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

Autor:	Rajath E. Soans, Theresa A. Feeser, Michael N. Kent, Smita Krishnamurthy, Thomas G. Olsen, B Hunter Jackson, Denise D Lunsford, John C. Moad
Rok vydání:	2018
Předmět:	0301 basic medicine Artificial intelligence Artificial Intelligence System whole slide images Computer science Health Informatics lcsh:Computer applications to medicine. Medical informatics Convolutional neural network Pathology and Forensic Medicine 03 medical and health sciences 0302 clinical medicine lcsh:Pathology Medical diagnosis dermatopathology business.industry Deep learning Digital pathology Computer Science Applications 030104 developmental biology Computer-aided diagnosis 030220 oncology & carcinogenesis Pattern recognition (psychology) lcsh:R858-859.7 Original Article computer-aided diagnosis Dermatopathology digital pathology business Algorithm lcsh:RB1-214 computational pathology deep learning algorithm
Zdroj:	Journal of Pathology Informatics Journal of Pathology Informatics, Vol 9, Iss 1, Pp 32-32 (2018)
ISSN:	2153-3539
DOI:	10.4103/jpi.jpi_31_18
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a32cc48044db036fd5917bebc993a5b9 https://doi.org/10.4103/jpi.jpi_31_18 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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