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Remote Assessment of Eczema Severity via AI-powered Skin Image Analytics: A Systematic Review.
Huang, Leo; Tang, Wai Hoh; Attar, Rahman; Gore, Claudia; Williams, Hywel C; Custovic, Adnan; Tanaka, Reiko J.
Afiliación
  • Huang L; Department of Bioengineering, Imperial College London, UK; UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, UK; Department of Computing, Imperial College London, UK.
  • Tang WH; Department of Bioengineering, Imperial College London, UK.
  • Attar R; Department of Bioengineering, Imperial College London, UK; School of Electronics and Computer Science, University of Southampton, UK.
  • Gore C; Department of Paediatric Allergy, Imperial College Healthcare NHS Trust, UK.
  • Williams HC; Centre of Evidence Based Dermatology, University of Nottingham, UK.
  • Custovic A; National Heart & Lung Institute, Imperial College London, UK.
  • Tanaka RJ; Department of Bioengineering, Imperial College London, UK. Electronic address: r.tanaka@imperial.ac.uk.
Artif Intell Med ; 156: 102968, 2024 10.
Article en En | MEDLINE | ID: mdl-39213813
ABSTRACT
Various studies have been published on the remote assessment of eczema severity from digital camera images. Successful deployment of an accurate and robust AI-powered tool for such purposes can aid the formulation of eczema treatment plans and assist in patient monitoring. This review aims to provide an overview of the quality of published studies on this topic and to identify challenges and suggestions to improve the robustness and reliability of existing tools. We identified 25 articles from the Scopus database that aimed to assess eczema severity automatically from digital camera images by eczema area detection (n=13), which is important for prior delineation of the most relevant clinical features, and/or severity prediction (n=12). Deep learning methods (n=14) were more commonly used in recent years over conventional machine learning (n=11). A set of 20 pre-defined criteria were used for critical appraisal in this study. Study quality was hindered in many cases due to dataset challenges, with only 28% of studies reporting patient age range and 16% reporting skin phototype range. Furthermore, 52% of studies utilised solely non-public datasets and only 17% provided open-source access to code repositories, making validation of experimental results a significant challenge. In terms of algorithm design, attempts to improve model accuracy and process automation are widely reported. However, there remains limited implementation of methods for explicitly improving model trustworthiness and robustness. There is a need for a high-quality dataset with a sufficient number of bias-free images and consistent labels, as well as improved image analytics methods, to enhance the state of remote eczema severity assessment algorithms. Improving the interpretability and explainability of developed tools will further improve long-term reliability and trustworthiness.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Índice de Severidad de la Enfermedad / Eccema Límite: Humans Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Índice de Severidad de la Enfermedad / Eccema Límite: Humans Idioma: En Revista: Artif Intell Med Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article Pais de publicación: Países Bajos