Artificial intelligence (AI) in melanocytic lesions diagnosis: a systematic review of methods incorporating ABCD(E)-derived features
5 affiliations
- 1Federal University of Minas Gerais, Av. Prof. Alfredo Balena, 190 - Santa Efigênia, Belo Horizonte, Minas Gerais 30130-100 Brazil
- 2Universidade José do Rosário Vellano, Belo Horizonte, Minas Gerais Brazil
- 3Instituto Nacional do Câncer, Rio de Janeiro, Brazil
- 4Faculdade de Ciências Médicas de Ipatinga, Ipatinga, Minas Gerais Brazil
- 5Department of Gynecology and Obstetrics, Federal University of Minas Gerais, Belo Horizonte, Brazil
Abstract
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.
Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00432-026-06519-4.
Related
- Generative Artificial Intelligence Models in Breast Cancer Diagnosis: A Systematic Review
- Artificial Intelligence in Eye Disease: A Systematic Review of Screening, Diagnosis, Prognosis, Treatment-Response Prediction, and Clinical Decision Support
- Advances in artificial intelligence for brain imaging and computer-aided diagnosis of neurological diseases: a systematic literature review.
- Artificial intelligence empowering the diagnosis and treatment of rotator cuff tears: A systematic review from image optimization to prognosis prediction.
- Diagnostic accuracy of tongue diagnosis for stroke syndrome differentiation in traditional Chinese medicine: a systematic review, meta-analysis, and narrative synthesis of artificial intelligence-assisted models.
BibTeX
@article{lopes2026artificial,
title = {{Artificial intelligence (AI) in melanocytic lesions diagnosis: a systematic review of methods incorporating ABCD(E)-derived features}},
author = {Otto Muller Silva Lopes and Juliana Almeida Oliveira and Victor Brunno Alves Nogueira and Ludmila Fausto Ribeiro and Mara Inês Stefanini Zamae and Matheus Figueiredo Garrocho Ottoni Vieira and Vinicius Marciano Dantas e Pimenta and Zilma Silveira Nogueira Reis},
journal = {Journal of Cancer Research and Clinical Oncology},
year = {2026},
month = jul,
doi = {10.1007/s00432-026-06519-4},
url = {https://doi.org/10.1007/s00432-026-06519-4},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence (AI) in melanocytic lesions diagnosis: a systematic review of methods incorporating ABCD(E)-derived features
AU - Otto Muller Silva Lopes
AU - Juliana Almeida Oliveira
AU - Victor Brunno Alves Nogueira
AU - Ludmila Fausto Ribeiro
AU - Mara Inês Stefanini Zamae
AU - Matheus Figueiredo Garrocho Ottoni Vieira
AU - Vinicius Marciano Dantas e Pimenta
AU - Zilma Silveira Nogueira Reis
PY - 2026
DA - 2026/07/01
JO - Journal of Cancer Research and Clinical Oncology
DO - 10.1007/s00432-026-06519-4
UR - https://doi.org/10.1007/s00432-026-06519-4
AB - 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. Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s00432-026-06519-4.
SN - 0171-5216
SN - 1432-1335
ER -