臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：人工智慧輔助之遠距傷口照護決策支援系統
作者：張惇皓
作者(外文)：Chang, Dun-Hao
校院名稱：元智大學
系所名稱：資訊管理學系
指導教授：詹前隆
學位類別：博士
出版日期：2024
主題關鍵詞：遠距醫療；傷口照護；人工智慧；決策支援系統；Telemedicine；Wound care；Artificial intelligence；Clinical decision support system
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:2
慢性傷口的照護因為漫長的癒合期，對於照護者或是醫療系統造成不小的挑
戰和負擔。這些慢性傷口的病患，如褥瘡或是糖尿病潰瘍，往往因為行動不便而
無法即時的獲取醫療資源。遠距醫療雖然可以彌補這些照護上的不足，但現行的
遠距醫療也受到特定時間和區域的限制，使得普及性不高，也無法適用到每一個
居家照護的環境。
因此我們目標要開發一個人工智慧輔助的臨床決策支援系統，來提供自動的
傷口判斷和相關的照護建議。在此研究中，我們由傷口影像處理著手，經由醫師
標記的傷口照片，建立深度學習的模組，達成傷口的擷取、組織分類的判斷及傷
口測量。結合其他照護者所輸入的資訊，如傷口滲液、氣味、周邊皮膚情況等，
醫師由此所編寫而成的照護建議形成一決策支援系統。最後將此系統進一步結合
雲端平台，讓病患或照護者可上傳傷口照片，並在醫師或是傷口照護師的監管之
下，得到系統分析的傷口照護建議和回饋。
建立完成的系統名為 Ulcer Care。我們在慢性傷口的病患進行臨床的隨機分
派研究。在臨床試驗的初始報告中，我們發現使用遠距傷口照護的病患和一般傷
口照護組有相似的傷口癒合率，但有較少的就醫次數，照護者也有較高的生活品
質。人工智慧模組的表現雖不如訓練集資料優異，但未來仍可經由多元資料訓
練、開發訓練新的模組來增加準確率。我們期待此系統未來有更多的臨床應用。
Management of chronic wounds is a challenge and burden for both caregivers and healthcare systems because the healing process is time consuming with high cost. Patients with chronic wounds, such as pressure ulcers or diabetic foot ulcers are usually disabled and have difficulties in seeking medical resources. Telemedical wound care can fulfil this unmet need. However, current telemedicine has certain time and region limitsand has not been widely available for every home care setting.
Therefore, we aim to develop an artificial intelligence assisted clinical decision support system (AI-CDSS) to provide automatic wound assessment and relevant wound care recommendations. In this study, we started from wound image processing. Based on doctor labeled images, we trained the deep learning models to perform wound segmentation, tissue classification and wound measurement. In combination with other wound information input by caregivers, such as wound discharge, smell and peri-wound conditions, the wound care recommendations could be developed by the doctors, forming a decision support system. Finally, the AI-CDSS would be connected to a cloud platform, allowing the patients or caregivers to upload the wound images and obtain feedback from the AI-CDSS under the supervision of doctors or wound specialists.
The established AI-CDSS telemedical platform named “Ulcer Care” (UC). We
vconducted a clinical randomized control trial, comparing the UC users with standard care in chronic wound patients. In our preliminary report, we found the UC users had similar wound healing rate but fewer clinic visits, and their caregivers had better quality of life. Although the AI system couldn’t achieve the same performance as the training dataset, it cab be improved by diverse data training with new deep learning models. We are looking forward to more clinical application with UC.
1. Cuddigan, J. and R.A. Frantz, Pressure ulcer research: pressure ulcer treatment. A monograph from the National Pressure Ulcer Advisory Panel. Adv Wound Care, 1998. 11(6): p. 294-300; quiz 302.
2. Garcia-Sanchez, F.J., V. Martinez-Vizcaino, and B. Rodriguez-Martin, Patients' and Caregivers' Conceptualisations of Pressure Ulcers and the Process of Decision-Making in the Context of Home Care. Int J Environ Res Public Health, 2019. 16(15).
3. Rodrigues, A.M., P.L. Ferreira, and C. Ferre-Grau, Providing informal home care for pressure ulcer patients: how it affects carers' quality of life and burden.J Clin Nurs, 2016. 25(19-20): p. 3026-35.
4. Arora, M., et al., Effectiveness and cost-effectiveness of telephone-based support versus usual care for treatment of pressure ulcers in people with spinal cord injury in low-income and middle-income countries: study protocol for a 12-week randomised controlled trial. BMJ Open, 2015. 5(7): p. e008369.
5. Sood, A., et al., The Role of Telemedicine in Wound Care: A Review and Analysis of a Database of 5,795 Patients from a Mobile Wound-Healing Center in Languedoc-Roussillon, France. Plast Reconstr Surg, 2016. 138(3 Suppl): p. 248S-256S.
6. Mahoney, M.F., Telehealth, Telemedicine, and Related Technologic Platforms: Current Practice and Response to the COVID-19 Pandemic. J Wound Ostomy Continence Nurs, 2020. 47(5): p. 439-444.
7. 洪啟盛, 遠距醫療在偏鄉醫療照顧的應用. 台灣醫學, 2021. 25(5): p. 634-642.
8. Moore, Z., et al., TIME CDST: an updated tool to address the current challenges in wound care. J Wound Care, 2019. 28(3): p. 154-161.
9. Marijanović, D. and D. Filko, A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis. Applied Sciences, 2020. 10(21).
10. Zahia, S., et al., Pressure injury image analysis with machine learning techniques: A systematic review on previous and possible future methods. Artif Intell Med, 2020. 102: p. 101742.
11. Li, F., et al., A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks. Comput Intell Neurosci, 2018. 2018: p. 4149103.
12. Lucas, Y., et al., Wound Size Imaging: Ready for Smart Assessment and Monitoring. Adv Wound Care (New Rochelle), 2021. 10(11): p. 641-661.
13. Chang, C.W., et al., Deep learning approach based on superpixel segmentation assisted labeling for automatic pressure ulcer diagnosis. PLoS One, 2022. 17(2): p. e0264139.
14. Filko, D., D. Antonić, and D. Huljev. WITA — Application for wound analysis and management. in The 12th IEEE International Conference on e-Health Networking, Applications and Services. 2010.
15. Lu, L., et al., Deep learning and convolutional neural networks for medical image computing. Advances in computer vision and pattern recognition, 2017. 10: p. 978-3.
16. Garcia-Zapirain, B., et al., Classification of pressure ulcer tissues with 3D convolutional neural network. Med Biol Eng Comput, 2018. 56(12): p. 2245-2258.
17. Ohura, N., et al., Convolutional neural networks for wound detection: the role of artificial intelligence in wound care. J Wound Care, 2019. 28(Sup10): p. S13-s24.
18. Zahia, S., B. Garcia-Zapirain, and A. Elmaghraby, Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning. Sensors (Basel), 2020. 20(10).
19. Anderson, J.A. and P. Willson, Clinical decision support systems in nursing: synthesis of the science for evidence-based practice. Comput Inform Nurs, 2008. 26(3): p. 151-8.
20. Mebrahtu, T.F., et al., Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes: a systematic review of experimental and observational studies. BMJ Open, 2021. 11(12): p. e053886.
21. Fossum, M., et al., An evaluation of the usability of a computerized decision support system for nursing homes. Appl Clin Inform, 2011. 2(4): p. 420-36.
22. Schaarup, C., L.B. Pape-Haugaard, and O.K. Hejlesen, Models Used in Clinical Decision Support Systems Supporting Healthcare Professionals Treating Chronic Wounds: Systematic Literature Review. JMIR Diabetes, 2018. 3(2): p. e11.
23. Khong, P., L. Lee, and A. Dawang, Modeling the Construct of an Expert Evidence-Adaptive Knowledge Base for a Pressure Injury Clinical Decision Support System. Informatics, 2017. 4(3).
24. Khong, P.C., et al., Nurses' Clinical Decision Making on Adopting a Wound Clinical Decision Support System. Comput Inform Nurs, 2015. 33(7): p. 295-305.
25. Tsai, H.H., et al., Teleconsultation by using the mobile camera phone for remote management of the extremity wound: a pilot study. Ann Plast Surg, 2004. 53(6): p. 584-7.
26. Chen, C.H., et al., Patient-centered wound teleconsultation for cutaneous wounds: a feasibility study. Ann Plast Surg, 2014. 72(2): p. 220-4.
27. Pak, C., et al., A smartphone-based teleconsultation system for the management of chronic pressure injuries. Wound Repair Regen, 2018. 26 Suppl 1: p. S19-S26.
28. Chang, C.W., et al., Deep Learning-Assisted Burn Wound Diagnosis: Diagnostic Model Development Study. JMIR Med Inform, 2021. 9(12): p. e22798.
29. Nguyen, D.-k., et al., A Deep Learning Model for Wound Size Measurement Using Fingernails, in Proceedings of the 6th International Conference on Medical and Health Informatics. 2022, Association for Computing Machinery: Virtual Event, Japan. p. 141–146.
30. Uijlings, J., et al., Selective Search for Object Recognition. International Journal of Computer Vision, 2013. 104(2): p. 154-171.
31. Bochkovskiy, A., C.-Y. Wang, and H.-y. Liao, YOLOv4: Optimal Speed and Accuracy of Object Detection. 2020.
32. Thompson, N., et al., Reliability and validity of the revised photographic wound assessment tool on digital images taken of various types of chronic wounds. Adv Skin Wound Care, 2013. 26(8): p. 360-73.
33. Gorecki, C., et al., Development and validation of a new patient-reported outcome measure for patients with pressure ulcers: the PU-QOL instrument. Health Qual Life Outcomes, 2013. 11: p. 95.
34. Basra, M.K., R. Sue-Ho, and A.Y. Finlay, The Family Dermatology Life Quality Index: measuring the secondary impact of skin disease. Br J Dermatol, 2007. 156(3): p. 528-38.
35. Stoyanov, S.R., et al., Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth, 2015. 3(1): p. e27.
36. Chen, L., et al., Telemedicine in Chronic Wound Management: Systematic Review And Meta-Analysis. JMIR Mhealth Uhealth, 2020. 8(6): p. e15574.
37. Anisuzzaman, D.M., et al., Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review. Adv Wound Care (New Rochelle), 2021.
38. Jiang, S.W., et al., Quality and Perceived Usefulness of Patient-Submitted Storeand-Forward Teledermatology Images. JAMA Dermatol, 2022. 158(10): p. 1183-1186.
39. Grzybowski, A., K. Jin, and H. Wu, Challenges of Artificial Intelligence in Medicine and Dermatology. Clin Dermatol, 2024.
40. Scheibner, J., et al., Revolutionizing Medical Data Sharing Using Advanced Privacy-Enhancing Technologies: Technical, Legal, and Ethical Synthesis. J Med Internet Res, 2021. 23(2): p. e25120.
41. Huang, Y., et al., Investigation of the status and influence factors of caregiver's quality of life on caring for patients with chronic wound during COVID-19 epidemic. Int Wound J, 2021. 18(4): p. 440-447.
42. Ferreira, F., et al., A Systematic Investigation of Models for Color Image Processing in Wound Size Estimation. Computers, 2021. 10(4): p. 43.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：1375（臺灣：1340，外國：35） ／ 本年度總使用人次：2516201 ／ 訪客人次（自102年09月）：68322386
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上