Deep Learning-Based Identification of Common Complication Features of Surgical Incisions

ZHAO Chunlin, HU Shiqi, HE Tingting, YUAN Linyan, YANG Xue, WANG Jing, CHEN Xiao, LIANG Zhimin, GUO Yuchen, LI

Abstract

In recent years, due to the development of accelerated recovery after surgery and day surgery in the field of surgery, the average length-of-stay of patients has been shortened and patients stay at home for post-surgical recovery and healing of the surgical incisions. In order to identify, in a timely manner, the problems that may appear at the incision site and help patients prevent or reduce the anxiety they may experience after discharge, we used deep learning method in this study to classify the features of common complications of surgical incisions, hoping to realize patient-directed early identification of complications common to surgical incisions.   Methods   A total of 1224 postoperative photographs of patients' surgical incisions were taken and collected at a tertiary-care hospital between June 2021 and March 2022. The photographs were collated and categorized according to different features of complications of the surgical incisions. Then, the photographs were divided into training, validation, and test sets at the ratio of 8∶1∶1 and 4 types of convolutional neural networks were applied in the training and testing of the models.   Results   Through the training of multiple convolutional neural networks and the testing of the model performance on the basis of a test set of 300 surgical incision images, the average accuracy of the four ResNet classification network models, SE-ResNet101, ResNet50, ResNet101, and SE-ResNet50, for surgical incision classification was 0.941, 0.903, 0.896, and 0.918, respectively, the precision was 0.939, 0.898, 0.868, and 0.903, respectively, and the recall rate was 0.930, 0.880, 0.850, and 0.894, respectively, with the SE-Resnet101 network model showing the highest average accuracy of 0.941 for incision feature classification.   Conclusion   Through the combined use of deep learning technology and images of surgical incisions, problematic features of surgical incisions can be effectively identified by examining surgical incision images. It is expected that patients will eventually be able to perform self-examination of surgical incisions on smart terminals.


Keywords: Surgical incisions, Complications, Deep learning, Image classification


 

Full Text:

PDF


References


WANG P， HUANG B， YEH C， et al. Wound healing. J Chin Med Assoc，2018，81(2): 94–101. doi: 10.1016/j.jcma.2017.11.002.

PIEPER B， SIEGGREEN M， NORDSTROM C K， et al. Discharge knowledge and concerns of patients going home with a wound. J Wound Ostomy Continence Nurs，2007，34(3): 245–253. doi: 10.1097/01.WON. 0000270817.06942.00.

SANGER P C， HARTZLER A， HAN S M， et al. Patient perspectives on post-discharge surgical site infections: towards a patient-centered mobile health solution. PLoS One，2014，9(12): e114016. doi: 10.1371/journal. pone.0114016.

TANDON S， QIN K R， NATARAJA R M， et al. Surgical wound care: a survey of parental knowledge and expectations. J Pediatr Surg，2019， 54(12): 2606–2613. doi: 10.1016/j.jpedsurg.2019.08.024.

WAHL T S， HAWN M T. How to predict 30-day readmission. Adv Surg， 2018，52(1): 101–111. doi: 10.1016/j.yasu.2018.03.015.

SREEDHARAN S， NEMETH L S， HIRSCH J， et al. Patient and provider preferences for monitoring surgical wounds using an mhealth app: a formative qualitative analysis. Surg Infect (Larchmt)，2022，23(2): 168–173. doi: 10.1089/sur.2021.240.

HILLS J， SIVAGANESAN A， KHAN I， et al. Causes and timing of unplanned 90-day readmissions following spine surgery. Spine，2018， 43(14): 991–998. doi: 10.1097/BRS.0000000000002535.

WOELBER E， SCHRICK E J， GESSNER B D， et al. Proportion of surgical site infections occurring after hospital discharge: a systematic review. Surg Infect (Larchmt)，2016，17(5): 510–519. doi: 10.1089/sur. 2015.241.

MERKOW R P， JU M H， CHUNG J W， et al. Underlying reasons associated with hospital readmission following surgery in the United States. JAMA，2015，313(5): 483–495. doi: 10.1001/jama.2014.18614.

JIANG Y， HUANG S， FU X， et al. Epidemiology of chronic cutaneous wounds in China. Wound Repair Regen，2011，19(2): 181–188. doi: 10. 1111/j.1524-475X.2010.00666.x.

HUANG Z， WU S， YU T， et al. Efficacy of telemedicine for patients with chronic wounds: a meta-analysis of randomized controlled trials. Adv Wound Care，2021，10(2): 103–112. doi: 10.1089/wound.2020.1169.

HWA K， WREN S M. Telehealth follow-up in lieu of postoperative clinic visit for ambulatory surgery: results of a pilot program. JAMA Surg， 2013，148(9): 823–827. doi: 10.1001/jamasurg.2013.2672.

GUNTER R L， FERNANDES-TAYLOR S， RAHMAN S， et al. Feasibility of an image-based mobile health protocol for postoperative wound monitoring. J Am Coll Surg，2018，226(3): 277–286. doi: 10.1016/j. jamcollsurg.2017.12.013.

HOLMES J H， SACCHI L， BELLAZZI R， et al. Artificial intelligence in medicine AIME 2015. Artif Intell Med，2017，81: 1–2. doi: 10.1016/j. artmed.2017.06.011.

WANG L， PEDERSEN P C， AGU E， et al. Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification. IEEE Trans Biomed Eng，2016，64(9): 2098–2109. doi: 10. 1109/TBME.2016.2632522.

ROSTAMI B， ANISUZZAMAN D M， WANG C， et al. Multiclass wound image classification using an ensemble deep CNN-based classifier. Comput Biol Med，2021，134: 104536. doi: 10.1016/j.compbiomed.2021. 104536.

SHENOY V N， FOSTER E， AALAMI L， et al. Deepwound: automated postoperative wound assessment and surgical site surveillance through convolutional neural networks//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid: IEEE， 2018: 1017−1021.

WU J M， TSAI C J， HO T W， et al. A unified framework for automatic detection of wound infection with artificial intelligence. Applied Sciences，2020，10(15): 5353. doi: 10.3390/app10155353.

BOWEN A C， BURNS K， TONG S Y C， et al. Standardising and assessing digital images for use in clinical trials: a practical, reproducible method that blinds the assessor to treatment allocation. PLoS One，2014， 9(11): e110395. doi: 10.1371/journal.pone.0110395.

SANDY-HODGETTS K， WATTS R. Effectiveness of negative pressure wound therapy/closed incision management in the prevention of post-surgical wound complications: a systematic review and meta-analysis. JBI Database System Rev Implement Rep，2015，13(1): 253–303. doi: 10. 11124/jbisrir-2015-1687.

HE K， ZHANG X， REN S， et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)， Las Vegas: IEEE， 2016:770−778. doi: 10.1109/CVPR.2016.90.

HU J， SHEN L， ALBANIE S， et al. Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell，2020，42(8): 2011–2023. doi: 10. 1109/TPAMI.2019.2913372.

DING S， LIN F， GILLESPIE B M. Surgical wound assessment and documentation of nurses: an integrative review. J Wound Care，2016， 25(5): 232–240. doi: 10.12968/jowc.2016.25.5.232.

SANDY-HODGETTS K， WATTS R. Effectiveness of negative pressure wound therapy/closed incision management in the prevention of post-surgical wound complications: a systematic review and meta-analysis. JBI Evid Synth，2015，13(1): 253–303. doi: 10.33699/PIS.2021.100.7.313-324.

FILKO D， MARIJANOVIĆ D， NYARKO E K. Automatic robot-driven 3D reconstruction system for chronic wounds. Sensors，2021，21(24): 8308. doi: 10.3390/s21248308.

RAMIREZ-GARCIALUNA J L， BARTLETT R， ARRIAGA-CABALLERO J E， et al. Infrared thermography in wound care, surgery, and sports medicine: a review. Front Physiol，2022，13: 210. doi: 10.3389/fphys.2022.838528.


Refbacks

  • There are currently no refbacks.