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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Machine learning &#40;ML&#41; is a subset of artificial intelligence &#40;AI&#41; which most often utilizes image recognition and analysis for the diagnosis in most of the medical fields like dermatology&#44; ophthalmology&#44; radiology and medicine&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">1</span></a> AI has potential role in dermatology such as screening and diagnosis of melanoma&#44; basal cell carcinoma &#40;BCC&#41;&#44; psoriasis and other inflammatory dermatoses&#46; ML is a method of creating AI&#46; It has various approaches viz&#46;&#44; supervised&#44; unsupervised and semi-supervised or reinforcement learning&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">2</span></a> Supervised approach uses labelled data and has been utilized in detecting benign versus malignant skin lesions&#46; Unsupervised learning approach has been utilized in detecting dermoscopic diagnosis of BCC&#46; Machine learning can also be applied in chronic urticaria &#40;CU&#41;&#46; Recently&#44; in a study by T&#252;rk et al&#46;&#44;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">3</span></a> authors have tried to distinguish different chronic urticaria &#40;CU&#41; subtypes phenotypically and pathogenetically through unsupervised model of machine learning&#46; The authors have generated four clusters in their study which corresponded to a specific phenotypes and biomarkers&#46; ML has much more potential in CU&#46; Lesions in CU look the same but ML can also play a role in defining the severity through the number and size of the wheal&#46; Larger wheal size corresponds to more severe and difficult to treat CU&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">4</span></a> The number of lesions may be detected through an algorithm in ML which may aid in generating another cluster of CU with increased severity&#46; The clustering in unsupervised learning is advantageous when data seems substantially different to one another&#46; However&#44; in CU data may not vary much therefore&#44; there is a concept of semi-supervised learning in ML which utilizes both labelled and unlabelled data&#46;<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">2</span></a> This approach would be much practical and easy which can utilize less labelled data and more unlabelled data for its operation&#46; The qualitative data other than the images may be better utilized in defining and differentiating subtypes of CU through this learning&#46; The unsupervised learning may be more helpful in generating new clusters through the unlabelled data&#44; but addition of labelled data may further provide precise information in CU&#46;</p><p id="par0010" class="elsevierStylePara elsevierViewall">Moreover&#44; addition of data for the validated scores such as UAS7 &#40;urticaria activity score&#41; and UCT &#40;urticaria control test&#41; would really augment in classifying and differentiating the disease activity and control through ML&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">AI in CU offers an innovative approach to develop diagnostic algorithms which may potentially aid in diagnosis and classifying the subtypes of CU&#46; It may also augment in evaluation of multiple modalities or issues at the same time&#46; Albeit&#44; the development and validation of AI algorithms require large data inputs either learned or labelled data and unlabelled but ML in dermatology especially CU is a new untouched field which have bright future prospects&#46; Therefore&#44; large studies are required in future in this field to validate the findings&#46;</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflict of interests</span><p id="par0020" class="elsevierStylePara elsevierViewall">The author declares no conflict of interest&#46;</p></span></span>"
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