ArticleJournal of pathology informatics2018
Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology.
Article in Journal of pathology informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- Revolutionizing dermatopathology using AI in skin diagnostics: scoping review.Frontiers in medicine · 2026Pooled it
- The accuracy of artificial intelligence used for non-melanoma skin cancer diagnoses: a meta-analysis.BMC medical informatics and decision making · 2023Pooled it
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Melan-Dx: a knowledge-enhanced vision-language framework improves differential diagnosis of melanocytic neoplasm pathology.NPJ digital medicine · 2026Article
- Performance of artificial intelligence chatbots in the diagnosis and management of simulated dental trauma cases: an evaluation based on IADT guidelines.Clinical oral investigations · 2025Article
- Artificial Intelligence for Lentigo Maligna: Automated Margin Assessment via Sox-10-Based Melanocyte Density Mapping.Dermatopathology (Basel, Switzerland) · 2025Article
- Large-Scale Dermatopathology Dataset for Lesion Segmentation: Model Development and Analysis.Journal of Korean medical science · 2025Article
- Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives.Medical sciences (Basel, Switzerland) · 2025Review
- Enhancing basal cell carcinoma classification in preoperative biopsies via transfer learning with weakly supervised graph transformers.BMC medical imaging · 2025Article
- Assessing the performance of an artificial intelligence based chatbot in the differential diagnosis of oral mucosal lesions: clinical validation study.Clinical oral investigations · 2025Article
- Comprehensive Evaluation of Multi-Omics Clustering Algorithms for Cancer Molecular Subtyping.International journal of molecular sciences · 2025Article
- Mini review on skin biopsy: traditional and modern techniques.Frontiers in medicine · 2025Review
- Integrating telepathology and digital pathology with artificial intelligence: An inevitable future.Veterinary world · 2024Review
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Weakly supervised detection and classification of basal cell carcinoma using graph-transformer on whole slide images.Scientific reports · 2023Article
- The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.Joint diseases and related surgery · 2023Article
- High-fidelity detection, subtyping, and localization of five skin neoplasms using supervised and semi-supervised learning.Journal of pathology informatics · 2023Article
- Improvement of Mucosal Lesion Diagnosis with Machine Learning Based on Medical and Semiological Data: An Observational Study.Journal of clinical medicine · 2022Article
- A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements.International wound journal · 2022Article
- Evaluation of a Deep Learning Approach to Differentiate Bowen's Disease and Seborrheic Keratosis.Cancers · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms.
aimsThis study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses.
methodsWhole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis.
resultsArtificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses.
conclusionsArtificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.