Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker
The number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/1/169 |
| _version_ | 1797498638218297344 |
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| author | Franziska Schollemann Janosch Kunczik Henriette Dohmeier Carina Barbosa Pereira Andreas Follmann Michael Czaplik |
| author_facet | Franziska Schollemann Janosch Kunczik Henriette Dohmeier Carina Barbosa Pereira Andreas Follmann Michael Czaplik |
| author_sort | Franziska Schollemann |
| collection | DOAJ |
| description | The number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to accelerate the application of treatments. For this reason, the infection probability index (IPI) is introduced as a novel infection marker based on thermal wound imaging. To improve usability, the IPI was implemented to automate scoring. Visual and thermal image pairs of 60 wounds were acquired to test the implemented algorithms on clinical data. The proposed process consists of (1) determining various parameters of the IPI based on medical hypotheses, (2) acquiring data, (3) extracting camera distortions using camera calibration, and (4) preprocessing and (5) automating segmentation of the wound to calculate (6) the IPI. Wound segmentation is reviewed by user input, whereas the segmented area can be refined manually. Furthermore, in addition to proof of concept, IPIs’ correlation with C-reactive protein (CRP) levels as a clinical infection marker was evaluated. Based on average CRP levels, the patients were clustered into two groups, on the basis of the separation value of an averaged CRP level of 100. We calculated the IPIs of the 60 wound images based on automated wound segmentation. Average runtime was less than a minute. In the group with lower average CRP, a correlation between IPI and CRP was evident. |
| first_indexed | 2024-03-10T03:36:11Z |
| format | Article |
| id | doaj.art-ea9e7d8ab312408da1a06e7d66537662 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T03:36:11Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-ea9e7d8ab312408da1a06e7d665376622023-11-23T11:44:41ZengMDPI AGJournal of Clinical Medicine2077-03832021-12-0111116910.3390/jcm11010169Infection Probability Index: Implementation of an Automated Chronic Wound Infection MarkerFranziska Schollemann0Janosch Kunczik1Henriette Dohmeier2Carina Barbosa Pereira3Andreas Follmann4Michael Czaplik5Department of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, GermanyDepartment of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, GermanyDepartment of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, GermanyDepartment of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, GermanyDepartment of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, GermanyDepartment of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, 52062 Aachen, GermanyThe number of people suffering from chronic wounds is increasing due to demographic changes and the global epidemics of obesity and diabetes. Innovative imaging techniques within the field of chronic wound diagnostics are required to improve wound care by predicting and detecting wound infections to accelerate the application of treatments. For this reason, the infection probability index (IPI) is introduced as a novel infection marker based on thermal wound imaging. To improve usability, the IPI was implemented to automate scoring. Visual and thermal image pairs of 60 wounds were acquired to test the implemented algorithms on clinical data. The proposed process consists of (1) determining various parameters of the IPI based on medical hypotheses, (2) acquiring data, (3) extracting camera distortions using camera calibration, and (4) preprocessing and (5) automating segmentation of the wound to calculate (6) the IPI. Wound segmentation is reviewed by user input, whereas the segmented area can be refined manually. Furthermore, in addition to proof of concept, IPIs’ correlation with C-reactive protein (CRP) levels as a clinical infection marker was evaluated. Based on average CRP levels, the patients were clustered into two groups, on the basis of the separation value of an averaged CRP level of 100. We calculated the IPIs of the 60 wound images based on automated wound segmentation. Average runtime was less than a minute. In the group with lower average CRP, a correlation between IPI and CRP was evident.https://www.mdpi.com/2077-0383/11/1/169chronic woundsmonitoringthermal imagingcamera calibrationsegmentationregion growing |
| spellingShingle | Franziska Schollemann Janosch Kunczik Henriette Dohmeier Carina Barbosa Pereira Andreas Follmann Michael Czaplik Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker Journal of Clinical Medicine chronic wounds monitoring thermal imaging camera calibration segmentation region growing |
| title | Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker |
| title_full | Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker |
| title_fullStr | Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker |
| title_full_unstemmed | Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker |
| title_short | Infection Probability Index: Implementation of an Automated Chronic Wound Infection Marker |
| title_sort | infection probability index implementation of an automated chronic wound infection marker |
| topic | chronic wounds monitoring thermal imaging camera calibration segmentation region growing |
| url | https://www.mdpi.com/2077-0383/11/1/169 |
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