DOI: 10.1186/s13550-018-0393-5Pages: 1-10

Computer-aided diagnosis for (123I)FP-CIT imaging: impact on clinical reporting

1. Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Nuclear Medicine, I-floor

2. Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Radiology

3. University of Oxford, Oxford Parkinson’s Disease Centre

4. University of Sheffield, Department of Neuroscience, Sheffield Institute for Translational Neuroscience

5. Insigneo, Infection Immunity and Cardiovascular Disease, University of Sheffield, Royal Hallamshire Hospital

Correspondence to:
Jonathan Christopher Taylor
Tel: 0114 2712628
Email: Jonathan.taylor@sth.nhs.uk

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Abstract

Background

For (123I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters’ performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology.

Three experienced (123I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1). Once completed, the process was then repeated (read 2). Immediately after submitting each image score for a second time, the CADx system output was displayed to reporters alongside the image data. With this information available, the reporters submitted a score for the third time (read 3). Comparisons between reads 1 and 2 provided evidence of intra-operator reliability, and differences between reads 2 and 3 showed the impact of the CADx.

Results

The performance of all reporters demonstrated a degree of variability when analysing images through visual analysis alone. However, inclusion of CADx improved consistency between reporters, for both clinical and research data. The introduction of CADx increased the accuracy of the radiologists when reporting (unfamiliar) research images but had less impact on the clinical scientist and caused no significant change in accuracy for the clinical data.

Conclusions

The outcomes for this study indicate the value of CADx as a diagnostic aid in the clinic and encourage future development for more refined incorporation into clinical practice.

This article is freely available, click here to access the full text/PDF

  • Accepted: Apr 27, 2018
  • Online: May 8, 2018

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