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Public Health Genomics and Precision Health Knowledge Base (v9.0)
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Precision Health Database|Search|Public Health Genomics and Precision Health Knowledge Base (PHGKB)
Last data update: Apr 23, 2024
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Using artificial intelligence-based technologies to support the diagnosis and early detection of melanoma in primary care.
Owain T Jones et al. Br J Dermatol 2024
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Basic principles of artificial intelligence in dermatology explained using melanoma.
Tim Hartmann et al. J Dtsch Dermatol Ges 2024
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Unveiling the power of convolutional neural networks in melanoma diagnosis.
Loïc Van Dieren et al. Eur J Dermatol 2024 33(5) 495-505
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Predicting Malignant Transformation of Choroidal Nevi Using Machine Learning.
Sabrina P Iddir et al. Res Sq 2024
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Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.
E V Goessinger et al. J Eur Acad Dermatol Venereol 2023
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Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance.
Lucy Thomas et al. Front Med (Lausanne) 2023 101264846
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Deep-learning-based survival prediction of patients with lower limb melanoma.
Jinrong Zhang et al. Discov Oncol 2023 14(1) 218
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Artificial Intelligence in Melanoma Dermatopathology: A Review of Literature.
Hannah Neimy et al. Am J Dermatopathol 2023
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Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review.
Raj H Patel et al. Cancers (Basel) 2023 15(19)
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Predicting cutaneous malignant melanoma patients' survival using deep learning: a retrospective cohort study.
Siyu Cai et al. J Cancer Res Clin Oncol 2023
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Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?
Sara E Cerminara et al. Eur J Cancer 2023 190112954
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Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study)
M Marchetti et al, NPJ Digital Medicine, July 12, 2023
Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review.
Haleigh Stafford et al. Cancers (Basel) 2023 15(12)
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Deep-learning-based survival prediction of patients with cutaneous malignant melanoma.
Hai Yu et al. Front Med (Lausanne) 2023 101165865
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A decision support system for the identification of metastases of Metastatic Melanoma using whole-body FDG PET/CT images.
Theodoros P Vagenas et al. IEEE journal of biomedical and health informatics 2023 PP
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CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma.
Laurens S Ter Maat et al. European journal of cancer (Oxford, England : 1990) 2023 185167-177
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Clinical Application of Artificial Intelligence for Non-melanoma Skin Cancer.
Katherine Sanchez et al. Current treatment options in oncology 2023 1-7
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Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks.
Anand K Nambisan et al. Cancers 2023 15(4)
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Machine learning to predict overall short-term mortality in cutaneous melanoma.
C Cozzolino et al. Discover. Oncology 2023 14(1) 13
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Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare.
Kleinberg Giona et al. Journal of biomed research 2023 3(1) 42-47
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Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.
Jin Wenyi et al. Frontiers in immunology 2022 131025330
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Computer Aided Diagnosis of Melanoma Using Deep Neural Networks and Game Theory: Application on Dermoscopic Images of Skin Lesions.
Foahom Gouabou Arthur Cartel et al. International journal of molecular sciences 2022 23(22)
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Prediction of early-stage melanoma recurrence using clinical and histopathologic features.
Wan Guihong et al. NPJ precision oncology 2022 6(1) 79
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Automatic Malignant and Benign Skin Cancer Classification Using a Hybrid Deep Learning Approach.
Bassel Atheer et al. Diagnostics (Basel, Switzerland) 2022 12(10)
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Artificial Intelligence in Dermatology: Challenges and Perspectives.
Liopyris Konstantinos et al. Dermatology and therapy 2022
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Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review.
Li Suli et al. Mediators of inflammation 2022 20221734327
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A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.
Jiang Zijing et al. IEEE/ACM transactions on computational biology and bioinformatics 2022 PP
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An Online Prognostic Application for Melanoma Based on Machine Learning and Statistics.
Liu Wenhui et al. Journal of plastic, reconstructive & aesthetic surgery : JPRAS 2022
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Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients' Perception.
Jahn Anna Sophie et al. Cancers 2022 14(15)
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Automatic skin lesions detection from images through microscopic hybrid features set and machine learning classifiers.
Alyami Jaber et al. Microscopy research and technique 2022
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Page last reviewed:
Feb 1, 2024
Page last updated:
Apr 23, 2024
Content source:
Public Health Genomics Branch in the Division of Blood Disorders and Public Health Genomics
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National Center on Birth Defects and Developmental Disabilities
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