Effectiveness of the artificial intelligence image recognition system in diagnosing endometrial cytopathology

Jing AN; Panyue Yin; Bin Wang; Guizhi Shi; Dexing Zhong; Jianliu Wang; Qiling LI

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Effectiveness of the artificial intelligence image recognition system in diagnosing endometrial cytopathology

VernacularTitle:利用人工智能图像识别系统诊断子宫内膜细胞病理学的有效性研究
Author: Jing AN ¹ ; Panyue YIN ¹ ; Bin WANG ² ; Guizhi SHI ³ ; Dexing ZHONG ⁴ ; Jianliu WANG ⁵ ; Qiling LI ¹
Author Information

1. Department of Obstetrics and Gynecology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061
2. Department of Obstetrics and Gynecology, Xi’an Daxing Hospital, Xi’an 710016
3. Savaid Medical School, University of Chinese Academy of Sciences, Beijing 101408
4. School of Automation Science and Engineering, Xi’an Jiaotong University, Xi’an 710049
5. Department of Obstetrics and Gynecology, Peking University People’s Hospital, Beijing 100044, China
Publication Type:Journal Article
Keywords: endometrial cancer; artificial intelligence (AI); cytology; diagnosis; effectiveness
From: Journal of Xi'an Jiaotong University(Medical Sciences) 2024;45(2):343-347
CountryChina
Language:Chinese
Abstract: 【Objective】 To explore the effectiveness of an image recognition system based on artificial intelligence (AI) in diagnosing benign and malignant endometrial cell clumps. 【Methods】 We selected endometrial cytological specimens from The First Affiliated Hospital of Xi’an Jiaotong University and Xi’an Daxing Hospital from August 2021 to February 2023; histopathology was used as the gold standard. We compared and analyzed the sensitivity, specificity, positive predictive value, negative predictive value, accuracy and diagnostic time of AI image recognition system (AI diagnosis) and professional pathologists’ manual diagnosis (manual diagnosis) of benign and malignant endometrial cell clumps. 【Results】 Among the 126 patients included in the analysis, the overall coincidence rate of AI diagnosis and histological diagnosis was 92.1% (116/126), which was highly consistent with histopathological results (Kappa=0.841). The overall coincidence rate of manual diagnosis and histological diagnosis was 94.4% (119/126), which was highly consistent with histopathological results (Kappa=0.889). There was no statistically significant difference between AI diagnosis and manual diagnosis methods (χ²=0.568, P=0.451). The sensitivity, specificity, positive predictive value, and negative predictive value of AI diagnosis were 91.8%, 92.3%, 91.8%, and 92.3%, respectively. There were 126 cytology sections, each of which required 6.67 minutes for manual diagnosis and 5.00 minutes for AI diagnosis. 【Conclusion】 The AI image recognition system has high diagnostic accuracy, sensitivity and specificity, which is equivalent to the manual diagnosis level of professional pathologists. Therefore, this system has application value in the diagnosis of benign and malignant endometrial cell clumps.