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. 2018 Sep 27:9:32.
doi: 10.4103/jpi.jpi_31_18. eCollection 2018.

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

Thomas George Olsen  1   2 , B Hunter Jackson  3 , Theresa Ann Feeser  2 , Michael N Kent  1   2 , John C Moad  1   2 , Smita Krishnamurthy  1   2 , Denise D Lunsford  2 , Rajath E Soans  3
Affiliations

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

Thomas George Olsen et al. J Pathol Inform. .

Abstract

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.

Keywords: Artificial intelligence; computational pathology; computer-aided diagnosis; deep learning algorithm; dermatopathology; digital pathology; whole slide images.

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Conflict of interest statement

Dr. Thomas Olsen is an owner at DLCS-Clearpath, LLC.

Figures

Figure 1
Figure 1
Nodular basal cell carcinoma. Multiple H and E-stained sections annotated for artificial intelligence training phase (×1 magnification)
Figure 2
Figure 2
Nodular basal cell carcinoma. Probability heat map visualization generated by deep learning model indicates high probability regions of nodular basal cell carcinoma (×3 magnification)
Figure 3
Figure 3
Nodular basal cell carcinoma. Receiver operating characteristics curve for binary detection
Figure 4
Figure 4
Dermal nevus. Receiver operating characteristics curve for binary detection
Figure 5
Figure 5
Seborrheic keratosis. Receiver operating characteristics curve for binary detection
Figure 6
Figure 6
Dermal nevus. H and E-stained whole slide images not identified by algorithm (false negative) (×2 magnification)
See this image and copyright information in PMC

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