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Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks

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Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks

dc.contributor.author	Cañada Soriano, Mar
dc.contributor.author	Bovaira Forner, Maite
dc.contributor.author	García Vitoria, Carles
dc.contributor.author	Salvador Palmer, M. del Rosario
dc.contributor.author	Cibrián Ortiz de Anda, Rosa María
dc.contributor.author	Moratal Pérez, David
dc.contributor.author	Priego Quesada, José Ignacio
dc.date.accessioned	2023-02-23T14:16:59Z
dc.date.available	2023-02-23T14:16:59Z
dc.date.issued	2023
dc.identifier.uri	https://hdl.handle.net/10550/85538
dc.description.abstract	Purpose There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. Methods 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine. Results All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors. Conclusion These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance.
dc.language.iso	eng
dc.relation.ispartof	Journal of Thermal Biology, 2023, vol. 113, p. 103523-103531
dc.source	Cañada Soriano, Mar Bovaira Forner, Maite García Vitoria, Carles Salvador Palmer, M. del Rosario Cibrián Ortiz de Anda, Rosa Maria Moratal Pérez, David Priego-Quesada, José Ignacio 2023 Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks Journal of Thermal Biology 113 103523 103531
dc.subject	medicina
dc.subject	sistema nerviós malalties
dc.subject	extremitats
dc.title	Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks
dc.type	journal article	es_ES
dc.date.updated	2023-02-23T14:16:59Z
dc.identifier.doi	https://doi.org/10.1016/j.jtherbio.2023.103523
dc.identifier.idgrec	156988
dc.rights.accessRights	open access	es_ES