ArticleCancers2022
Development of an Image Analysis-Based Prognosis Score Using Google's Teachable Machine in Melanoma.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multimodal artificial intelligence for enhanced skin cancer diagnosis and prognosis.Discover oncology · 2026Review
- Article
- Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization.Biomedicines · 2025Article
- Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma.Cancers · 2022Review
- Predicting melanoma survival and metastasis with interpretable histopathological features and machine learning models.Frontiers in medicine · 2022Article
- Empowering healthcare professionals with no-code artificial intelligence platforms for model development, a practical demonstration for pathology.Discoveries (Craiova, Romania)Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe increasing number of melanoma patients makes it necessary to establish new strategies for prognosis assessment to ensure follow-up care. Deep-learning-based image analysis of primary melanoma could be a future component of risk stratification.
objectivesTo develop a risk score for overall survival based on image analysis through artificial intelligence (AI) and validate it in a test cohort.
methodsHematoxylin and eosin (H&E) stained sections of 831 melanomas, diagnosed from 2012-2015 were photographed and used to perform deep-learning-based group classification. For this purpose, the freely available software of Google's teachable machine was used. Five hundred patient sections were used as the training cohort, and 331 sections served as the test cohort.
resultsUsing Google's Teachable Machine, a prognosis score for overall survival could be developed that achieved a statistically significant prognosis estimate with an AUC of 0.694 in a ROC analysis based solely on image sections of approximately 250 × 250 µm. The prognosis group "low-risk" (
conclusionsThe study supports the possibility of using deep learning-based classification systems for risk stratification in melanoma. The AI assessment used in this study provides a significant risk estimate in melanoma, but it does not considerably improve the existing risk classification based on the TNM classification.
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