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Srivastava GK, Martinez-Rodriguez S, Md Fadilah NI, Looi Qi Hao D, Markey G, Shukla P, Fauzi MB, Panetsos F. Progress in Wound-Healing Products Based on Natural Compounds, Stem Cells, and MicroRNA-Based Biopolymers in the European, USA, and Asian Markets: Opportunities, Barriers, and Regulatory Issues. Polymers (Basel) 2024; 16:1280. [PMID: 38732749 PMCID: PMC11085499 DOI: 10.3390/polym16091280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/22/2024] [Accepted: 04/02/2024] [Indexed: 05/13/2024] Open
Abstract
Wounds are breaks in the continuity of the skin and underlying tissues, resulting from external causes such as cuts, blows, impacts, or surgical interventions. Countless individuals suffer minor to severe injuries, with unfortunate cases even leading to death. In today's scenario, several commercial products are available to facilitate the healing process of wounds, although chronic wounds still present more challenges than acute wounds. Nevertheless, the huge demand for wound-care products within the healthcare sector has given rise to a rapidly growing market, fostering continuous research and development endeavors for innovative wound-healing solutions. Today, there are many commercially available products including those based on natural biopolymers, stem cells, and microRNAs that promote healing from wounds. This article explores the recent breakthroughs in wound-healing products that harness the potential of natural biopolymers, stem cells, and microRNAs. A comprehensive exploration is undertaken, covering not only commercially available products but also those still in the research phase. Additionally, we provide a thorough examination of the opportunities, obstacles, and regulatory considerations influencing the potential commercialization of wound-healing products across the diverse markets of Europe, America, and Asia.
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Affiliation(s)
- Girish K. Srivastava
- Departamento de Cirugía, Oftalmología, Otorrinolaringología y Fisioterapia, Facultad de Medicina, Universidad de Valladolid, 47005 Valladolid, Spain;
- Instituto Universitario de Oftalmobiología Aplicada, Facultad de Medicina, Universidad de Valladolid, 47011 Valladolid, Spain;
| | - Sofia Martinez-Rodriguez
- Instituto Universitario de Oftalmobiología Aplicada, Facultad de Medicina, Universidad de Valladolid, 47011 Valladolid, Spain;
| | - Nur Izzah Md Fadilah
- Centre for Tissue Engineering and Regenerative Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (N.I.M.F.); (D.L.Q.H.); (M.B.F.)
| | - Daniel Looi Qi Hao
- Centre for Tissue Engineering and Regenerative Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (N.I.M.F.); (D.L.Q.H.); (M.B.F.)
- My Cytohealth Sdn. Bhd., Kuala Lumpur 56000, Malaysia
| | - Gavin Markey
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry BT47 6SB, UK; (G.M.); (P.S.)
| | - Priyank Shukla
- Personalised Medicine Centre, School of Medicine, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Glenshane Road, Londonderry BT47 6SB, UK; (G.M.); (P.S.)
| | - Mh Busra Fauzi
- Centre for Tissue Engineering and Regenerative Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia; (N.I.M.F.); (D.L.Q.H.); (M.B.F.)
| | - Fivos Panetsos
- Neurocomputing and Neurorobotics Research Group, Faculty of Biology and Faculty of Optics, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Institute for Health Research San Carlos Clinical Hospital (IdISSC), 28040 Madrid, Spain
- Silk Biomed SL, 28260 Madrid, Spain
- Bioactive Surfaces SL, 28260 Madrid, Spain
- Omnia Mater SL, 28009 Madrid, Spain
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Rippon MG, Fleming L, Chen T, Rogers AA, Ousey K. Artificial intelligence in wound care: diagnosis, assessment and treatment of hard-to-heal wounds: a narrative review. J Wound Care 2024; 33:229-242. [PMID: 38573907 DOI: 10.12968/jowc.2024.33.4.229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
OBJECTIVE The effective assessment of wounds, both acute and hard-to-heal, is an important component in the delivery by wound care practitioners of efficacious wound care for patients. Improved wound diagnosis, optimising wound treatment regimens, and enhanced prevention of wounds aid in providing patients with a better quality of life (QoL). There is significant potential for the use of artificial intelligence (AI) in health-related areas such as wound care. However, AI-based systems remain to be developed to a point where they can be used clinically to deliver high-quality wound care. We have carried out a narrative review of the development and use of AI in the diagnosis, assessment and treatment of hard-to-heal wounds. We retrieved 145 articles from several online databases and other online resources, and 81 of them were included in this narrative review. Our review shows that AI application in wound care offers benefits in the assessment/diagnosis, monitoring and treatment of acute and hard-to-heal wounds. As well as offering patients the potential of improved QoL, AI may also enable better use of healthcare resources.
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Affiliation(s)
- Mark G Rippon
- University of Huddersfield, Huddersfield, UK
- Daneriver Consultancy Ltd, Holmes Chapel, UK
| | - Leigh Fleming
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | - Tianhua Chen
- School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
| | | | - Karen Ousey
- University of Huddersfield Department of Nursing and Midwifery, Huddersfield, UK
- Adjunct Professor, School of Nursing, Faculty of Health at the Queensland University of Technology, Australia
- Visiting Professor, Royal College of Surgeons in Ireland, Dublin, Ireland
- Chair, International Wound Infection Institute
- President Elect, International Skin Tear Advisory Panel
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Lo ZJ, Mak MHW, Liang S, Chan YM, Goh CC, Lai T, Tan A, Thng P, Rodriguez J, Weyde T, Smit S. Development of an explainable artificial intelligence model for Asian vascular wound images. Int Wound J 2024; 21:e14565. [PMID: 38146127 PMCID: PMC10961881 DOI: 10.1111/iwj.14565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 12/04/2023] [Indexed: 12/27/2023] Open
Abstract
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.
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Affiliation(s)
- Zhiwen Joseph Lo
- Department of SurgeryWoodlands HealthSingaporeSingapore
- Lee Kong Chian School of MedicineNanyang Technological UniversitySingaporeSingapore
| | | | | | - Yam Meng Chan
- Department of General SurgeryTan Tock Seng HospitalSingaporeSingapore
| | - Cheng Cheng Goh
- Wound and Stoma Care, Nursing SpecialityTan Tock Seng HospitalSingaporeSingapore
| | - Tina Lai
- Wound and Stoma Care, Nursing SpecialityTan Tock Seng HospitalSingaporeSingapore
| | - Audrey Tan
- Wound and Stoma Care, Nursing SpecialityTan Tock Seng HospitalSingaporeSingapore
| | - Patrick Thng
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| | - Jorge Rodriguez
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| | - Tillman Weyde
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
| | - Sylvia Smit
- AITIS ‐ Advanced Intelligence and Technology InnovationsLondonUnited Kingdom
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