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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

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Front. Med.    2020, Vol. 14 Issue (4) : 498-505    https://doi.org/10.1007/s11684-020-0791-8
RESEARCH ARTICLE
Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development
Dongping Ning1,2, Zhan Zhang3, Kun Qiu3, Lin Lu1(), Qin Zhang4(), Yan Zhu5, Renzhi Wang6
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. Department of Pediatrics, Linfen Central Hospital, Linfen 041000, China
3. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing 100084, China
4. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
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
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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.
Keywords disorders of sex development (DSD)      intelligent diagnosis      dynamic uncertain causality graph     
Corresponding Author(s): Lin Lu,Qin Zhang   
Just Accepted Date: 19 May 2020   Online First Date: 20 July 2020    Issue Date: 26 August 2020
 Cite this article:   
Dongping Ning,Zhan Zhang,Kun Qiu, et al. Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development[J]. Front. Med., 2020, 14(4): 498-505.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-020-0791-8
https://academic.hep.com.cn/fmd/EN/Y2020/V14/I4/498
Fig.1  Subgraph of the 21OHD model in the DUCG.
Fig.2  Subgraph of the PCOS model in the DUCG.
Fig.3  DUCG with DSDs as the chief complaint combined with 12 subgraphs, two of which are illustrated in Figs. 1 and 2.
Recruited DSD patients Number of ?cases Diagnostic ?accuracy ?of the DUCG ?model Diagnostic ?accuracy ?of interns Diagnostic ?accuracy ?of third-year ?residents P value
21-Alpha hydroxylase deficiency (21OHD) 23 100% 82.6% 86.9% 0.127
11β-Hydroxylase deficiency (11βOHD) 10 90% 70% 70% 0.475
17-Hydroxylase deficiency (17OHD) 20 90% 75% 85% 0.432
Steroidogenic acute regulatory (StAR) protein mutations 11 100% 72.7% 81.8% 0.192
3β-Hydroxysteroid dehydrogenase deficiency (3βHSD) 9 88.9% 55.6% 55.6% 0.223
P450 oxidoreductase deficiency (PORD) 3 100% 0% 66.7% NA
Androgen insensitivity syndrome (AIS) 13 84.6% 53.8% 76.9% 0.293
5α-Reductase deficiency 10 90% 40% 70% 0.080
17β-Hydroxysteroid dehydrogenase deficiency (17βHSD)* 13 100% 53.8% 61.5% 0.019
Androgen-producing tumor 19 89.4% 68.4% 73.7% 0.376
Polycystic ovary syndrome (PCOS)* 12 100% 58.3% 83.3% 0.046
Hypercortisolism 10 100% 70% 90% 0.286
Total* (all the above DSD cases) 153 94.1% 64.7% 77.1% <0.001
Tab.1  Accuracy of the DUCG diagnostic model for patients with DSDs and non-DSD-related diseases
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