Diagnostic Performance of Deep Learning Algorithms Applied to Three Common Diagnoses in Dermatopathology

• Published in: Journal of pathology informatics Vol. 9; no. 1; p. 32
• Main Authors: Olsen, Thomas George, Jackson, B. Hunter, Feeser, Theresa Ann, Kent, Michael N., Moad, John C., Krishnamurthy, Smita, Lunsford, Denise D., Soans, Rajath E.
• Format: Journal Article
• Language: English
• Published: India Elsevier Inc 01.01.2018; Wolters Kluwer India Pvt. Ltd; Medknow Publications & Media Pvt. Ltd; Medknow Publications & Media Pvt Ltd; Elsevier
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Breast cancer; computational pathology; computer-aided diagnosis; Data collection; Deep learning; deep learning algorithm; dermatopathology; Diagnosis; Diagnostic systems; digital pathology; Efficiency; FDA approval; Ground truth; Image classification; Laboratories; Medical imaging; Medicine; Morphology; Original; Pathology; Pattern recognition; Power; Software; Studies; Tumors; whole slide images; Workflow; United States > US; Ohio; deep learning algorithm; dermatopathology; computational pathology; digital pathology; computer-aided diagnosis; Artificial intelligence; whole slide images
• ISSN: 2153-3539; 2229-5089; 2153-3539
• DOI: 10.4103/jpi.jpi_31_18

Background: Artificial 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. Aims: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. Methods: Whole 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. Results: Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. Conclusions: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.