Early Discrimination of Periprosthetic Hip Infections Using Neural Networks (SEPTIC-ANNR) (SEPTIC-ANNR)

November 6, 2023 updated by: Istituto Ortopedico Rizzoli

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

The study is about the role of cellular neural networks-genetic algorithm in the diagnosis of periprosthetic hip infections. A retrospective case series of septic and aseptic loosening of primary hip arthroplasties is selected. The diagnosis of septic loosening is made according to well-established criteria (CDC 2014 and culture samples). The serial radiographs of the selected patients are processed using cellular neural networks-genetic algorithm. 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).

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

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):

  • baseline: the first post-implant image is compared to the pre-implant radiographic image;
  • progresses are recorded by periodical radiographs using standard and repeatable projections (pelvis X-rays);
  • the features are extracted from each image, in the manually segmented area (region of interest - ROI). Three steps take place: 1) image pre-processing, to create uniform frameworks of input data (gray-level images). Color Histogram Equalization; 2) features extracted from neural networks are applied to ROI. Cloning templates using genetic algorithms. The features will be processed by a fully connected layer + SoftMax; 3) features extracted from AutoEconder with fully-connected layer + SoftMax;
  • analysis of differential radiographic features (analysis of cellular and convolutive sides) and comparison to the baseline. Post-processing in the following way: 1) fully-connected regression layer and multiclass classifier: will produce the percentage of septic progression risk; 2) fully-connected regression layer and binary classifier: features in the septic / aseptic clusters;
  • a final decision tree: fusion of the above-mentioned data, providing a percentage of septic progression risk at the indexed imaging. The aim is to verify whether the neural networks applied to radiographic imaging can accurately, sensitively and specifically recognize a late, chronic periprosthetic hip infection diagnosed according to validated and certain criteria.

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Italy
      • Bologna, Italy, 40136
        • Recruiting
        • IRCCS Istituto Ortopedico Rizzoli
        • Contact:
        • Contact:
        • Sub-Investigator:
          • Francesco Rundo, BS
        • Sub-Investigator:
          • Sabrina Conoci, BS

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

A consecutive series of adult patients treated for aseptic and septic loosening of primary hip implants at IRCCS Istituto Ortopedico Rizzoli.

Description

Inclusion criteria:

  • Revisions of primary total hip arthroplasty due to septic and aseptic loosening
  • In case of septic loosening, diagnosis of late chronic periprosthetic hip infection
  • Complete clinical data
  • Complete lab data (pre-revision erythrocyte sedimentation rate and C-reactive protein, at least 5 intraoperative tissue samples).
  • Complete radiographic assessment (pre-implant X-ray, a series of post-operative X-rays, pre-revision X-ray)

Exclusion criteria

  • Hip re-revisions
  • Incomplete or inadequate radiographic assessment
  • Inadequate data to diagnose infection according to 2014 CDC criteria and tissue samples

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Other
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
septic loosening
Septic loosening of primary hip implants according to the 2014 CDC criteria (as routinely performed in the clinical setting), adding another major and necessary criterion: at least 3 positive intraoperative tissue samples (same micro-organism).
Diagnostic test: cellular neural networks-genetic algorithm
Cellular neural networks-genetic algorithm applied to conventional radiographs of hip implants with a well-established diagnosis of loosening. The study is not intended to use a software without a CE mark as a medical device, or to use the software as a tool to diagnose or prevent human disease, according to Directive 93/42 / European Economic Community. The study will evaluate if the software, properly calibrated, is able to recognize with adequate accuracy infections already diagnosed with validated methods.
aseptic loosening
aseptic loosening of primary hip implants not meeting the CDC 2014 criteria
Diagnostic test: cellular neural networks-genetic algorithm
Cellular neural networks-genetic algorithm applied to conventional radiographs of hip implants with a well-established diagnosis of loosening. The study is not intended to use a software without a CE mark as a medical device, or to use the software as a tool to diagnose or prevent human disease, according to Directive 93/42 / European Economic Community. The study will evaluate if the software, properly calibrated, is able to recognize with adequate accuracy infections already diagnosed with validated methods.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy
Time Frame: 15 years

Definition: ability of the cellular neural network to discriminate between septic and aseptic loosening. Technique: the diagnostic accuracy will be measured as a receiver operating characteristic (ROC) curve, according to the maximum likelihood method (binomial approximation).

Metric: percentage. Minimum-maximum values: 0-100.
15 years
Sensitivity
Time Frame: 15 years

Definition: the probability of being septic in septic hips with ascertained CDC criteria.

Technique: true positive / (true positive + false negative). Metric: percentage. Minimum-maximum values: 0-100.
15 years
Specificity
Time Frame: 15 years
Definition: proportion of aseptic loosening in total of aseptic loosening ascertained using CDC criteria Technique: True negative / (true negative + false positive) Metric: percentage. Minimum-maximum values: 0-100.
15 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Istituto Ortopedico Rizzoli

Collaborators

Università degli studi di Messina

Investigators

  • Study Chair: Francesco Traina, PhD, IRCCS Istituto Ortopedico Rizzoli

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 3, 2019

Primary Completion (Estimated)

October 1, 2024

Study Completion (Estimated)

October 2, 2024

Study Registration Dates

First Submitted

October 7, 2019

First Submitted That Met QC Criteria

October 7, 2019

First Posted (Actual)

October 8, 2019

Study Record Updates

Last Update Posted (Actual)

November 7, 2023

Last Update Submitted That Met QC Criteria

November 6, 2023

Last Verified

November 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 438/2019/Oss/IOR

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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