Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2021
A machine-learning algorithm for the reliable identification of oral lichen planus.
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Idrees Majdy, Farah Camile S, Shearston Kate, Kujan Omar
Аннотация
BACKGROUND: Oral lichen planus (OLP) is a relatively common oral disorder which shares clinical and histopathological features with other lichenoid lesions, leading to considerable inter-observer disagreement. This negatively impacts understanding of the pathogenesis and malignant transformation potential of this condition.
METHODS: Artificial intelligence was employed to create a machine-learning artificial neural network to identify and quantify mononuclear cells and granulocytes within the inflammatory infiltrates in digitized hematoxylin and eosin microscopic slides. Twenty-four regions of interest were extracted from OLP cases for learning purposes and validated on a retrospective cohort of 130 cases. All cases were related to patients with confirmed diagnoses of OLP, oral lichenoid lesions (OLLs), or oral epithelial dysplasia (OED) with lichenoid host response.
RESULTS: The number of inflammatory cells was statistically significantly higher in OLP compared to OLLs or OED with lichenoid host response (p < 0.0005). The proposed machine-learning method was reliably capable of detecting OLP cases based on the number of inflammatory cells and the number of mononuclear cells with an area under the curve of 0.982 and 0.988, respectively. Identifying a cut-off point between OLP and other lichenoid conditions based on the number of mononuclear cells showed a sensitivity of 100% and an accuracy of 94.62%.
CONCLUSION: Artificial intelligence has shown promising outcomes and provides a robust approach to enhance the accuracy of anatomical pathologists in accurately diagnosing OLP using features of disease pathogenesis.
METHODS: Artificial intelligence was employed to create a machine-learning artificial neural network to identify and quantify mononuclear cells and granulocytes within the inflammatory infiltrates in digitized hematoxylin and eosin microscopic slides. Twenty-four regions of interest were extracted from OLP cases for learning purposes and validated on a retrospective cohort of 130 cases. All cases were related to patients with confirmed diagnoses of OLP, oral lichenoid lesions (OLLs), or oral epithelial dysplasia (OED) with lichenoid host response.
RESULTS: The number of inflammatory cells was statistically significantly higher in OLP compared to OLLs or OED with lichenoid host response (p < 0.0005). The proposed machine-learning method was reliably capable of detecting OLP cases based on the number of inflammatory cells and the number of mononuclear cells with an area under the curve of 0.982 and 0.988, respectively. Identifying a cut-off point between OLP and other lichenoid conditions based on the number of mononuclear cells showed a sensitivity of 100% and an accuracy of 94.62%.
CONCLUSION: Artificial intelligence has shown promising outcomes and provides a robust approach to enhance the accuracy of anatomical pathologists in accurately diagnosing OLP using features of disease pathogenesis.