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.