Early Discrimination of Periprosthetic Hip Infections Using Neural Networks: a Pilot Study

Periprosthetic hip infections are an hot topic in orthopedic surgery, whose incidence is about 1%. The morbidity, mortality and additional costs associated with prolonged hospitalization and further treatments are the main concerns. Periprosthetic infections are generally diagnosed using the CDC (Center for Disease Control and Prevention) criteria (2014). The diagnosis is based on major and minor criteria, including pre-operative and intra-operative parameters. In order to achieve a reliable diagnosis of infection, when fistula are not present, synovial fluid aspiration or tissue samples are required. However, these techniques are expensive and invasive. Moreover, sensitivity is not always so accurate, as shown by some series of revision surgeries performed for presumed aseptic loosening, which turned out to be septic after cultures. Therefore, diagnosis of infection often occurs late and after a long, complex, expensive and not always decisive diagnostic workup, impacting on the timing and success of the treatment.

A practical, rapid, reliable and non-invasive (possibly outpatient) diagnostic procedure for periprosthetic infections would be desirable. It may rely on diagnostic imaging, limiting the collection of liquid or tissues to doubtful cases. Currently, CT and nuclear medicine imaging techniques are not routinely adopted in the diagnosis of infection, due to the modest reliability, costs and exposure to radiant agents.

Recently, neural networks have been introduced: they consist of many simple parallel processors, deeply connected, realizing a computational model. Neural networks mimic brain and its ability to learn. Computational models recognize of visual signals, manage complex situations in real time, classify and manage noise, use associative memory with real-time access to large amounts of data and reconstruct partial or corrupted information. Neural networks have been already used to predict the onset of infections, metastases and treatment failures, integrating clinical and diagnostic imaging data. To date, no studies about neural networks in periprosthetic infection have been conducted. The purpose of this study is to evaluate whether neural networks (cellular neural networks-genetic algorithm), applied to conventional radiographies, are accurate, sensitive and specific for the early-discrimination of a periprosthetic hip infection, already diagnosed with well-recognized methods (CDC 2014).

Specifically, a population of patients, with a complete radiographic history (pre-operative X-rays and a series of other post-operative X-rays), treated for septic or aseptic loosening, is selected.

Both cases are necessary to "instruct" a neural network. The first step consists in identifying a consecutive series of patients with septic or aseptic loosening diagnosis, consulting the hospital database. Thus, patients are categorically divided into septic, or aseptic, loosening. The 2014 CDC criteria are used (as routinely performed in the clinical setting), adding another major and necessary criterion: at least 3 positive intraoperative tissue samples (same micro-organism). In case of aseptic loosening, the case must not meet the CDC 2014 criteria. Thus, the imaging and clinical data of the patients are collected. Having ascertained the diagnosis, the radiographic material is processed (cellular neural networks-genetic algorithm). The proposed procedure processes the radiographic images using the following pipeline and the MatLab software (Mathworks, Natick, US):