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Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development

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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.

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Acknowledgements

This research was supported by the National Key Research and Development Program of China (No. 2016YFC0901501), CAMS Innovation Fund for Medical Science (No. CAMS-2017-I2M-1-011), and the Research Project of the Institute of Internet Industry, Tsinghua University, titled “DUCG theory and application of medical aided diagnosis-algorithm of introducing classification variables in DUCG.”

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Author notes

  1. These authors are co-first authors who contributed equally to this work.

Authors and Affiliations

  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

    Dongping Ning & Lin Lu

  2. Department of Pediatrics, Linfen Central Hospital, Linfen, 041000, China

    Dongping Ning

  3. Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, 100084, China

    Zhan Zhang & Kun Qiu

  4. Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China

    Qin Zhang

  5. Institute of Internet Industry, Tsinghua University, Beijing, 100084, China

    Yan Zhu

  6. Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China

    Renzhi Wang

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  1. Dongping Ning
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  2. Zhan Zhang
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  3. Kun Qiu
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  4. Lin Lu
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  5. Qin Zhang
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  6. Yan Zhu
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Corresponding authors

Correspondence to Lin Lu or Qin Zhang.

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Compliance with ethics guidelines

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, and Renzhi Wang declare that they have no potential conflicts of interest associated with this manuscript. All procedures were performed in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients included in the study.

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Ning, D., Zhang, Z., Qiu, K. et al. Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of sex development. Front. Med. 14, 498–505 (2020). https://doi.org/10.1007/s11684-020-0791-8

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  • DOI: https://doi.org/10.1007/s11684-020-0791-8

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