Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Betz-Stablein, B; D'Alessandro, B; Koh, U; Plasmeijer, E; Janda, M; Menzies, SW; Hofmann-Wellenhof, R; Green, AC; Soyer, HP.
Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.
Dermatology. 2022; 238(1):4-11
Doi: 10.1159/000517218
[OPEN ACCESS]
Web of Science
PubMed
FullText
FullText_MUG
- Co-Autor*innen der Med Uni Graz
- Hofmann-Wellenhof Rainer
- Soyer Hans Peter
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuable tool for image classification in dermatology. OBJECTIVES: To test whether automated, reproducible naevus counts are possible through the combination of convolutional neural networks (CNN) and three-dimensional (3D) total body imaging. METHODS: Total body images from a study of naevi in the general population were used for the training (82 subjects, 57,742 lesions) and testing (10 subjects; 4,868 lesions) datasets for the development of a CNN. Lesions were labelled as naevi, or not ("non-naevi"), by a senior dermatologist as the gold standard. Performance of the CNN was assessed using sensitivity, specificity, and Cohen's kappa, and evaluated at the lesion level and person level. RESULTS: Lesion-level analysis comparing the automated counts to the gold standard showed a sensitivity and specificity of 79% (76-83%) and 91% (90-92%), respectively, for lesions ≥2 mm, and 84% (75-91%) and 91% (88-94%) for lesions ≥5 mm. Cohen's kappa was 0.56 (0.53-0.59) indicating moderate agreement for naevi ≥2 mm, and substantial agreement (0.72, 0.63-0.80) for naevi ≥5 mm. For the 10 individuals in the test set, person-level agreement was assessed as categories with 70% agreement between the automated and gold standard counts. Agreement was lower in subjects with numerous seborrhoeic keratoses. CONCLUSION: Automated naevus counts with reasonable agreement to those of an expert clinician are possible through the combination of 3D total body photography and CNNs. Such an algorithm may provide a faster, reproducible method over the traditional in person total body naevus counts.
- Find related publications in this database (using NLM MeSH Indexing)
- Adult - administration & dosage
- Aged - administration & dosage
- Early Detection of Cancer - methods
- Female - administration & dosage
- Humans - administration & dosage
- Imaging, Three-Dimensional - administration & dosage
- Male - administration & dosage
- Melanoma - diagnosis
- Middle Aged - administration & dosage
- Neural Networks, Computer - administration & dosage
- Nevus - diagnostic imaging
- Photography - methods
- Reproducibility of Results - administration & dosage
- Sensitivity and Specificity - administration & dosage
- Skin Neoplasms - diagnostic imaging
- Whole Body Imaging - methods
- Find related publications in this database (Keywords)
- Melanocytic naevi
- Moles
- Melanoma
- Artificial intelligence
- 3D total body imaging