Abstract
Kasai portoenterostomy (KP) represents the first-line treatment for biliary atresia (BA). The purpose was to compare the accuracy of quantitative parameters extracted from laboratory tests, US imaging, and MR imaging studies using machine learning (ML) algorithms to predict the long-term medical outcome in native liver survivor BA patients after KP. Twenty-four patients were evaluated according to clinical and laboratory data at initial evaluation (median follow-up = 9.7 years) after KP as having ideal (n = 15) or non-ideal (n = 9) medical outcomes. Patients were re-evaluated after an additional 4 years and classified in group 1 (n = 12) as stable and group 2 (n = 12) as non-stable in the disease course. Laboratory and quantitative imaging parameters were merged to test ML algorithms. Total and direct bilirubin (TB and DB), as laboratory parameters, and US stiffness, as an imaging parameter, were the only statistically significant parameters between the groups. The best algorithm in terms of accuracy, sensitivity, specificity, and AUCROC was naive Bayes algorithm, selecting only laboratory parameters (TB and DB). This preliminary ML analysis confirms the fundamental role of TB and DB values in predicting the long-term medical outcome for BA patients after KP, even though their values may be within the normal range. Physicians should be alert when TB and DB values change slightly.
Highlights
In the past decades, due to the growth of medical information digitalization and thanks to the availability of increasingly sophisticated technological quantitative tools, large volumes of patient data have become widely available
In biliary atresia (BA) patients surviving with native liver after Kasai portoenterostomy (KP), the evaluation of the disease course and biliary cirrhosis occurrence is clinically relevant during follow-up [18,23]
The accuracy of several diagnostic quantitative parameters extracted from different methodologies, such as laboratory tests and imaging exams (US and magnetic resonance imaging (MR)), using machine learning (ML) algorithms was compared to predict the long-term medical outcome for native liver survivor patients with BA who have undergone KP
Summary
Due to the growth of medical information digitalization and thanks to the availability of increasingly sophisticated technological quantitative tools, large volumes of patient data have become widely available In this scenario, new approaches from computational sciences can be used to analyze medical data to extract critical health information that can help clinicians in the decision-making process and prognostic evaluation [1]. Machine learning (ML) has gained great interest thanks to cheaper computing power and inexpensive memory and because it is agnostic to the domain of application. It is a methodology of data analysis, a branch of artificial intelligence, that enables systems to learn and improve from data [2].
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