Artificial intelligence in health care
  • 2019 № 4 Prediction of recurrence in patients with Cushing’s disease after successful endoscopic transnasal adenomectomy: neural network model and its software implementation

    Introduction. Due to the high frequency of recurrences in patients with Cushing’s disease after endoscopic transnasal adenomectomy (up to 55% in the 5 year period), it is important to develop a method for predicting recurrence of the disease based on a combination of factors. Мaterials and methods. The study included 219 patients who underwent endoscopic transnasal adenomectomy in 2007–2014.Over 3 years, remission persisted in 172 patients; relapse developed in 47 patients. The construction of artificial neural networks of various topologies was performed in the Statistica v. 13, and then software was developed for the best network.
    Results. A highly efficient neural network (3-layer perceptron) was constructed, which allows predicting recurrence within 3 years or remission for at least 3 years. The sensitivity of the model is 74%, the specificity 97%, the positive predictive value 85%, the negative predictive value 93%. The predictors of the model are sex, age, duration of the disease, MRI type of adenoma, levels of adrenocorticotropic hormone and cortisol in blood in early postoperative period. Web-calculator was developed and is available to doctors for free practical use on
    Сonclusion. The software implementing neural network is a quite effective tool for predicting recurrence and it will allow to perform personalized approach to management of patients who underwent neurosurgical treatment for the Cushing’s disease.

    Authors: Nadezhdina E. Y. [1] O. Yu. Rebrova [2] Antyukh M. S. [1] Grigoriev A. Y. [1]

    Tags: artificial neural network2 prediction2 recurrence1 software calculator1 web-based application1 сushing disease1

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