Abstract Neural networks (NNs), a subset of artificial intelligence (AI), have the ability to assess the severity of vitiligo and improve diagnosis accuracy. In contrast to human physicians’ diagnosis, which is primarily based on subjective assessment, standardised and objective diagnostic methods are needed to provide an accurate diagnosis and grade the severity of vitiligo. The aim of this study is to analyse the sensitivity and specificity of AI in vitiligo diagnosis and severity assessment. The PRISMA 2020 protocol was followed to screen literature in several databases: Proquest, PubMed, Taylor and Francis, SAGE, JSTOR, and ScienceDirect. Human studies that investigated the potential of AI in vitiligo diagnosis were included in vitiligo diagnosis were included. Statistical analysis was carried out using RevMan 5.4 and RStudio, and risk of bias tools were used to assess study bias. Certainty of evidence was analysed using GRADE. The systematic review consisted of 15 studies, and seven studies were statistically analysed. The most studied NNs were Inception V3 (n = 2), visual geometry group (n = 2), and ResNet (n = 2). The overall sensitivity ranged from 72.5% to 99.7%, with a pooled sensitivity of 95.5%. Specificity ranged from 72% to 99.9%, with a pooled specificity of 97%. Among the evaluated models, Inception V3 showed the highest diagnostic performance. The summary receiver operating characteristics curve (SROC) from multiple analyses showed an area under the curve of 0.96. AI showed potential in diagnosing and evaluating the severity of vitiligo.
Stella et al. (Thu,) studied this question.