Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development.
10.1007/s11684-020-0791-8
- Author:
Dongping NING
1
;
Zhan ZHANG
2
;
Kun QIU
2
;
Lin LU
3
;
Qin ZHANG
4
;
Yan ZHU
5
;
Renzhi WANG
6
Author Information
1. Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
2. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China.
3. Key Laboratory of Endocrinology of National Health Commission, Department of Endocrinology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. lulin88@sina.com.
4. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China. qinzhang@tsinghua.edu.cn.
5. Institute of Internet Industry, Tsinghua University, Beijing, 100084, China.
6. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China.
- Publication Type:Journal Article
- Keywords:
disorders of sex development (DSD);
dynamic uncertain causality graph;
intelligent diagnosis
- From:
Frontiers of Medicine
2020;14(4):498-505
- CountryChina
- Language:English
-
Abstract:
Disorders of sex development (DSD) are a group of rare complex clinical syndromes with multiple etiologies. Distinguishing the various causes of DSD is quite difficult in clinical practice, even for senior general physicians because of the similar and atypical clinical manifestations of these conditions. In addition, DSD are difficult to diagnose because most primary doctors receive insufficient training for DSD. Delayed diagnoses and misdiagnoses are common for patients with DSD and lead to poor treatment and prognoses. On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis model for DSD was jointly constructed by experts on DSD and engineers of artificial intelligence. "Chaining" inference algorithm and weighted logic operation mechanism were applied to guarantee the accuracy and efficiency of diagnostic reasoning under incomplete situations and uncertain information. Verification was performed using 153 selected clinical cases involving nine common DSD-related diseases and three causes other than DSD as the differential diagnosis. The model had an accuracy of 94.1%, which was significantly higher than that of interns and third-year residents. In conclusion, the DUCG model has broad application prospects as a computer-aided diagnostic tool for DSD-related diseases.
