Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise.

Publication type:
Journal article
Metadata:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000852247000020&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1109/JBHI.2022.3186530
eISSN
2168-2208
Externe Identifier
  • Clarivate Analytics Document Solution ID: 4K9FX
  • PubMed Identifier: 35759601
ISSN
2168-2194
Ausgabe der Veröffentlichung
9
Zeitschrift
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Schlüsselwörter
  • Artificial neural networks
  • biotechnology
  • incremental exercise testing
  • semantic segmentation
  • thermal imaging
Paginierung
4530 - 4540
Datum der Veröffentlichung
2022
Status
Published
Titel
Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise
Sub types
  • Article
Ausgabe der Zeitschrift
26

Data source: Web of Science (Lite)

Other metadata sources:
Autoren
DOI
10.1109/jbhi.2022.3186530
eISSN
2168-2208
ISSN
2168-2194
Ausgabe der Veröffentlichung
9
Zeitschrift
IEEE Journal of Biomedical and Health Informatics
Paginierung
4530 - 4540
Datum der Veröffentlichung
2022
Status
Published
Herausgeber
Institute of Electrical and Electronics Engineers (IEEE)
Herausgeber URL
http://dx.doi.org/10.1109/jbhi.2022.3186530
Datum der Datenerfassung
2024
Titel
Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise
Ausgabe der Zeitschrift
26

Data source: Crossref

Abstract
Infrared thermography is increasingly applied in sports science due to promising observations regarding changes in skin's surface radiation temperature ( T<sub>sr</sub>) before, during, and after exercise. The common manual thermogram analysis limits an objective and reproducible measurement of T<sub>sr</sub>. Previous analysis approaches depend on expert knowledge and have not been applied during movement. We aimed to develop a deep neural network (DNN) capable of automatically and objectively segmenting body parts, recognizing blood vessel-associated T<sub>sr</sub> distributions, and continuously measuring T<sub>sr</sub> during exercise. We conducted 38 cardiopulmonary exercise tests on a treadmill. We developed two DNNs: body part network and vessel network, to perform semantic segmentation of 1 107 855 thermal images. Both DNNs were trained with 263 training and 75 validation images. Additionally, we compare the results of a common manual thermogram analysis with these of the DNNs. Performance analysis identified a mean IoU of 0.8 for body part network and 0.6 for vessel network. There is a high agreement between manual and automatic analysis (r = 0.999; p 0.001; T-test: p = 0.116), with a mean difference of 0.01 <sup>°</sup>C (0.08). Non-parametric Bland Altman's analysis showed that the 95% agreement ranges between - 0.086 <sup>°</sup>C and 0.228 <sup>°</sup>C. The developed DNNs enable automatic, objective, and continuous measurement of T<sub>sr</sub> and recognition of blood vessel-associated T<sub>sr</sub> distributions in resting and moving legs. Hence, the DNNs surpass previous algorithms by eliminating manual region of interest selection and form the currently needed foundation to extensively investigate T<sub>sr</sub> distributions related to non-invasive diagnostics of (patho-)physiological traits in means of exercise radiomics.
Autoren
DOI
10.1109/jbhi.2022.3186530
eISSN
2168-2208
Externe Identifier
  • PubMed Identifier: 35759601
Funding acknowledgements
  • German Parliament: ZF4211603GR9
  • Bundesministerium für Wirtschaft und Energie:
Open access
false
ISSN
2168-2194
Ausgabe der Veröffentlichung
9
Zeitschrift
IEEE journal of biomedical and health informatics
Schlüsselwörter
  • Humans
  • Thermography
  • Exercise
  • Algorithms
  • Neural Networks, Computer
Sprache
eng
Medium
Print-Electronic
Online publication date
2022
Paginierung
4530 - 4540
Datum der Veröffentlichung
2022
Status
Published
Datum der Datenerfassung
2022
Titel
Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise.
Sub types
  • Research Support, Non-U.S. Gov't
  • Journal Article
Ausgabe der Zeitschrift
26

Data source: Europe PubMed Central

Abstract
Infrared thermography is increasingly applied in sports science due to promising observations regarding changes in skin's surface radiation temperature ( Tsr) before, during, and after exercise. The common manual thermogram analysis limits an objective and reproducible measurement of Tsr. Previous analysis approaches depend on expert knowledge and have not been applied during movement. We aimed to develop a deep neural network (DNN) capable of automatically and objectively segmenting body parts, recognizing blood vessel-associated Tsr distributions, and continuously measuring Tsr during exercise. We conducted 38 cardiopulmonary exercise tests on a treadmill. We developed two DNNs: body part network and vessel network, to perform semantic segmentation of 1 107 855 thermal images. Both DNNs were trained with 263 training and 75 validation images. Additionally, we compare the results of a common manual thermogram analysis with these of the DNNs. Performance analysis identified a mean IoU of 0.8 for body part network and 0.6 for vessel network. There is a high agreement between manual and automatic analysis (r = 0.999; p 0.001; T-test: p = 0.116), with a mean difference of 0.01 °C (0.08). Non-parametric Bland Altman's analysis showed that the 95% agreement ranges between - 0.086 °C and 0.228 °C. The developed DNNs enable automatic, objective, and continuous measurement of Tsr and recognition of blood vessel-associated Tsr distributions in resting and moving legs. Hence, the DNNs surpass previous algorithms by eliminating manual region of interest selection and form the currently needed foundation to extensively investigate Tsr distributions related to non-invasive diagnostics of (patho-)physiological traits in means of exercise radiomics.
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/35759601
DOI
10.1109/JBHI.2022.3186530
eISSN
2168-2208
Ausgabe der Veröffentlichung
9
Zeitschrift
IEEE J Biomed Health Inform
Schlüsselwörter
  • Algorithms
  • Exercise
  • Humans
  • Neural Networks, Computer
  • Thermography
Sprache
eng
Country
United States
Paginierung
4530 - 4540
Datum der Veröffentlichung
2022
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Titel
Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
26

Data source: PubMed

Autoren
DOI
10.1109/JBHI.2022.3186530
eISSN
2168-2208
ISSN
2168-2194
Zeitschrift
IEEE Journal of Biomedical and Health Informatics
Paginierung
1 - 11
Datum der Veröffentlichung
2022
Status
Published
Herausgeber
Institute of Electrical and Electronics Engineers (IEEE)
Datum der Datenerfassung
2022
Titel
Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise.
Sub types
  • Article

Data source: Manual

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