Towards intelligent supervision of operating rooms using stencil-based character recognition
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Hernández-Aceituno, Javier
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Méndez-Pérez, Juan Albino
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González-Cava, José M.
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Reboso-Morales, José Antonio
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1
Universidad de La Laguna
info
ISSN: 0010-4825
Año de publicación: 2023
Volumen: 162
Páginas: 107071
Tipo: Artículo
Otras publicaciones en: Computers in Biology and Medicine
Resumen
The development of intelligent operating rooms is an example of a cyber–physical system resulting from the symbiosis of Industry 4.0 and medicine. A problem with this type of systems is that it requires demanding solutions that allow the real time acquisition of heterogeneous data in an efficient way. The aim of the presented work is the development of a data acquisition system, based on a real-time artificial vision algorithm which can capture information from different clinical monitors. The system was designed for the registration, pre-processing, and communication of clinical data recorded in an operating room. The methods for this proposal are based on a mobile device running a Unity application, which extracts information from clinical monitors and transmits the data to a supervision system through a wireless Bluetooth connection. The software implements a character detection algorithm and allows online correction of identified outliers. The results validate the system with real data obtained during surgical interventions, where only 0.42% values were missed and 0.89% were misread. The outlier detection algorithm was able to correct all the reading errors. In conclusion, the development of a low-cost compact solution to supervise operating rooms in real-time, collecting visual information non-intrusively and communicating data wirelessly, can be a very useful tool to overcome the lack of expensive data recording and processing technology in many clinical situations. The acquisition and pre-processing method presented in this article constitutes a key element towards the development of a cyber–physical system for the development of intelligent operating rooms.
Referencias bibliográficas
- Sony, (2022), Hosp. Top., pp. 1, 10.1080/00185868.2022.2048220
- Aceto, (2020), J. Ind. Inf. Integr., 18
- Inuwa, (2022), Mater. Today: Proc., 62, pp. 3593
- McInerney, (2022), Stud. Health Technol. Inform., 290, pp. 364
- Salim, (2022), IEEE J. Biomed. Health Inf., pp. 1
- Rehman, (2022), Comput. Biol. Med., 150, 10.1016/j.compbiomed.2022.106019
- Gonzalez-Cava, (2020), Comput. Biol. Med., 118, 10.1016/j.compbiomed.2020.103645
- Jove, (2021), Logic J. IGPL, 29, pp. 193, 10.1093/jigpal/jzaa046
- Gonzalez-Cava, (2018), Complexity, 2018, pp. 1, 10.1155/2018/9012720
- Ahmad, (2022), Comput. Biol. Med., 145, 10.1016/j.compbiomed.2022.105418
- Catarinucci, (2015), IEEE Internet Things J., 2, pp. 515, 10.1109/JIOT.2015.2417684
- Kuo, (2022), Emerg. Med. Int., 2022, pp. 1, 10.1155/2022/2177548
- M.A. Mohammed, A. Lakhan, D.A. Zebari, K.H. Abdulkareem, J. Nedoma, R. Martinek, U. Tariq, M. Alhaisoni, P. Tiwari, Adaptive secure malware efficient machine learning algorithm for healthcare data, CAAI Trans. Intell. Technol. http://dx.doi.org/10.1049/cit2.12200, URL.
- Sarra, (2022), Diagnostics, 12, pp. 2899, 10.3390/diagnostics12122899
- Lakhan, (2022), IEEE Trans. Netw. Sci. Eng., pp. 1, 10.1109/TNSE.2022.3213651
- Pham, (2020), IEEE Access, 8, pp. 130820, 10.1109/ACCESS.2020.3009328
- Chng, (2019), pp. 47
- Okamoto, (2018), Biomed. Eng. / Biomed. Tech., 63, pp. 31, 10.1515/bmt-2017-0006
- Zhang, (2022), J. Healthc. Eng., 2022, pp. 1, 10.1155/2022/4114178
- Birkhoff, (2021), Surg. Innov., 28, pp. 611, 10.1177/1553350621996961
- Bellini, (2020), J. Med. Syst., 44, pp. 20, 10.1007/s10916-019-1512-1
- Reboso, (2019), Minerva Anestesiol., 85, pp. 585, 10.23736/S0375-9393.18.13058-6
- Mendez, (2018), Artif. Intell. Med., 84, pp. 159, 10.1016/j.artmed.2017.12.005
- Tanimoto, (1958)
- R. Smith, An Overview of the Tesseract OCR Engine, in: Proc. Ninth Int. Conference on Document Analysis and Recognition, ICDAR, 2007, pp. 629–633.