Role of Smart Image Segmentation in Infection Diagnosis and Monitoring of Acute Surgical Wounds

Abstract

Background/Purpose: There are challenges in applying artificial intelligence (AI) for acute wound healing assessment due to limited data availability, the dynamic nature of healing, and inter-patient variability. These challenges require further research on data collection, algorithm design, and validation techniques specific to each wound type. This study proposes a computer vision model for identifying acute surgical wounds using image segmentation techniques. Methods: The ACW.ai model was used to segment wound images and assign various labels to different wound features, mimicking a clinician’s perspective. The model was trained and validated using ACW.ai on a chronic wound dataset (FUSeg). Results: The ACW.ai model achieved a DICE score of 90.9%, outperforming existing methods. Additionally, the model was trained on a private dataset of 59 acute wound images, achieving a DICE score of 87.90% and a mean average precision of 82.10%. Conclusion: Image segmentation is crucial for the early detection of surgical site infections (SSIs) and facilitates better assessment of wound healing. Healthcare professionals may identify potential signs of infection and monitor healing progress by segmenting wounds and analyzing specific features, enabling them to take preventive actions and improve patient outcomes.

Keywords

• Post Operative Wound Healing
• Surgical Site Infections
• Computer Vision
• Image Segmentation

References

1. Aeffner, F., Zarella, M. D., Buchbinder, N., Bui, M. M., Goodman, M. R., Hartman, D. J., Lujan, G. M., Molani, M. A., Parwani, A. V., Lillard, K., Turner, O. C., Vemuri, V. N. P., Yuil-Valdes, A. G., & Bowman, D. (2019). Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association. Journal of Pathology Informatics, 10(1). https://doi.org/10.4103/JPI.JPI_82_18 google scholar
2. Anisuzzaman, D. M., Wang, C., Rostami, B., Gopalakrishnan, S., Niezgoda, J., & Yu, Z. (2022). Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review. Advances in Wound Care, 11(12), 687–709. https://doi.org/10.1089/WOUND.2021.0091 google scholar
3. Antonelli, L., De Simone, V., Daniela Di Serafino, ·, Di, D., & B, S.-D. (2022). A view of computational models for image segmentation. ANNALI DELL’UNIVERSITA’ DI FERRARA, 68, 277–294. https://doi.org/10.1007/s11565-022-00417-6 google scholar
4. Arbeláez, P., Maire, M., Fowlkes, C., & Malik, J. (2011). Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(5), 898–916. https://doi.org/10.1109/TPAMI.2010.161 google scholar
5. Badia, J. M., Casey, A. L., Petrosillo, N., Hudson, P. M., Mitchell, S. A., & Crosby, C. (2017). Impact of surgical site infection on healthcare costs and patient outcomes: a systematic review in six European countries. The Journal of Hospital Infection, 96(1), 1–15. https://doi.org/10.1016/J.JHIN.2017.03.004 google scholar
6. Bao, H., Sun, X., Zhang, Y., Pang, B., Li, H., Zhou, L., Wu, F., Cao, D., Wang, J., Turic, B., & Wang, L. (2020). The artificial intelligence-assisted cytology diagnostic system in large-scale cervical cancer screening: A population-based cohort study of 0.7 million women. Cancer Medicine, 9(18), 6896–6906. https://doi.org/10.1002/CAM4.3296 google scholar
7. Barakat-Johnson, M., Jones, A., Burger, M., Leong, T., Frotjold, A., Randall, S., Kim, B., Fethney, J., & Coyer, F. (2022). Reshaping wound care: Evaluation of an artificial intelligence app to improve wound assessment and management amid the COVID-19 pandemic. International Wound Journal, 19(6), 1561–1577. https://doi.org/10.1111/IWJ.13755 google scholar
8. Bawa, A., Kansal, R., Kalra, V., Rengan, V., Sundaram, P., & Ramana, B. (2023). OC-004 CAN AI HELP IN EARLY PREDICTION OF SURGICAL SITE INFECTIONS? British Journal of Surgery, 110(Supplement_2). https://doi.org/10.1093/BJS/ZNAD080.011 google scholar

9. Bradski, G. (2000). The OpenCV Library. Dr. Dobb’s Journal of Software Tools. google scholar
10. Carrión, H., Jafari, M., Bagood, M. D., Yang, H. Y., Isseroff, R. R., & Gomez, M. (2022). Automatic wound detection and size estimation using deep learning algorithms. PLOS Computational Biology, 18(3), e1009852. https://doi.org/10.1371/JOURNAL.PCBI.1009852 google scholar
11. Carvalho, V., Gonçalves, I. M., Souza, A., Souza, M. S., Bento, D., Ribeiro, J. E., Lima, R., & Pinho, D. (2021). Manual and Automatic Image Analysis Segmentation Methods for Blood Flow Studies in Microchannels. Micromachines, 12(3). https://doi.org/10.3390/MI12030317 google scholar
12. Cassini, A., Plachouras, D., Eckmanns, T., Abu Sin, M., Blank, H. P., Ducomble, T., Haller, S., Harder, T., Klingeberg, A., Sixtensson, M., Velasco, E., Weiß, B., Kramarz, P., Monnet, D. L., Kretzschmar, M. E., & Suetens, C. (2016). Burden of Six Healthcare-Associated Infections on European Population Health: Estimating Incidence-Based Disability-Adjusted Life Years through a Population Prevalence-Based Modelling Study. PLoS Medicine, 13(10). https://doi.org/10.1371/JOURNAL.PMED.1002150 google scholar
13. Chairat, S., Chaichulee, S., Dissaneewate, T., Wangkulangkul, P., & Kongpanichakul, L. (2023). AI-Assisted Assessment of Wound Tissue with Automatic Color and Measurement Calibration on Images Taken with a Smartphone. Healthcare (Basel, Switzerland), 11(2). https://doi.org/10.3390/HEALTHCARE11020273 google scholar
14. Chan, H. P., Samala, R. K., Hadjiiski, L. M., & Zhou, C. (2020). Deep Learning in Medical Image Analysis. Advances in Experimental Medicine and Biology, 1213, 3–21. https://doi.org/10.1007/978-3-030-33128-3_1 google scholar
15. Chen, J., Stanley, R. J., Moss, R. H., & Van Stoecker, W. (2003). Color analysis of skin lesion regions for melanoma discrimination in clinical images. Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI), 9(2), 94. https://doi.org/10.1034/J.1600-0846.2003.00024.X google scholar
16. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184 google scholar
17. Counseller, Q., & Aboelkassem, Y. (2023). Recent technologies in cardiac imaging. Frontiers in Medical Technology, 4, 99. https://doi.org/10.3389/FMEDT.2022.984492 google scholar
18. Curti, N., Merli, Y., Zengarini, C., Giampieri, E., Merlotti, A., Dall’Olio, D., Marcelli, E., Bianchi, T., & Castellani, G. (2023). Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation. International Journal of Molecular Sciences, 24(1), 706. https://doi.org/10.3390/IJMS24010706/S1 google scholar
19. Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. (2021). Skin Cancer Detection: A Review Using Deep Learning Techniques. International Journal of Environmental Research and Public Health, 18(10). https://doi.org/10.3390/IJERPH18105479 google scholar
20. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (n.d.). Rich feature hierarchies for accurate object detection and semantic segmentation Tech report (v5). Retrieved May 28, 2023, from http://www.cs.berkeley.edu/˜rbg/rcnn. google scholar
21. Glinos, G. D., Verne, S. H., Aldahan, A. S., Liang, L., Nouri, K., Elliot, S., Glassberg, M., Cabrera DeBuc, D., Koru-Sengul, T., Tomic-Canic, M., & Pastar, I. (2017). Optical coherence tomography for assessment of epithelialization in a human ex vivo wound model. Wound Repair and Regeneration, 25(6), 1017–1026. https://doi.org/10.1111/WRR.12600 google scholar
22. Gosling, R. C., & Al-Mohammad, A. (2022). The Role of Cardiac Imaging in Heart Failure with Reduced Ejection Fraction. Cardiac Failure Review, 8. https://doi.org/10.15420/CFR.2021.33 google scholar
23. Gruosso, M., Capece, N., & Erra, U. (2021). Human segmentation in surveillance video with deep learning. Multimedia Tools and Applications, 80(1), 1175–1199. https://doi.org/10.1007/S11042-020-09425-0/FIGURES/11 google scholar
24. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. https://doi.org/10.1038/S41586-020-2649-2 google scholar
25. He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 386–397. https://doi.org/10.1109/TPAMI.2018.2844175 google scholar
26. Horská, A., & Barker, P. B. (2010). Imaging of Brain Tumors: MR Spectroscopy and Metabolic Imaging. Neuroimaging Clinics of North America, 20(3), 293. https://doi.org/10.1016/J.NIC.2010.04.003 google scholar
27. Howell, R. S., Liu, H. H., Khan, A. A., Woods, J. S., Lin, L. J., Saxena, M., Saxena, H., Castellano, M., Petrone, P., Slone, E., Chiu, E. S., Gillette, B. M., & Gorenstein, S. A. (2021). Development of a Method for Clinical Evaluation of Artificial Intelligence–Based Digital Wound Assessment Tools. JAMA Network Open, 4(5), e217234–e217234. https://doi.org/10.1001/JAMANETWORKOPEN.2021.7234 google scholar
28. Hughes, C. M. L., Jeffers, A., Sethuraman, A., Klum, M., Tan, M., & Tan, V. (2023). The detection and prediction of surgical site infections using multi-modal sensors and machine learning: Results in an animal model. Frontiers in Medical Technology, 5, 24. https://doi.org/10.3389/FMEDT.2023.1111859 google scholar
29. Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. https://doi.org/10.1109/MCSE.2007.55 google scholar
30. Jin, C., Udupa, J. K., Zhao, L., Tong, Y., Odhner, D., Pednekar, G., Nag, S., Lewis, S., Poole, N., Mannikeri, S., Govindasamy, S., Singh, A., Camaratta, J., Owens, S., & Torigian, D. A. (2022). Object recognition in medical images via anatomy-guided deep learning. Medical Image Analysis, 81. https://doi.org/10.1016/J.MEDIA.2022.102527 google scholar
31. Kabir, Y., Dojat, M., Scherrer, B., Forbes, F., & Garbay, C. (2007). Multimodal MRI segmentation of ischemic stroke lesions. Conference Proceedings, 2007, 1595. https://doi.org/10.1109/IEMBS.2007.4352610 google scholar
32. Kabiraj, A., Pal, D., Ganguly, D., Chatterjee, K., & Roy, S. (2022). Number plate recognition from enhanced super-resolution using generative adversarial network. Multimedia Tools and Applications, 82(9), 13837–13853. https://doi.org/10.1007/S11042-022-14018-0/TABLES/5 google scholar
33. Khan, M. A., Akram, T., Sharif, M., Shahzad, A., Aurangzeb, K., Alhussein, M., Haider, S. I., & Altamrah, A. (2018). An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification. BMC Cancer, 18(1), 1–20. https://doi.org/10.1186/S12885-018-4465-8/TABLES/16 google scholar
34. Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2022). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 1, 1. https://doi.org/10.1007/S12652-021-03612-Z google scholar
35. Landau, M. S., & Pantanowitz, L. (2019). Artificial intelligence in cytopathology: a review of the literature and overview of commercial landscape. Journal of the American Society of Cytopathology, 8(4), 230–241. https://doi.org/10.1016/J.JASC.2019.03.003 google scholar
36. Linguraru, M. G., Pura, J. A., Chowdhury, A. S., & Summers, R. M. (2010). Multi-Organ Segmentation from Multi-Phase Abdominal CT via 4D Graphs using Enhancement, Shape and Location Optimization. Medical Image Computing and Computer-Assisted Intervention : MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention, 13(Pt 3), 89. https://doi.org/10.1007/978-3-642-15711-0_12 google scholar
37. Lucas, Y., Niri, R., Treuillet, S., Douzi, H., & Castaneda, B. (2021). Wound Size Imaging: Ready for Smart Assessment and Monitoring. Advances in Wound Care, 10(11), 641. https://doi.org/10.1089/WOUND.2018.0937 google scholar
38. Malahfji, M., & Chamsi-Pasha, M. A. (2019). Advanced Cardiac Imaging for Complex Adult Congenital Heart Diseases. Methodist DeBakey Cardiovascular Journal, 15(2), 99. https://doi.org/10.14797/MDCJ-15-2-99 google scholar
39. Mohd Safuan, S. N., Md Tomari, M. R., & Wan Zakaria, W. N. (2018). White blood cell (WBC) counting analysis in blood smear images using various color segmentation methods. Measurement, 116, 543–555. https://doi.org/10.1016/J.MEASUREMENT.2017.11.002 google scholar
40. Mukherjee, R., Manohar, D. D., Das, D. K., Achar, A., Mitra, A., & Chakraborty, C. (2014a). Automated Tissue Classification Framework for Reproducible Chronic Wound Assessment. BioMed Research International, 2014. https://doi.org/10.1155/2014/851582 google scholar
41. Mukherjee, R., Manohar, D. D., Das, D. K., Achar, A., Mitra, A., & Chakraborty, C. (2014b). Automated tissue classification framework for reproducible chronic wound assessment. BioMed Research International, 2014. https://doi.org/10.1155/2014/851582 google scholar
42. Owens, C. D., & Stoessel, K. (2008). Surgical site infections: epidemiology, microbiology and prevention. The Journal of Hospital Infection, 70 Suppl 2(SUPPL. 2), 3–10. https://doi.org/10.1016/S0195-6701(08)60017-1 google scholar
43. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury Google, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Xamla, A. K., Yang, E., Devito, Z., Raison Nabla, M., Tejani, A., Chilamkurthy, S., Ai, Q., Steiner, B., … Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. google scholar
44. Patel, A., Schreuder, F. H. B. M., Klijn, C. J. M., Prokop, M., Ginneken, B. van, Marquering, H. A., Roos, Y. B. W. E. M., Baharoglu, M. I., Meijer, F. J. A., & Manniesing, R. (2019). Intracerebral Haemorrhage Segmentation in Non-Contrast CT. Scientific Reports 2019 9:1, 9(1), 1–11. https://doi.org/10.1038/s41598-019-54491-6 google scholar
45. Prinyakupt, J., & Pluempitiwiriyawej, C. (2015). Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers. BioMedical Engineering OnLine, 14(1). https://doi.org/10.1186/S12938-015-0037-1 google scholar
46. Privalov, M., Beisemann, N., Barbari, J. El, Mandelka, E., Müller, M., Syrek, H., Grützner, P. A., & Vetter, S. Y. (2021). Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study. Journal of Digital Imaging, 34(4), 788. https://doi.org/10.1007/S10278-021-00490-X google scholar
47. Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/S41591-021-01614-0 google scholar
48. Ramachandram, D., Ramirez-GarciaLuna, J. L., Fraser, R. D. J., Martínez-Jiménez, M. A., Arriaga-Caballero, J. E., & Allport, J. (2022a). Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study. JMIR MHealth and UHealth, 10(4). https://doi.org/10.2196/36977 google scholar
49. Ramachandram, D., Ramirez-GarciaLuna, J. L., Fraser, R. D. J., Martínez-Jiménez, M. A., Arriaga-Caballero, J. E., & Allport, J. (2022b). Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study. JMIR MHealth and UHealth, 10(4). https://doi.org/10.2196/36977 google scholar
50. Ravi, N., Gabeur, V., Hu, Y. T., Hu, R., Ryali, C., Ma, T., … & Feichtenhofer, C. (2024). Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714. google scholar
51. Scala, A., Loperto, I., Triassi, M., & Improta, G. (2022). Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study. International Journal of Environmental Research and Public Health, 19(16), 10021. https://doi.org/10.3390/IJERPH191610021 google scholar
52. Sharma, N., Ray, A. K., Shukla, K. K., Sharma, S., Pradhan, S., Srivastva, A., & Aggarwal, L. (2010). Automated medical image segmentation techniques. Journal of Medical Physics / Association of Medical Physicists of India, 35(1), 3. https://doi.org/10.4103/0971-6203.58777 google scholar
53. Shelhamer, E., Long, J., & Darrell, T. (2014). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640–651. https://doi.org/10.1109/TPAMI.2016.2572683 google scholar
54. Shen, F., Larson, D. W., Naessens, J. M., Habermann, E. B., Liu, H., & Sohn, S. (2019). Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes. Journal of Healthcare Informatics Research, 3(3), 267. https://doi.org/10.1007/S41666-018-0042-9 google scholar
55. Tiwari, A., Srivastava, S., & Pant, M. (2020). Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters, 131, 244–260. https://doi.org/10.1016/J.PATREC.2019.11.020 google scholar
56. Tomsic, I., Heinze, N. R., Chaberny, I. F., Krauth, C., Schock, B., & Von Lengerke, T. (2020). Implementation interventions in preventing surgical site infections in abdominal surgery: a systematic review. BMC Health Services Research, 20(1). https://doi.org/10.1186/S12913-020-4995-Z google scholar
57. Ullman, S. (2007). Object recognition and segmentation by a fragment-based hierarchy. Trends in Cognitive Sciences, 11(2), 58–64. https://doi.org/10.1016/J.TICS.2006.11.009 google scholar
58. Vu, Q. D., Graham, S., Kurc, T., To, M. N. N., Shaban, M., Qaiser, T., Koohbanani, N. A., Khurram, S. A., Kalpathy-Cramer, J., Zhao, T., Gupta, R., Kwak, J. T., Rajpoot, N., Saltz, J., & Farahani, K. (2019). Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Frontiers in Bioengineering and Biotechnology, 7(APR), 53. https://doi.org/10.3389/FBIOE.2019.00053 google scholar
59. Wagh, A., Jain, S., Mukherjee, A., Agu, E., Pedersen, P. C., Strong, D., Tulu, B., Lindsay, C., & Liu, Z. (2020). Semantic Segmentation of Smartphone Wound Images: Comparative Analysis of AHRF and CNN-Based Approaches. IEEE Access : Practical Innovations, Open Solutions, 8, 181590. https://doi.org/10.1109/ACCESS.2020.3014175 google scholar
60. Wang, C., Mahbod, A., Ellinger, I., Galdran, A., Gopalakrishnan, S., Niezgoda, J., & Yu, Z. (2022). FUSeg: The Foot Ulcer Segmentation Challenge. https://arxiv.org/abs/2201.00414v1 google scholar
61. Wang, X., Zhang, R., Shen, C., Kong, T., & Li, L. (2021). SOLO: A Simple Framework for Instance Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11), 8587–8601. https://doi.org/10.1109/TPAMI.2021.3111116 google scholar
62. Wang, Y., Freeman, A., Ajjan, R., Del Galdo, F., & Tiganescu, A. (2022). Automated quantification of 3D wound morphology by machine learning and optical coherence tomography in type 2 diabetes. Skin Health and Disease, e203. https://doi.org/10.1002/SKI2.203 google scholar
63. Weng, W., & Zhu, X. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. IEEE Access, 9, 16591–16603. https://doi.org/10.1109/ACCESS.2021.3053408 google scholar
64. Wienert, S., Heim, D., Saeger, K., Stenzinger, A., Beil, M., Hufnagl, P., Dietel, M., Denkert, C., & Klauschen, F. (2012). Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach. Scientific Reports 2012 2:1, 2(1), 1–7. https://doi.org/10.1038/srep00503 google scholar
65. Wu, J. M., Tsai, C. J., Ho, T. W., Lai, F., Tai, H. C., & Lin, M. T. (2020). A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence. Applied Sciences 2020, Vol. 10, Page 5353, 10(15), 5353. https://doi.org/10.3390/APP10155353 google scholar
66. Xu, J., Zhang, R., Zhou, Z., Wu, C., Gong, Q., Zhang, H., Wu, S., Wu, G., Deng, Y., Xia, C., & Ma, J. (2021). Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT. Frontiers in Neuroscience, 14, 1084. https://doi.org/10.3389/FNINS.2020.541817/BIBTEX google scholar
67. Zhang, Z., & Sejdić, E. (2019). Radiological images and machine learning: trends, perspectives, and prospects. Computers in Biology and Medicine, 108, 354. https://doi.org/10.1016/J.COMPBIOMED.2019.02.017 google scholar