Augmented Reality-Based Chronic Wound Healing Assessment Using 3D Stereoscopic Imaging and Self-Organizing Maps
Bhuneshwari Dewangan 1*, Mahendra Kumar Sahu 1
Journal of Angiotherapy 8(9) 1-7 https://doi.org/10.25163/angiotherapy.899874
Submitted: 17 July 2024 Revised: 04 September 2024 Published: 06 September 2024
This study presents a novel AR-based approach for accurate, non-invasive chronic wound evaluation, enhancing clinician-patient communication and care.
Abstract
Background: Chronic wounds (CW) present significant public health challenges, impacting individuals' well-being and daily activities. Traditional wound measurement methods, such as the ruler technique, are time-consuming, imprecise, and pose infection risks. Advances in wound care technology, including artificial intelligence (AI) and augmented reality (AR), have improved wound healing (WH) assessment accuracy and enhanced clinician-patient communication. Methods: This study developed a novel CW evaluation device combining AR and a stereoscopic camera system. The device captures images using two Leopard Imaging LI-OV580 stereo cameras and a pico-projector to create a 3D wound model. The system calculates the wound area by processing stereoscopic images, utilizing structured-from-motion techniques and convolutional neural networks (CNNs). Additionally, the device measures epipolar and projection errors (PE) during AR-based wound evaluation. A Self-Organizing Map (SOM) was employed to refine the 3D reconstruction of the wound. Results: The AR-PE analysis demonstrated the device's accuracy, with the lowest PE recorded at 20 cm from the wound area. The projection error increased as the measurement distance moved toward the edges of the wound. The average epipolar error (EE) was 0.46 pixels, indicating high precision in stereoscopic measurements. Conclusion: The study introduced a non-invasive CW evaluation device that improves wound assessment accuracy and clinician-patient interaction. By utilizing 3D wound modeling and AR, the system enables more effective communication and progress tracking during WH. The results suggest this technology could enhance CW management, offering a reliable alternative to traditional methods.
Keywords: Chronic wound healing (CWH), 3D stereoscopic imaging, Augmented Reality (AR), Wound segmentation, Self-organizing maps (SOM)
References
Barbosa, F. M., Carvalho, B. M., & Gomes, R. B. (2020, July). Accurate chronic wound area measurement using structure from motion. In 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) (pp. 208-213). IEEE.
Begum, H. M. (2022). Scientometric analysis of the research paper output on artificial intelligence: A study. Indian Journal of Information Sources and Services, 12(1), 52–58.
Biagioni, R. B., Carvalho, B. V., Manzioni, R., Matielo, M. F., Neto, F. C. B., & Sacilotto, R. (2021). Smartphone application for wound area measurement in clinical practice. Journal of Vascular Surgery Cases, Innovations and Techniques, 7(2), 258-261.
Bowers, S., & Franco, E. (2020). Chronic wounds: evaluation and management. American Family Physician, 101(3), 159-166.
Casal-Guisande, M., Comesaña-Campos, A., Cerqueiro-Pequeño, J., & Bouza-Rodríguez, J. B. (2020). Design and development of a methodology based on expert systems, applied to the treatment of pressure ulcers. Diagnostics, 10(9), 614.
Casal-Guisande, M., Comesaña-Campos, A., Cerqueiro-Pequeño, J., & Bouza-Rodríguez, J. B. (2020). Design and development of a methodology based on expert systems, applied to the treatment of pressure ulcers. Diagnostics, 10(9), 614.
Condino, S., Carbone, M., Piazza, R., Ferrari, M., & Ferrari, V. (2019). Perceptual limits of optical see-through visors for augmented reality guidance of manual tasks. IEEE Transactions on Biomedical Engineering, 67(2), 411-419.
Gurkan, A., Kirtil, I., Aydin, Y. D., & Kutuk, G. (2022). Pressure injuries in surgical patients: a comparison of Norton, Braden and Waterlow risk assessment scales. Journal of Wound Care, 31(2), 170-177.
Gwilym, B. L., Mazumdar, E., Naik, G., Tolley, T., Harding, K., & Bosanquet, D. C. (2023). Initial reduction in ulcer size as a prognostic indicator for complete wound healing: A systematic review of diabetic foot and venous leg ulcers. Advances in Wound Care, 12(6), 327-338.
Kim, P. J., Homsi, H. A., Sachdeva, M., Mufti, A., & Sibbald, R. G. (2022). Chronic wound telemedicine models before and during the COVID-19 pandemic: A scoping review. Advances in Skin & Wound Care, 35(2), 87-94.
Kodric, Z., Vrhovec, S., & Jelovcan, L. (2021). Securing edge-enabled smart healthcare systems with blockchain: A systematic literature review. Journal of Internet Services and Information Security, 11(4), 19-32.
Kodric, Z., Vrhovec, S., & Jelovcan, L. (2021). Securing edge-enabled smart healthcare systems with blockchain: A systematic literature review. Journal of Internet Services and Information Security, 11(4), 19-32.
Liu, C., Fan, X., Guo, Z., Mo, Z., Chang, E. I. C., & Xu, Y. (2019). Wound area measurement with 3D transformation and smartphone images. BMC Bioinformatics, 20(1), 1-21.
Malathi, K., Shruthi, S.N., Madhumitha, N., Sreelakshmi, S., Sathya, U., & Sangeetha, P.M. (2024). Medical data integration and interoperability through remote monitoring of healthcare devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 60-72. https://doi.org/10.58346/JOWUA.2024.I2.005
Malathi, K., Shruthi, S.N., Madhumitha, N., Sreelakshmi, S., Sathya, U., & Sangeetha, P.M. (2024). Medical data integration and interoperability through remote monitoring of healthcare devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 60-72. https://doi.org/10.58346/JOWUA.2024.I2.005
Moura, M., Dowsett, C., Bain, K., & Bain, M. (2020). Advancing practice in holistic wound management: a consensus-based call to action. Wounds International, 11(4), 70-75.
Mumtaj Begum, H. (2022). Scientometric analysis of the research paper output on artificial intelligence: A study. Indian Journal of Information Sources and Services, 12(1), 52–58.
Neelima, S., Govindaraj, M., Subramani, K., ALkhayyat, A., & Mohan, C. (2024). Factors influencing data utilization and performance of health management information systems: A case study. Indian Journal of Information Sources and Services, 14(2), 146–152. https://doi.org/10.51983/ijiss-2024.14.2.21
Neelima, S., Govindaraj, M., Subramani, K., ALkhayyat, A., & Mohan, C. (2024). Factors influencing data utilization and performance of health management information systems: A case study. Indian Journal of Information Sources and Services, 14(2), 146–152. https://doi.org/10.51983/ijiss-2024.14.2.21
Niri, R., Gutierrez, E., Douzi, H., Lucas, Y., Treuillet, S., Castañeda, B., & Hernandez, I. (2021). Multiview data augmentation to improve wound segmentation on 3D surface model by deep learning. IEEE Access, 9, 157628-157638.
Ramachandram, D., Ramirez-GarciaLuna, J. L., Fraser, R. D., Martínez-Jiménez, M. A., Arriaga-Caballero, J. E., & Allport, J. (2022). Fully automated wound tissue segmentation using deep learning on mobile devices: Cohort study. JMIR mHealth and uHealth, 10(4), e36977.
Sofiene, M., Souhaila, B., & Souhir, C. (2024). Machine learning for early diabetes detection and diagnosis. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(1), 216-230.
Surendar, A., Veerappan, S., Sadulla, S., & Arvinth, N. (2024). Lung cancer segmentation and detection using KMP algorithm. Onkologia i Radioterapia, 18(4).
Wang, C., Anisuzzaman, D. M., Williamson, V., Dhar, M. K., Rostami, B., Niezgoda, J., ... & Yu, Z. (2020). Fully automatic wound segmentation with deep convolutional neural networks. Scientific Reports, 10(1), 21897.
Wu, X., Liu, R., Yu, J., Xu, S., Yang, C., Yang, S., ... & Ye, Z. (2018). Mixed reality technology launches in orthopedic surgery for comprehensive preoperative management of complicated cervical fractures. Surgical Innovation, 25(4), 421-422.
View Dimensions
View Altmetric
Save
Citation
View
Share