Establishment and application of an artificial intelligence-assisted platform for detection of parasite eggs
10.16250/j.32.1374.2024094
- VernacularTitle:人工智能辅助寄生虫虫卵检测平台的建立与应用
- Author:
Huiyin ZHU
1
,
2
;
Yuting LI
1
;
Daiqian ZHU
1
;
Yaqian WANG
1
;
Jinhong ZHANG
1
;
Shaoxuan CHEN
1
;
Xiaoyuan MA
1
;
Huidi WANG
1
;
Hongjun LI
3
;
Jian LI
1
Author Information
1. School of Basic Medical Sciences, Hubei University of Medicine, Shiyan, Hubei 442000, China
2. Department of Pediatrics, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, China
3. Department of Clinical Laboratory, Weifang Maternal and Child Health Hospital, Weifang, Shandong 261000, China
- Publication Type:Journal Article
- Keywords:
Parasite;
Egg;
Artificial intelligence;
Detection efficiency
- From:
Chinese Journal of Schistosomiasis Control
2024;36(6):643-648
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish an artificial intelligence (AI)-assisted platform for detection of parasite eggs, and to evaluate its detection efficiency and accuracy, so as to provide technical supports for elimination of parasitic diseases. Methods A total of 1 003 slides of Enterobius vermicularis, horkworm, Trichuris trichiura, Clonorchis sinensis, Taenia, Ascaris lumbricoides, Schistosoma japonicum, Paragonimus westermani and Fasciolopsis buski eggs were collected, and converted into digital images with an automatated scanning microscope to create a dataset. Based on the Object Detection platform on the Baidu Easy DL model, an AI-assisted platform for detection of parasite eggs was created through procedures of uploading, labeling, training, evaluation and optimization. Then, 70% of the datasets were randomly selected for model training, and the precision, recall and average accuracy were calculated to evaluate the effectiveness of platform for recognition of parasite eggs. In addition, the platform was deployed on the computer and smart phone terminals for use. Results An AI-assisted platform for detection of parasite eggs was successfully created. If the platform was deployed using the public cloud application programming interface (API), the average accuracy, precision and recall of the platform were 93.42%, 92.55% and 89.32% for recognition of parasite eggs. If the platform was deployed using the offline software development kit (SDK), the average accuracy, precision and recall of the platform were 92.97%, 94.78% and 87.63% for recognition of parasite eggs. In addition, the precision of the platform was 97.00% and 96.23% for identification of Taenia and C. sinensis eggs, respectively. Conclusions The AI-assisted platform for detection of parasite eggs has been successfully created, which is high in the accuracy for recognition of parasite eggs and convenient in use. This platform may provide a powerful technical support for parasitic disease diagnosis.
