Skin neoplasms dynamic thermal assessment

Magalhães, Cíntia Lopes de Brito; Mendes, Joaquim; Filipe, Rita Valença; Vardasca, Ricardo

doi:10.1109/enbeng.2019.8692482

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…In other cases (44,74), the datasets were equally split in training and test set. Some studies (57,73,74,76,77) used features extracted from the original data to feed their algorithms. Although this operation could reduce computational cost, the resulting classification performance could be affected since the features extracted manually might not represent most of the information content of the original dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Thermal images of the entire area can be acquired without skin contact and in <5 min (66,67,69,70,72,73). The diagnostic performance of this technique is still unclear since few studies (69,(72)(73)(74)76) used the technology with the aim of making a diagnosis, moreover the results reported were not exhaustively detailed from a methodological point of view. Further studies are needed to uncover the histopathological underpinnings on which this system acts.…”

Section: Discussionmentioning

confidence: 99%

“…Thermal imaging (66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76)(77) is the leading technique investigated in literature. Thermal cameras were used to obtain skin lesion features at steady-state (71,74,77) and in dynamic thermal conditions (66)(67)(68)(69)(70)(72)(73)(74)(75)(76)(77). In steady-state studies, thermal images were used to obtain temperature features of the investigated lesion, such as, pixels temperature profiles (74) and temperature difference between several type of lesions and the healthy surrounding skin (71).…”

Section: Thermal Measurementsmentioning

confidence: 99%

“…Godoy et al (72,73) added to this pipeline two different decisional algorithms to enable the automatic classification of a lesion (melanoma vs. other benign skin lesions). In Magalhaes et al (76,77) a different cooling and processing pipeline was implemented to extract thermal features from steady-state and dynamic imaging to fed machine learning algorithms for different classification tasks.…”

Section: Thermal Measurementsmentioning

confidence: 99%

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Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

et al. 2021

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Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8–93.2%) and specificity (85.2%, 95%CI 84.9–85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3–93.1%; specificity 65.2%, 55–74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Thermal Measurementsmentioning

confidence: 99%

Section: Thermal Measurementsmentioning

confidence: 99%

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Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

et al. 2021

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“…Magalhaes et al first tried to distinguish benign from malignant lesions with static IR imaging, reaching a low ACC value of 60% with k-NN classifiers [58]. The overall results were slightly improved when dynamic thermal information was added to the feature input set [59]. Recently, the distinction of melanomas and nevi lesions was successfully performed, reaching ACC and SN values of 84.2% and 91.3% [9].…”

Section: Skin Cancermentioning

confidence: 99%

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

2021

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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.

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Review on the evolving role of infrared thermography in oncological applications

Pokharel,

Luitel,

Khatri

et al. 2024

Infrared Physics & Technology

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Skin neoplasms dynamic thermal assessment

Cited by 5 publications

References 12 publications

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Review on the evolving role of infrared thermography in oncological applications

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