exploring the Potential for Early Prediction of Calving Signs through Analysis of Tail-Raising Behavior in Dairy Cows Using a Deep Learning-Based Pose Estimation Algorithm, Journal of Korea Society of Industrial Information Systems, 29(6), 1-9. https://doi.org/10.9723/jksiis.2024.29.6.001.
  3. Chandrasekhar, A., Yavarimanesh, M., Natarajan, K., Hahn, J. O. and Mukkamala, R. (2020). PPG Sensor Contact Pressure Should be Taken into Account for Cuff-less Blood Pressure Measurement, IEEE Transactions on Biomedical Engineering, 67(11), 3134-3140. https://doi.org/101109/TBME.2020.2976989. 101109/TBME.2020.2976989
  4. Grajales, L. and Nicolaescu, I. V. (2006). Wearable Multisensor Heart Rate Monitor, International Workshop on Wearable and Implantable Body Sensor Networks, Apr. 3-5, Cambridge, USA, pp. 150-157.
  5. Guo, Y., Shi, H., Kumar, A., Grauman, K., Rosing, T. and Feris, R. (2019). SpotTune: Transfer Learning through Adaptive Fine-tuning, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 16-20, Long Beach, USA, pp. 4805-4814.
  6. Hadi, A. and Amin, Y. H. (2012). Designing and Constructing an Optical Monitoring System of Blood Supply to Tissues Under Pressure, Journal of Medical Signals & Sensors, 2(2), 114-119. https://doi.org/10.4103/2228-7477.110448
  7. He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep Residual Learning for Image Recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Jun. 26–Jul. 1, Las Vegas, USA, pp. 770-778.
  8. Hwang, H. Y., Shin, Y. S., Cho, H. S. and Yeo, J. S. (2007). Risk Factors of Pressure Sore in Patients undergoing General Anesthesia, Korean Journal of Anesthesiology, 53(1), 79-84. https://doi.org/10.4097/kjae.2007.53.1.79.
  9. Jadhav, T., Gulhane, M., Bhattacharya, S., Khetani, V., Gandhi, Y. and Dolas, D. R. (2024). IoT-enabled Smart Operating Rooms for Enhancing Surgical Efficiency, Journal of Neonatal Surgery, 13, 37. https://doi.org/10.52783/jns.v13.1431.
  10. Kim, B. K., Byun, J. Y. and Cha, K. A. (2024). Mobile App for Detecting Canine Skin Diseases Using U-Net Image Segmentation, Journal of Korea Society of Industrial Information Systems, 29(4), 25-34. https://doi.org/10.9723/jksiis.2024.29.4.025.
  11. Kim, N. Y., Ryu, H. and Kwak, S. (2024). Patient Safety Incidents in Operating Rooms Reported in the Past Five Years (2017-2021) in Korea, Risk Management and Healthcare Policy, 17, 1639-1646. https://doi.org/10.2147/RMHP.S462485.
  12. Laterza, V., Marchegiani, F., Aisoni, F., Ammendola, M., Schena, C. A., Lavazza, L., Ravaioli, C., Carra, M. C., Costa, V. A., De Franceschi, A., De Simone, B. and de'Angelis, N. (2024). Smart Operating Room in Digestive Surgery: A Narrative Review, Healthcare, 12(15), 1530. https://doi.org/10.3390/healthcare12151530.
  13. Levina, A., Iliashenko, V. M., Kalyazina, S. and Overes, E. (2021). Smart Hospital Architecture: IT and Digital Aspects, Algorithms and Solutions Based on Computer Technology: 5th Scientific International Online Conference Algorithms and Solutions based on Computer Technology, Jun. 8-9, Online, pp. 235-247.
  14. May, J. M., Mejía-Mejía, E., Nomoni, M., Budidha, K., Choi, C. and Kyriacou, P. A. (2021). Effects of Contact Pressure in Reflectance Photoplethysmography in an In vitro Tissue-vessel Phantom, Sensors, 21(24), 8421. https://doi.org/10.3390/s21248421.
  15. Mirota, D. J., Ishii, M. and Hager, G. D. (2011). Vision-based Navigation in Image-guided Interventions, Annual Review of Biomedical Engineering, 13(1), 297-319. https://doi.org/10.1146/annurev-bioeng-071910-124757.
  16. Nilsson, U. G. (2013). Intraoperative Positioning of Patients under General Anesthesia and the Risk of Postoperative Pain and Pressure Ulcers, Journal of PeriAnesthesia Nursing, 28(3), 137-143. https://doi.org/10.1016/j.jopan.2012.09.006.
  17. Park, J., Cha, K. and Choi, A. (2023). 3D ResNet-based Children's Behavior Recognition Method using Video Image Sequence, Journal of Korea Society of Industrial Information Systems, 28(3), 1–10. https://doi.org/10.9723/jksiis.2023.28.3.001.
  18. Qidwai, U., Al-Sulaiti, S., Ahmed, G., Hegazy, A. and Ilyas, S. K. (2016). Intelligent Integrated Instrumentation Platform for Monitoring Long-term Bedridden Patients, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences, Dec. 4-8, Kuala Lumpur, Malaysia, pp. 561-564.
  19. Schrumpf, F., Frenzel, P., Aust, C., Osterhoff, G. and Fuchs, M. (2021). Assessment of Non-invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning, Sensors, 21(18), 6022. https://doi.org/10.3390/s21186022.
  20. Um, T. T., Pfister, F. M., Pichler, D., Endo, S., Lang, M., Hirche, S., Fietzek, U. and Kulić, D. (2017). Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring Using Convolutional Neural Networks, Proceedings of the 19th ACM International Conference on Multimodal Interaction, Nov. 13-15, Glasgow, UK, pp. 216-220.
  21. Vrbančič, G. and Podgorelec, V. (2020). Transfer Learning with Adaptive Fine-tuning, IEEE Access, 8, 196197-196211. https://doi.org/10.1109/ACCESS.2020.3034343.
  22. Zhou, C., Wang, H., Zhang, Y. and Ye, X. (2020). Study of a Ring-type Surgical Pleth Index Monitoring System Based on Flexible PPG Sensor, IEEE Sensors Journal, 21(13), 14360-14368. https://doi.org/10.1109/JSEN.2020.3041072.