Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2018  |  Volume : 9  |  Issue : 1  |  Page : 32

Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology


1 Department of Dermatology, Boonshoft School of Medicine, Wright State University School of Medicine; Dermatopathology Laboratory of Central States, Dayton, Ohio, USA
2 Proscia Inc., Philadelphia, Pennsylvania, USA
3 Dermatopathology Laboratory of Central States, Dayton, Ohio, USA

Correspondence Address:
Dr. Michael N Kent
7835 Paragon Rd, Dayton, Ohio 45459
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_31_18

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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.


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