Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography
Open accessAbstract
Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Asymmetric breast temperature on a thermogram may indicate potential breast cancer.
“From this thermal scan, the doctor may indicate potential breast cancer in case of asymmetric breast temperature [163,164,165,166,167,168].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [167]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 80 · Cited by 49
Research works printed but not linked: 7
- Ring, F.; Ammer, K. Infrared thermal imaging in medicine. Physiol. Meas. 2012, 33, R33-R46. [CrossRef] [PubMed] · DOI
- Arens, E.; Zhang, H.A. The Skin's Role in Human Thermoregulation and Comfort. In Thermal and Moisture Transport in Fibrous Materials; Pan, N., Gibson, P., Eds.; Woodhead Publishing Limited: Cambridge, UK, 2006; pp. 560-602.
- Vardasca, R.; Ramalhão, C.; Gabriel, J. Bases da Termografia: Enquadramento da Termografia Com Outras Técnicas de Imagem Médica. In Termografia: Imagem Médica e Síndromes Dolorosas, 1st ed.; Lidel: Lisbon, Portugal, 2016; pp. 3-10.
- Ammer, K. The Glamorgan Protocol for recording and evaluation of thermal images of the human body. Thermol. Int. 2008, 18, 125-129.
- Gururajarao, S.B.; Venkatappa, U.; Shivaram, J.M.; Sikkandar, M.Y.; Al Amoudi, A. Infrared Thermography and Soft Computing for Diabetic Foot Assessment; Elsevier BV: Amsterdam, The Netherlands, 2019; pp. 73-97.
- Resmini, R.; Borchartt, T.B.; Conci, A.; Lima, R.C.F. Auxílio ao Diagnóstico Precoce de Patologias da Mama Usando Imagens Térmicas Câncer de mama. Anais Comput. Beach 2013, 1, 305-314.
- Gopinath, M.P.; Prabu, S. Classification of thyroid Abnormalities on thermal image: A study and approach. IIOAB J. 2016, 7, 41-57.
- Authors
- Magalhaes, C., Mendes, J., & Vardasca, R.
- Affiliations
- Universidade do Porto (PT)
- University of South Wales (GB)
- Corresponding author
- Vardasca, Ricardo
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2021-01-18
- Volume
- 11
- Issue
- 2
- Pages
- 842
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3121676285
- Cited by
- 49
- References
- 80
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 10 · 2023: 7 · 2022: 13 · 2021: 7 · 2013: 1
- FWCI
- 1.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Repositório Comum (Repositório Científico de Acesso Aberto de Portugal) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundação para a Ciência e Tecnologia (SFRH/BD/144906/2019, UIDP/50022/2020)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Physical Sciences › Engineering › Mechanics of Materials › Thermography and Photoacoustic Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- infrared thermography, machine learning classifiers, artificial intelligence in healthcare, skin temperature, disease diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
