The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area
Abstract
:1. Introduction
2. Method
2.1. Research Design
2.2. Measurements
2.3. Data Collection
2.4. Statistics and Data Analysis
2.5. Ethical Considerations
3. Results
3.1. Demographic Data of Patients
3.2. Hyperspectral Image Data
3.3. Comparison of Related Statistical Parameters of Various Wound Area Methods
3.4. Difference in Measurement Time for Pressure Injury Assessment
4. Discussion
5. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variable | n | % |
---|---|---|
Gender | ||
Male | 20 | 66.7 |
Female | 10 | 33.3 |
Age (Mean ± SD), Y | 71.7 ± 15.97 | |
Admission diagnosis Pneumonia | 8 | 26.7 |
Respiratory failure | 4 | 13.3 |
Obstructive lung disease | 1 | 3.3 |
Cerebrovascular disease | 5 | 16.7 |
Brain tumor | 1 | 3.3 |
Intracranial injury | 3 | 10.0 |
Diabetes Urinary tract infection Septicemia Buttock abscess Fever | 3 3 2 1 1 | 10.0 10.0 6.7 3.3 3.3 |
Pressure injury stage | ||
Stage 1 | 2 | 6.6 |
Stage 2 | 13 | 43.3 |
Stage 3 | 10 | 33.3 |
Stage 4 | 1 | 3.3 |
Unstageable | 4 | 13.3 |
Subject | Classification |
---|---|
Case 01 | Stage 2 |
Case 02 | Stage 3 |
Case 03 | Unstageable |
Case 04 | Stage 2 |
Case 05 | Stage 3 |
Case 06 | Stage 3 |
Case 07 | Unstageable |
Case 08 | Unstageable |
Case 09 | Stage 4 |
Case10 | Stage 3 |
Case 11 | Stage 2 |
Case 12 | Stage 2 |
Case 13 | Stage 2 |
Case 14 | Stage 3 |
Case 15 | Unstageable |
Case 16 | Stage 2 |
Case 17 | Stage 3 |
Case 18 | Stage 3 |
Case 19 | Stage 2 |
Case 20 | Stage 2 |
Case 21 | Stage 1 |
Case 22 | Stage 3 |
Case 23 | Stage 1 |
Case 24 | Stage 2 |
Case 25 | Stage 3 |
Case 26 | Stage 2 |
Case 27 | Stage 3 |
Case 28 | Stage 2 |
Case 29 | Stage 2 |
Case 30 | Stage 2 |
Subject | Nurses Using the Length × Width Rule to Determine the Wound Area and Cal-culate the Area | The Machine Learning Meth-od Using the Length × Width Rule to Determine the Wound Area and Calculate the Area | Machine Learning Method Using Image Morphology Algorithm to Determine Wound Area and Calculate Area |
---|---|---|---|
Case 01 | 1.46 | 2.19 | 3.60 |
Case 02 | 12.05 | 10.22 | 22.60 ** |
Case 03 | 9.86 | 7.42 | 5.00 |
Case 04 | 0.18 | 0.09 | 0.30 |
Case 05 | 34.49 | 26.28 | 26.10 |
Case 06 | 4.93 | 5.11 | 20.70 ** |
Case 07 | 6.57 | 14.86 | 26.50 ** |
Case 08 | 54.75 | 40.88 | 26.80 |
Case 09 | 14.60 | 10.07 | 7.90 |
Case10 | 35.95 | 11.68 | 11.60 |
Case 11 | 23.66 | 11.41 | 7.00 |
Case 12 | 18.25 | 8.76 | 7.10 |
Case 13 | 10.95 | 8.41 | 8.80 |
Case 14 | 3.65 | 3.10 | 2.70 |
Case 15 | 39.79 | 36.73 | 34.40 |
Case 16 | 1.21 | 5.26 | 3.80 |
Case 17 | 39.89 | 35.77 | 35.40 |
Case 18 | 25.55 | 15.42 | 13.70 |
Case 19 | 59.13 | 35.04 | 25.70 |
Case 20 | 77.38 | 29.20 | 29.60 |
Case 21 | 1.64 | 7.01 | 39.10 ** |
Case 22 | 65.70 | 33.73 | 32.60 |
Case 23 | 0.04 | 0.73 | 27.20 ** |
Case 24 | 4.38 | 4.38 | 20.40 ** |
Case 25 | 20.44 | 13.14 | 12.10 |
Case 26 | 14.60 | 6.48 | 6.50 |
Case 27 | 24.09 | 21.90 | 16.10 |
Case 28 | 52.56 | 2.92 | 1.10 ** |
Case 29 | 3.07 | 0.61 | 0.50 |
Case 30 | 3.29 | 0.73 | 0.70 |
Wound Assessment Method | Staff Length × Width vs. Machine Learning Length × Width | Staff Length × Width vs. Machine Learning Combined with Morphology | Machine Learning Length × Width vs. Machine Learning Combined with Morphology | |
---|---|---|---|---|
Statistical Parameters | ||||
With Outliers | ||||
Pearson’s correlation coefficient | 0.80 p < 0.001 | 0.44 p < 0.05 | 0.69 p < 0.001 | |
Spearman’s correlation coefficient | 0.82 p < 0.001 | 0.44 p < 0.05 | 0.70 p < 0.001 | |
Intraclass correlation coefficient ICC | 0.81 p < 0.001 | 0.54 p < 0.05 | 0.81 p < 0.001 | |
Unweighted kappa value κ | 0.03 95% CI: [−0.02, 0.09] | 0.01 95% CI: [0.01, 0.01] | 0.01 95% CI: [0.01, 0.01] | |
Weighted kappa value Weighted κ | 0.76 95% CI: [0.55, 0.97] | 0.42 95% CI: [0.06, 0.78] | 0.71 95% CI: [0.46, 0.95] | |
Without Outliers (The number of cases with inconsistent determination of wound location has been deducted) | ||||
Pearson’s correlation coefficient | 0.88 p < 0.001 | 0.87 p < 0.001 | 0.96 p < 0.001 | |
Spearman’s correlation coefficient | 0.93 p < 0.001 | 0.92 p < 0.001 | 0.97 p < 0.001 | |
Intraclass correlation coefficient ICC | 0.87 p < 0.001 | 0.85 p < 0.001 | 0.98 p < 0.001 | |
Unweighted kappa value κ | 0.01 95% CI: [0.01, 0.01] | 0.01 95% CI: [0.01, 0.01] | 0.01 95% CI: [0.01, 0.01] | |
Weighted kappa value Weighted κ | 0.85 95% CI: [0.77, 0.94] | 0.81 95% CI: [0.71, 0.91] | 0.9560 95% CI: [0.92, 0.99] |
Wound Assessment Method | Machine Learning Length × Width | Machine Learning Combined with Morphology | |
---|---|---|---|
Statistical Parameters | |||
Intraclass correlation coefficient ICC | 0.7728 p < 0.05 | 0.8372 p < 0.05 |
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Lee, L.-L.; Chen, S.-L. The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area. Int. J. Environ. Res. Public Health 2023, 20, 2851. https://doi.org/10.3390/ijerph20042851
Lee L-L, Chen S-L. The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area. International Journal of Environmental Research and Public Health. 2023; 20(4):2851. https://doi.org/10.3390/ijerph20042851
Chicago/Turabian StyleLee, Lin-Lin, and Shu-Ling Chen. 2023. "The Application of Hyperspectral Imaging to the Measurement of Pressure Injury Area" International Journal of Environmental Research and Public Health 20, no. 4: 2851. https://doi.org/10.3390/ijerph20042851