AI Article Synopsis
- Artificial intelligence is making significant strides in clinical applications like dermatopathology, enhancing efficiency, accuracy, and reducing costs through advanced computer-aided diagnostics.
- The study evaluated the effectiveness of deep learning algorithms in diagnosing three skin conditions: nodular basal cell carcinoma, dermal nevi, and seborrheic keratoses, using Whole Slide Images (WSIs) and a specially developed neural network.
- The AI system demonstrated high accuracy, correctly identifying 99.45% of BCCs, 99.4% of dermal nevi, and 100% of seborrheic keratoses, suggesting that deep learning could be a valuable tool for dermatopathologists, potentially streamlining their workflow.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166480 | PMC |
| http://dx.doi.org/10.4103/jpi.jpi_31_18 | DOI Listing |
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