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Vakili Ojarood M, Farzan R, Mohsenizadeh SM, Torabi H, Yaghoubi T. Deep Learning during burn prehospital care: An evolving perspective. Burns 2024; 50:1349-1351. [PMID: 38582694 DOI: 10.1016/j.burns.2024.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024]
Affiliation(s)
| | - Ramyar Farzan
- Department of Plastic & Reconstructive Surgery, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | - Seyed Mostafa Mohsenizadeh
- Department of Nursing, Qaen School of Nursing and Midwifery, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Torabi
- Department of General Surgery, Poursina Medical and Educational Center, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
| | - Tahereh Yaghoubi
- Traditional and Complementary Medicine Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Iran.
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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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Wilson RH, Rowland R, Kennedy GT, Campbell C, Joe VC, Chin TL, Burmeister DM, Christy RJ, Durkin AJ. Review of machine learning for optical imaging of burn wound severity assessment. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:020901. [PMID: 38361506 PMCID: PMC10869118 DOI: 10.1117/1.jbo.29.2.020901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/17/2024]
Abstract
Significance Over the past decade, machine learning (ML) algorithms have rapidly become much more widespread for numerous biomedical applications, including the diagnosis and categorization of disease and injury. Aim Here, we seek to characterize the recent growth of ML techniques that use imaging data to classify burn wound severity and report on the accuracies of different approaches. Approach To this end, we present a comprehensive literature review of preclinical and clinical studies using ML techniques to classify the severity of burn wounds. Results The majority of these reports used digital color photographs as input data to the classification algorithms, but recently there has been an increasing prevalence of the use of ML approaches using input data from more advanced optical imaging modalities (e.g., multispectral and hyperspectral imaging, optical coherence tomography), in addition to multimodal techniques. The classification accuracy of the different methods is reported; it typically ranges from ∼ 70 % to 90% relative to the current gold standard of clinical judgment. Conclusions The field would benefit from systematic analysis of the effects of different input data modalities, training/testing sets, and ML classifiers on the reported accuracy. Despite this current limitation, ML-based algorithms show significant promise for assisting in objectively classifying burn wound severity.
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Affiliation(s)
- Robert H. Wilson
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
- University of California, Irvine, Department of Medicine, Orange, California, United States
- University of California, Irvine, Health Policy Research Institute, Irvine, California, United States
| | - Rebecca Rowland
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Gordon T. Kennedy
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Chris Campbell
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
| | - Victor C. Joe
- UC Irvine Health Regional Burn Center, Orange, California, United States
| | | | - David M. Burmeister
- Uniformed Services University of the Health Sciences, School of Medicine, Bethesda, Maryland, United States
| | - Robert J. Christy
- UT Health San Antonio, Military Health Institute, San Antonio, Texas, United States
| | - Anthony J. Durkin
- University of California, Irvine, Beckman Laser Institute and Medical Clinic, Irvine, California, United States
- University of California, Irvine, Department of Biomedical Engineering, Irvine, California, United States
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Yang SY, Huang CJ, Yen CI, Kao YC, Hsiao YC, Yang JY, Chang SY, Chuang SS, Chen HC. Machine learning approach for predicting inhalation injury in patients with burns. Burns 2023; 49:1592-1601. [PMID: 37055284 PMCID: PMC10032063 DOI: 10.1016/j.burns.2023.03.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND The coronavirus disease pandemic has had a tangible impact on bronchoscopy for burn inpatients due to isolation and triage measures. We utilised the machine-learning approach to identify risk factors for predicting mild and severe inhalation injury and whether patients with burns experienced inhalation injury. We also examined the ability of two dichotomous models to predict clinical outcomes including mortality, pneumonia, and duration of hospitalisation. METHODS A retrospective 14-year single-centre dataset of 341 intubated patients with burns with suspected inhalation injury was established. The medical data on day one of admission and bronchoscopy-diagnosed inhalation injury grade were compiled using a gradient boosting-based machine-learning algorithm to create two prediction models: model 1, mild vs. severe inhalation injury; and model 2, no inhalation injury vs. inhalation injury. RESULTS The area under the curve (AUC) for model 1 was 0·883, indicating excellent discrimination. The AUC for model 2 was 0·862, indicating acceptable discrimination. In model 1, the incidence of pneumonia (P < 0·001) and mortality rate (P < 0·001), but not duration of hospitalisation (P = 0·1052), were significantly higher in patients with severe inhalation injury. In model 2, the incidence of pneumonia (P < 0·001), mortality (P < 0·001), and duration of hospitalisation (P = 0·021) were significantly higher in patients with inhalation injury. CONCLUSIONS We developed the first machine-learning tool for differentiating between mild and severe inhalation injury, and the absence/presence of inhalation injury in patients with burns, which is helpful when bronchoscopy is not available immediately. The dichotomous classification predicted by both models was associated with the clinical outcomes.
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Affiliation(s)
- Shih-Yi Yang
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Chih-Jung Huang
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Cheng-I Yen
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Yu-Ching Kao
- Muen Biomedical and Optoelectronic Technologies Inc, China
| | - Yen-Chang Hsiao
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Jui-Yung Yang
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Shu-Yin Chang
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Shiow-Shuh Chuang
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC
| | - Hung-Chang Chen
- Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Linkou, College of Medicine, Chang Gung University, Taoyuan, Taiwan, ROC.
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Rozo A, Miskovic V, Rose T, Keersebilck E, Iorio C, Varon C. A Deep Learning Image-to-Image Translation Approach for a More Accessible Estimator of the Healing Time of Burns. IEEE Trans Biomed Eng 2023; 70:2886-2894. [PMID: 37067977 DOI: 10.1109/tbme.2023.3267600] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
OBJECTIVE An accurate and timely diagnosis of burn severity is critical to ensure a positive outcome. Laser Doppler imaging (LDI) has become a very useful tool for this task. It measures the perfusion of the burn and estimates its potential healing time. LDIs generate a 6-color palette image, with each color representing a healing time. This technique has very high costs associated. In resource-limited areas, such as low- and middle-income countries or remote locations like space, where access to specialized burn care is inadequate, more affordable and portable tools are required. This study proposes a novel image-to-image translation approach to estimate burn healing times, using a digital image to approximate the LDI. METHODS This approach consists of a U-net architecture with a VGG-based encoder and applies the concept of ordinal classification. Paired digital and LDI images of burns were collected. The performance was evaluated with 10-fold cross-validation, mean absolute error (MAE), and color distribution differences between the ground truth and the estimated LDI. RESULTS Results showed a satisfactory performance in terms of low MAE ( 0.2370 ±0.0086). However, the unbalanced distribution of colors in the data affects this performance. SIGNIFICANCE This novel and unique approach serves as a basis for developing more accessible support tools in the burn care environment in resource-limited areas.
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Chang CW, Ho CY, Lai F, Christian M, Huang SC, Chang DH, Chen YS. Application of multiple deep learning models for automatic burn wound assessment. Burns 2023; 49:1039-1051. [PMID: 35945064 DOI: 10.1016/j.burns.2022.07.006] [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] [Received: 02/16/2022] [Revised: 06/24/2022] [Accepted: 07/14/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE Accurate assessment of the percentage of total body surface area (%TBSA) burned is crucial in managing burn injuries. It is difficult to estimate the size of an irregular shape by inspection. Many articles reported the discrepancy of estimating %TBSA burned by different doctors. We set up a system with multiple deep learning (DL) models for %TBSA estimation, as well as the segmentation of possibly poor-perfused deep burn regions from the entire wound. METHODS We proposed boundary-based labeling for datasets of total burn wound and palm, whereas region-based labeling for the dataset of deep burn wound. Several powerful DL models (U-Net, PSPNet, DeeplabV3+, Mask R-CNN) with encoders ResNet101 had been trained and tested from the above datasets. With the subject distances, the %TBSA burned could be calculated by the segmentation of total burn wound area with respect to the palm size. The percentage of deep burn area could be obtained from the segmentation of deep burn area from the entire wound. RESULTS A total of 4991 images of early burn wounds and 1050 images of palms were boundary-based labeled. 1565 out of 4994 images with deep burn were preprocessed with superpixel segmentation into small regions before labeling. DeeplabV3+ had slightly better performance in three tasks with precision: 0.90767, recall: 0.90065 for total burn wound segmentation; precision: 0.98987, recall: 0.99036 for palm segmentation; and precision: 0.90152, recall: 0.90219 for deep burn segmentation. CONCLUSION Combining the segmentation results and clinical data, %TBSA burned, the volume of fluid for resuscitation, and the percentage of deep burn area can be automatically diagnosed by DL models with a pixel-to-pixel method. Artificial intelligence provides consistent, accurate and rapid assessments of burn wounds.
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Affiliation(s)
- Che Wei Chang
- Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan; Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan.
| | - Chun Yee Ho
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics & Bioinformatics, National Taiwan University, Taipei, Taiwan; Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Mesakh Christian
- Department of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan
| | - Shih Chen Huang
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
| | - Dun Hao Chang
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan; Department of Information Management, Yuan Ze University, Taiwan
| | - Yo Shen Chen
- Division of Plastic and Reconstructive Surgery, Department of Surgery, Far Eastern Memorial Hospital, New Taipei, Taiwan
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Kairys A, Pauliukiene R, Raudonis V, Ceponis J. Towards Home-Based Diabetic Foot Ulcer Monitoring: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3618. [PMID: 37050678 PMCID: PMC10099334 DOI: 10.3390/s23073618] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/14/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
It is considered that 1 in 10 adults worldwide have diabetes. Diabetic foot ulcers are some of the most common complications of diabetes, and they are associated with a high risk of lower-limb amputation and, as a result, reduced life expectancy. Timely detection and periodic ulcer monitoring can considerably decrease amputation rates. Recent research has demonstrated that computer vision can be used to identify foot ulcers and perform non-contact telemetry by using ulcer and tissue area segmentation. However, the applications are limited to controlled lighting conditions, and expert knowledge is required for dataset annotation. This paper reviews the latest publications on the use of artificial intelligence for ulcer area detection and segmentation. The PRISMA methodology was used to search for and select articles, and the selected articles were reviewed to collect quantitative and qualitative data. Qualitative data were used to describe the methodologies used in individual studies, while quantitative data were used for generalization in terms of dataset preparation and feature extraction. Publicly available datasets were accounted for, and methods for preprocessing, augmentation, and feature extraction were evaluated. It was concluded that public datasets can be used to form a bigger, more diverse datasets, and the prospects of wider image preprocessing and the adoption of augmentation require further research.
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Affiliation(s)
- Arturas Kairys
- Automation Department, Electrical and Electronics Faculty, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Renata Pauliukiene
- Department of Endocrinology, Lithuanian University of Health Sciences, 50161 Kaunas, Lithuania
| | - Vidas Raudonis
- Automation Department, Electrical and Electronics Faculty, Kaunas University of Technology, 51368 Kaunas, Lithuania
| | - Jonas Ceponis
- Institute of Endocrinology, Lithuanian University of Health Sciences, 44307 Kaunas, Lithuania
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