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<p>Melanoma has a growing incidence, and early and accurate detection can enhance survival rates. However, its complex diagnoses pose challenges that could be overcome by Artificial Intelligence (AI), and the ABCD(E) rule (Asymmetry, Border irregularity, Color variegation, Diameter, and Evolution) is a major screening pattern enabling melanoma detection. This systematic review assessed advancements in Artificial Intelligence (AI) for melanoma diagnosis, particularly focusing on systems incorporating or informed by ABCD(E) criteria. Guided by Cochrane and PRISMA guidelines, this review included experimental or observational studies indexed on PubMed, LILACS, Latindex, and Cochrane Central Register of Controlled Trials platforms. Independent screening and data extraction were performed by two authors. 2,735 records were assessed and 21 studies were included in the final screening, analyzing over 90,000 images/cases. AI systems, notably CNNs and DCNNs, demonstrated promising diagnostic performance, often matching or exceeding human expert evaluation, achieving an Area Under the Curve (AUC) of 90.3% for early melanoma differentiation. Patient selection bias was observed, limiting applicability of the findings. AI shows significant and evolving capability in skin lesion diagnosis, increasingly integrating ABCD(E) criteria. While offering enhanced diagnostic consistency, challenges persist, including data limitations, model generalizability, and interpretability.</p>

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Artificial intelligence (AI) in melanocytic lesions diagnosis: a systematic review of methods incorporating ABCD(E)-derived features

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Otto Muller Silva Lopes,
  • Juliana Almeida Oliveira,
  • Victor Brunno Alves Nogueira,
  • Ludmila Fausto Ribeiro,
  • Mara Inês Stefanini Zamae,
  • Matheus Figueiredo Garrocho Ottoni Vieira,
  • Vinicius Marciano Dantas e Pimenta,
  • Zilma Silveira Nogueira Reis

摘要

Melanoma has a growing incidence, and early and accurate detection can enhance survival rates. However, its complex diagnoses pose challenges that could be overcome by Artificial Intelligence (AI), and the ABCD(E) rule (Asymmetry, Border irregularity, Color variegation, Diameter, and Evolution) is a major screening pattern enabling melanoma detection. This systematic review assessed advancements in Artificial Intelligence (AI) for melanoma diagnosis, particularly focusing on systems incorporating or informed by ABCD(E) criteria. Guided by Cochrane and PRISMA guidelines, this review included experimental or observational studies indexed on PubMed, LILACS, Latindex, and Cochrane Central Register of Controlled Trials platforms. Independent screening and data extraction were performed by two authors. 2,735 records were assessed and 21 studies were included in the final screening, analyzing over 90,000 images/cases. AI systems, notably CNNs and DCNNs, demonstrated promising diagnostic performance, often matching or exceeding human expert evaluation, achieving an Area Under the Curve (AUC) of 90.3% for early melanoma differentiation. Patient selection bias was observed, limiting applicability of the findings. AI shows significant and evolving capability in skin lesion diagnosis, increasingly integrating ABCD(E) criteria. While offering enhanced diagnostic consistency, challenges persist, including data limitations, model generalizability, and interpretability.