Application and evaluation of artificial intelligence TPS-assisted cytologic screening system in urine exfoliative cytology.
10.3760/cma.j.cn112151-20230831-00115
- Author:
L ZHU
1
,
2
;
M L JIN
3
;
S R HE
4
;
H M XU
5
;
J W HUANG
6
;
L F KONG
1
,
2
;
D H LI
1
,
2
;
J X HU
1
,
2
;
X Y WANG
1
,
2
;
Y W JIN
1
,
2
;
H HE
1
,
2
;
X Y WANG
7
;
Y Y SONG
7
;
X Q WANG
7
;
Z M YANG
7
;
A X HU
1
,
2
Author Information
1. Department of Pathology, Henan People's Hospital/Zhengzhou University People's Hospital
2. Zhengzhou 450003, China.
3. Department of Pathology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China.
4. Department of Pathology, Beijing Hospital, Beijing 100730, China.
5. Department of Pathology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
6. Department of Pathology, Luoyang Central Hospital, Luoyang 471000, China.
7. iDeepwise Artificial Intelligence Robot Technology (Beijing) Limited Company, Beijing 100089, China.
- Publication Type:Journal Article
- MeSH:
Humans;
Artificial Intelligence;
Urothelium/pathology*;
Cytodiagnosis;
Epithelial Cells/pathology*;
Sensitivity and Specificity;
Urologic Neoplasms/urine*
- From:
Chinese Journal of Pathology
2023;52(12):1223-1229
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the application of manual screening collaborated with the Artificial Intelligence TPS-Assisted Cytologic Screening System in urinary exfoliative cytology and its clinical values. Methods: A total of 3 033 urine exfoliated cytology samples were collected at the Henan People's Hospital, Capital Medical University, Beijing, China. Liquid-based thin-layer cytology was prepared. The slides were manually read under the microscope and digitally presented using a scanner. The intelligent identification and analysis were carried out using an artificial intelligence TPS assisted screening system. The Paris Report Classification System of Urinary Exfoliated Cytology 2022 was used as the evaluation standard. Atypical urothelial cells and even higher grade lesions were considered as positive when evaluating the recognition sensitivity, specificity, and diagnostic accuracy of artificial intelligence-assisted screening systems and human-machine collaborative cytologic screening methods in urine exfoliative cytology. Among the collected cases, there were also 1 100 pathological tissue controls. Results: The accuracy, sensitivity and specificity of the AI-assisted cytologic screening system were 77.18%, 90.79% and 69.49%; those of human-machine coordination method were 92.89%, 99.63% and 89.09%, respectively. Compared with the histopathological results, the accuracy, sensitivity and specificity of manual reading were 79.82%, 74.20% and 95.80%, respectively, while those of AI-assisted cytologic screening system were 93.45%, 93.73% and 92.66%, respectively. The accuracy, sensitivity and specificity of human-machine coordination method were 95.36%, 95.21% and 95.80%, respectively. Both cytological and histological controls showed that human-machine coordination review method had higher diagnostic accuracy and sensitivity, and lower false negative rates. Conclusions: The artificial intelligence TPS assisted cytologic screening system has achieved acceptable accuracy in urine exfoliation cytologic screening. The combination of manual screening and artificial intelligence TPS assisted screening system can effectively improve the sensitivity and accuracy of cytologic screening and reduce the risk of misdiagnosis.
