Contact Dermatitis · 2019 · 11 citations · 12 references
MedicineDermatologyBiomedical ImagingDermatopathologyDiagnostic ImagingHand DisordersImaging AssessmentMultispectral ImagingSkin AssessmentSkin ExaminationClinical Image AssessmentGlobal Assessment FrameworksHand ImagingImage-based Disease Severity AssessmentImage-based Severity AssessmentMorphological GradingQuantitative Severity MeasurementSeverity Assessment
BACKGROUND: Hand eczema is a disease with large variation in clinical presentation and severity. Scoring systems for quantitative severity assessment exist. However, they are observer-dependent. An objective quantitative tool for scoring of hand eczema would improve categorization of hand eczema. OBJECTIVE: To investigate the usefulness of multispectral imaging in assessing severity of hand eczema with respect to extent and the different morphological features. METHODS: Patients with hand eczema (n = 60) and healthy controls (n = 28) were included. The severity of hand eczema was assessed by a dermatologist using the Hand Eczema Severity Index (HECSI) and a global assessment (Physician Global Assessment [PGA]). Multispectral imaging of the hand was performed on all patients and controls using the VideometerLab Instrument. RESULTS: Areas of the morphological elements identified by multispectral imaging were statistically significantly correlated with the PGA scores. Analyzed by Cohen's kappa, a moderate agreement between imaging-based severity assessment and PGA was found. The imaging-based severity assessment was also correlated with HECSI (Spearman rho 0.683, P < .001). Still, the imaging-based algorithm was not capable of differentiating hand eczema patients from controls. CONCLUSIONS: Multispectral imaging allows quantitative measurements of different skin parameters to be performed. In its present form, multispectral imaging cannot replace the clinical assessment of a dermatologist. However, after refinement, this or similar technologies could prove useful.
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A. Novoa et al. · Nature · 2017 · 13.8K citations · Full text