Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications
Abstract Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of...
| 發表在: | Scientific Reports |
|---|---|
| Main Authors: | , , , |
| 格式: | Article |
| 語言: | 英语 |
| 出版: |
Nature Portfolio
2025-03-01
|
| 在線閱讀: | https://doi.org/10.1038/s41598-025-92096-4 |
| _version_ | 1849508175847358464 |
|---|---|
| author | Pramod K. B. Rangaiah B P Pradeep kumar Fredrik Huss Robin Augustine |
| author_facet | Pramod K. B. Rangaiah B P Pradeep kumar Fredrik Huss Robin Augustine |
| author_sort | Pramod K. B. Rangaiah |
| collection | DOAJ |
| container_title | Scientific Reports |
| description | Abstract Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes. |
| format | Article |
| id | doaj-art-40f7edf06f604aaa973d1feb35fff8fc |
| institution | Directory of Open Access Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| spelling | doaj-art-40f7edf06f604aaa973d1feb35fff8fc2025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115113010.1038/s41598-025-92096-4Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applicationsPramod K. B. Rangaiah0B P Pradeep kumar1Fredrik Huss2Robin Augustine3Microwaves in Medical Engineering Group, Division of Solid State Electronics, Department of Electrical Engineering, Uppsala UniversityDepartment of Computer Science and Design, Atria Institute of TechnologyDepartment of Surgical Sciences, Plastic Surgery, Uppsala UniversityMicrowaves in Medical Engineering Group, Division of Solid State Electronics, Department of Electrical Engineering, Uppsala UniversityAbstract Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes.https://doi.org/10.1038/s41598-025-92096-4 |
| spellingShingle | Pramod K. B. Rangaiah B P Pradeep kumar Fredrik Huss Robin Augustine Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| title | Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| title_full | Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| title_fullStr | Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| title_full_unstemmed | Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| title_short | Precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| title_sort | precision diagnosis of burn injuries using imaging and predictive modeling for clinical applications |
| url | https://doi.org/10.1038/s41598-025-92096-4 |
| work_keys_str_mv | AT pramodkbrangaiah precisiondiagnosisofburninjuriesusingimagingandpredictivemodelingforclinicalapplications AT bppradeepkumar precisiondiagnosisofburninjuriesusingimagingandpredictivemodelingforclinicalapplications AT fredrikhuss precisiondiagnosisofburninjuriesusingimagingandpredictivemodelingforclinicalapplications AT robinaugustine precisiondiagnosisofburninjuriesusingimagingandpredictivemodelingforclinicalapplications |