Preliminary application of artificial intelligence in the pathological diagnosis of periapical cysts
10.12016/j.issn.2096-1456.2023.09.005
- Author:
HAO Yihang
1
,
2
;
HUANG Meichang
1
,
3
;
LI Mao
1
,
3
;
TANG Yaling
1
,
3
;
LIANG Xinhua
1
,
2
Author Information
1. State Key Laboratory of Oral Diseases &
2. National Clinical Research Center for Oral Diseases, Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University
3. National Clinical Research Center for Oral Diseases, Department of Pathology, West China Hospital of Stomatology, Sichuan University, Chengdu
- Publication Type:Journal Article
- Keywords:
periapical cyst / pathological diagnosis / radiological diagnosis / artificial intelligence / oral pathology / U-net network / Dice coefficient / receiver operating characteristic curve
- From:
Journal of Prevention and Treatment for Stomatological Diseases
2023;31(9):641-646
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To study the effect of artificial intelligence in the pathological diagnosis of periapical cysts and to explore the application of artificial intelligence in the field of oral pathology.
Methods:Pathological images of eighty-seven periapical cysts were selected as subjects to read, and a neural network with a U-net structure was constructed. The 87 HE images and labeled images of periapical cysts were divided into a training set (72 images) and a test set (15 images), which were used in the training model and test model, respectively. Finally, the target level index F1 score, pixel level index Dice coefficient and receiver operating characteristic (ROC) curve were used to evaluate the ability of the U-net model to recognize periapical cyst epithelium.
Results : The F1 score of the U-net network model for recognizing periapical cyst epithelium was 0.75, and the Dice index and the areas under the ROC curve were 0.685 and 0.878, respectively.
Conclusion:The U-net network model constructed by artificial intelligence has a good segmentation result in identifying periapical cyst epithelium, which can be preliminarily applied in the pathological diagnosis of periapical cysts and is expected to be gradually popularized in clinical practice after further verification with large samples.
