Medical image video recorder with computer vision and face blurring
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1297Keywords:
Artificial Intelligence, Video Recording, Laparoscopic SurgeryAbstract
Modern solutions for recording medical procedures represent cutting-edge technology that is still emerging and facing challenges. This paper presents the Life Surgery Box, a Brazilian standalone multi-modal and synchronized image video recorder. Objective: presenting the development and prototyping of the equipment, intended for use in both operating rooms and medical offices. Method: involves the description of its hardware and software architectures, with a focus on an artificial intelligence-based face-blurring algorithm. Results: highlight the performance optimizations for efficient video processing and the artifacts generated by the equipment. Conclusion: the proposed solution exemplifies technological advancements and stands as an innovative contribution to healthcare technology.
References
Scherer L A, Chang M C, Meredith J W, Battistella F D. Videotape review leads to rapid and sustained learning. Am J Surg. 2003;185(6):516–20. https://doi.org/10.1016/S0002-9610(03)00062-X.
Bonrath E M, Gordon L E, Grantcharov T P. Characterising ‘near miss’ events in complex laparoscopic surgery through video analysis. BMJ Qual Saf. 2015; 24(8):516–21. https://doi.org/10.1136/bmjqs-2014-003816.
Hu Y Y, Peyre S E, Arriaga A F, et al. Postgame analysis: using video-based coaching for continuous professional development. J Am Coll Surg. 2012; 214(1):115–24. https://doi.org/10.1016/j.jamcollsurg.2011.10.009.
Bogen E M, Augestad K M, Patel H R, Lindsetmo R O. Telementoring in education of laparoscopic surgeons: An emerging technology. World J Gastrointest Endosc. 2014;6(5):148–55. https://doi.org/10.4253/wjge.v6.i5.148.
Møller K E, Sørensen J L, Topperzer M K, Koerner C, Ottesen B, Rosendahl M, Grantcharov T, Strandbygaard J. Implementation of an Innovative Technology Called
the OR Black Box: A Feasibility Study. Surg Innov. 2023;30(1):64-72. https://doi.org/10.1177/15533506221106258.
Rex D K, Hewett D G, Raghavendra M, Chalasani N. The impact of videorecording on the quality of colonoscopy performance: a pilot study. Am J Gastroenterol. 2010;105(11):2312-7. https://doi.org/10.1038/ajg.2010.245.
Bergström H, Larsson L G, Stenberg E. Audio-video recording during laparoscopic surgery reduces irrelevant conversation between surgeons: a cohort study. BMC Surg. 2018;18(1):92. https://doi.org/10.1186/s12893-018-0428-x.
Silas M R, Grassia P, Langerman A. Video recording of the operating room--is anonymity possible? J Surg Res. 2015;197(2):272-6. https://doi.org/10.1016/j.jss.2015.03.097.
International Electrotechnical Commission. IEC 60601-1: Medical electrical equipment - Part 1: General requirements for basic safety and essential performance. Revision 3.2. August 2020.
Minaee S, Luo P, Lin Z, Bowyer K. Going Deeper Into Face Detection: A Survey. 2021. ArXiv. /abs/2103.14983.
Feng Y, Yu S, Peng H, Li Y, Zhang J. Detect Faces Efficiently: A Survey and Evaluations. 2021. ArXiv. https://doi.org/10.1109/TBIOM.2021.3120412.
Deng J, Guo J, Zhou Y, Yu J, Kotsia I, Zafeiriou S. RetinaFace: Single-stage Dense Face Localisation in the Wild. 2019. ArXiv. /abs/1905.00641.
RetinaFace in PyTorch. https://github.com/biubug6/Pytorch_Retinaface.
Yang S, Luo P, Loy C C, Tang X. WIDER FACE: A Face Detection Benchmark. 2015. ArXiv. /abs/1511.06523.
Howard A G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M,
Adam H. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. 2017. ArXiv. /abs/1704.04861.
Zhu Y, Cai H, Zhang S, Wang C, Xiong Y. TinaFace: Strong but Simple Baseline for Face Detection. 2020. ArXiv. /abs/2011.13183.
Liu Y, Tang X, Wu X, Han J, Liu J, Ding E. HAMBox: Delving into Online High-quality Anchors Mining for Detecting Outer Faces. 2019. ArXiv. /abs/1912.09231.
Li J, Wang Y, Wang C, Tai Y, Qian J, Yang J, Wang C, Li J, Huang F. DSFD: Dual Shot Face Detector. 2018. ArXiv. /abs/1810.10220.
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2015.
ArXiv. /abs/1512.03385.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Submission of a paper to Journal of Health Informatics is understood to imply that it is not being considered for publication elsewhere and that the author(s) permission to publish his/her (their) article(s) in this Journal implies the exclusive authorization of the publishers to deal with all issues concerning the copyright therein. Upon the submission of an article, authors will be asked to sign a Copyright Notice. Acceptance of the agreement will ensure the widest possible dissemination of information. An e-mail will be sent to the corresponding author confirming receipt of the manuscript and acceptance of the agreement.