期刊
JOURNAL OF DENTAL RESEARCH
卷 101, 期 11, 页码 1321-1327出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/00220345221089858
关键词
machine learning; oral pathology; biopsy; oral cancer; medical image; convolutional neural network
资金
- National Natural Science Foundation of China [81771048]
- Major Special Science and Technology Project of Sichuan Province [2022ZDZX0031, 2018GZDZX0024]
- Sichuan Science and Technology Program [2020YFG0288]
- Science and Technology Department of Sichuan Province [2020YFS0087, 2020YFS0170]
- Research and Development Program, West China Hospital of Stomatology [RD-03-202012]
- central government guides the construction of local scientific and technological innovation funding [2021ZYD0104]
This study developed a deep learning model to assist pathologists in diagnosing oral squamous cell carcinoma (OSCC), demonstrating improved diagnostic accuracy and speed in both junior and senior pathologists.
Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.
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