RESUMO
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.
RESUMO
The increasing bureaucracy of health care has had a serious negative impact on physicians and patients for the last fifteen years. We report an example of this trend involving the interstate interpretation of dermatopathology specimens, which resulted in a significant interruption of diagnostic services to a large number of dermatologists. Temporary and long term solutions are discussed with the hope that dialogue and political consensus-building can bring resolution to the problem.